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Infrastructure digital twin technology: A new paradigm for future construction industry
基础设施数字孪生技术：未来建筑业的新范式
open access 开放获取
Highlights 亮点
- • ·Identify factors affecting digital twins' implications in construction industry.
确定影响数字孪生在建筑业中的影响的因素。 - • ·Evaluate and synthesise the evidence.
评估和综合证据。 - • ·Suggest a strategic plan for implementation of digital twins in construction industry.
为建筑业实施数字孪生提出战略计划。 - • ·Identify the digital twins and construction 4.0 theory and BIM application.
识别数字孪生和建筑4.0理论和BIM应用。 - • ·The article outlines the research philosophy and plan.
本文概述了研究思路和计划。
Abstract 摘要
The construction industry has traditionally been slow to adopt digital technology, resulting in inefficient workflows, frequent cost overruns, and delays. Moreover, its fragmented structure, inherent to market dynamics, exacerbates these challenges. Embracing digitalization and transitioning to Industry 4.0 can substantially enhance efficiency and productivity in construction through increased innovation and improved collaboration, ultimately reducing information gaps and data discrepancies. This study aims to assess the potential integration of digital twin technology across various construction stages, spanning from initial design to project delivery. Existing literature emphasizes the transformative power of digital twin technology in advancing building innovation and environmental sustainability. These virtual replicas are crucial in optimizing industrial manufacturing by harmonizing production processes and societal interactions. A focused examination of digital twin technology applications in construction highlights its ability to streamline coordination and facilitate data sharing among stakeholders. Property owners increasingly recognise the value of digital twin technology in local contexts, driving the digitization of design and collaboration methods in construction. Integrating digital twin technology right from a project's inception and extending it across design phases optimizes project delivery, enhances asset quality, and contributes to societal sustainability. As the nexus between digitalization and sustainability goals strengthens, the construction industry stands at the cusp of a significant transformative journey.
传统上，建筑行业采用数字技术的速度缓慢，导致工作流程效率低下，经常出现成本超支和延误。此外，市场动态所固有的分散结构加剧了这些挑战。拥抱数字化和向工业4.0过渡可以通过增加创新和改善协作来大幅提高建筑的效率和生产力，最终减少信息差距和数据差异。该研究旨在评估数字孪生技术在各个建设阶段的潜在集成，从初始设计到项目交付。现有文献强调了数字孪生技术在推进建筑创新和环境可持续性方面的变革力量。这些虚拟复制品对于通过协调生产流程和社会互动来优化工业制造至关重要。 对数字孪生技术在建筑业中的应用进行了重点研究，突出了其简化协调和促进利益相关者之间数据共享的能力。业主越来越认识到数字孪生技术在当地环境中的价值，推动了建筑设计和协作方法的数字化。从项目一开始就集成数字孪生技术，并将其扩展到整个设计阶段，可优化项目交付，提高资产质量，并有助于社会可持续发展。随着数字化与可持续发展目标之间的联系不断加强，建筑业正处于重大变革之旅的尖端。
传统上，建筑行业采用数字技术的速度缓慢，导致工作流程效率低下，经常出现成本超支和延误。此外，市场动态所固有的分散结构加剧了这些挑战。拥抱数字化和向工业4.0过渡可以通过增加创新和改善协作来大幅提高建筑的效率和生产力，最终减少信息差距和数据差异。该研究旨在评估数字孪生技术在各个建设阶段的潜在集成，从初始设计到项目交付。现有文献强调了数字孪生技术在推进建筑创新和环境可持续性方面的变革力量。这些虚拟复制品对于通过协调生产流程和社会互动来优化工业制造至关重要。 对数字孪生技术在建筑业中的应用进行了重点研究，突出了其简化协调和促进利益相关者之间数据共享的能力。业主越来越认识到数字孪生技术在当地环境中的价值，推动了建筑设计和协作方法的数字化。从项目一开始就集成数字孪生技术，并将其扩展到整个设计阶段，可优化项目交付，提高资产质量，并有助于社会可持续发展。随着数字化与可持续发展目标之间的联系不断加强，建筑业正处于重大变革之旅的尖端。
Keywords 关键词
Digital twin
Construction 4.0
Sustainability
Project delivery
Built environment
Construction industry
数字孪生
建筑4.0
可持续性
项目交付
建筑环境
建筑行业
1. Introduction 1.介绍
The rapid evolution of technology in recent years is exemplified by the emergence of Industry 4.0 and its corresponding initiatives across various sectors, including Smart City, Engineering, and Construction [1]. Examples within SCEC encompass Building 4.0 [2], Construction 4.0 [3,4], Real Estate 4.0 [5], Mining 4.0 [6], Education 4.0 [7], Mass personalization [8], and Manufacturing 4.0 [9]. Central to the Industry 4.0 concept is integrating physical environments with digital ecosystems. Despite these advancements, the building and construction industry grappled with numerous challenges, notably the pressing need for enhanced environmental sustainability and heightened operational efficiency and productivity. Information technology and digitalization have played pivotal roles in propelling the expansion of various industries in recent decades. However, the construction sector faces a unique set of obstacles that necessitate innovative solutions to leverage the potential benefits offered by Industry 4.0 fully. In this context, addressing environmental concerns and improving overall operational effectiveness become crucial imperatives for the continued growth and sustainability of the construction business [10]. It is a component of the most recent step in the Industrial Revolution, which builds on technical developments with a scale comparable to that of the mechanization, electrification, and automation revolutionary strides that came before it [11]. The building sector is characterized by a differently disjointed production process, which has brought about tardiness in embracing technology. Both of these characteristics contribute to the sector's overall lack of competitiveness [12]. According to Columbus [13], the sector is yet to adopt new digital technologies that require an initial investment, even though these technologies will result in considerable long-term advantages. Through digitalization and information technology, the integration of the building processes, disciplines, performers, and stakeholders can be improved throughout all the stages of project execution. According to Love & Matthews [14], changes in behaviours, procedures, expectations, relationships, and business strategy must be involved for technology to deliver value and advantages.
近年来技术的快速发展体现在工业4.0的出现及其在各个领域的相应举措，包括智慧城市，工程和建筑[1]。SCEC中的例子包括建筑4.0 [2]、建筑4.0 [3，4]、真实的房地产4.0 [5]、采矿4.0 [6]、教育4.0 [7]、大规模个性化[8]和制造4.0 [9]。工业4.0概念的核心是将物理环境与数字生态系统相结合。 尽管取得了这些进展，但建筑业仍面临着众多挑战，特别是迫切需要加强环境可持续性以及提高运营效率和生产力。近几十年来，信息技术和数字化在推动各行各业的扩张方面发挥了关键作用。然而，建筑行业面临着一系列独特的障碍，需要创新的解决方案来充分利用工业4.0提供的潜在优势。在这种情况下，解决环境问题和提高整体运营效率成为建筑业务持续增长和可持续性的关键。 它是工业革命最新一步的组成部分，工业革命建立在技术发展的基础上，其规模与之前的机械化，电气化和自动化革命性进步相当[11]。建筑部门的特点是生产过程的脱节，这导致了采用技术的缓慢。这两个特点都导致该部门总体上缺乏竞争力[12]。根据哥伦布[13]的说法，该行业尚未采用需要初始投资的新数字技术，尽管这些技术将带来相当大的长期优势。 通过数字化和信息技术，可以在项目执行的所有阶段改进建筑流程、学科、执行者和利益相关者的集成。根据Love&马修斯[14]的说法，行为、程序、期望、关系和业务战略的变化必须涉及技术提供价值和优势。
近年来技术的快速发展体现在工业4.0的出现及其在各个领域的相应举措，包括智慧城市，工程和建筑[1]。SCEC中的例子包括建筑4.0 [2]、建筑4.0 [3，4]、真实的房地产4.0 [5]、采矿4.0 [6]、教育4.0 [7]、大规模个性化[8]和制造4.0 [9]。工业4.0概念的核心是将物理环境与数字生态系统相结合。 尽管取得了这些进展，但建筑业仍面临着众多挑战，特别是迫切需要加强环境可持续性以及提高运营效率和生产力。近几十年来，信息技术和数字化在推动各行各业的扩张方面发挥了关键作用。然而，建筑行业面临着一系列独特的障碍，需要创新的解决方案来充分利用工业4.0提供的潜在优势。在这种情况下，解决环境问题和提高整体运营效率成为建筑业务持续增长和可持续性的关键。 它是工业革命最新一步的组成部分，工业革命建立在技术发展的基础上，其规模与之前的机械化，电气化和自动化革命性进步相当[11]。建筑部门的特点是生产过程的脱节，这导致了采用技术的缓慢。这两个特点都导致该部门总体上缺乏竞争力[12]。根据哥伦布[13]的说法，该行业尚未采用需要初始投资的新数字技术，尽管这些技术将带来相当大的长期优势。 通过数字化和信息技术，可以在项目执行的所有阶段改进建筑流程、学科、执行者和利益相关者的集成。根据Love&马修斯[14]的说法，行为、程序、期望、关系和业务战略的变化必须涉及技术提供价值和优势。
Gunderson [15] concluded that technology is a pivotal catalyst in the symbiotic relationship between organizations and society, particularly in shaping the natural environment. As such, technology must be examined within the framework of these intricate interactions [16,17]. Within the digital transformation landscape, Building Information Modelling (BIM) has emerged as an essential instrument for the construction industry. Contemporary research has primarily focused on diverse methodologies aimed at enhancing this technology. According to Boje et al. [18], the 3D model produced today is more than simply a model; it also includes specific information on a component level and digital tools that facilitate data mining and communication amongst many stakeholders. According to Lim et al. [19], the term "digital twin" refers to a technology that has already been widely used in the industrial industry for mathematical modelling, simulation, and optimization. In the context of smart manufacturing, it facilitates information flow from the internal to the external stakeholders [20].
Gunderson [15]总结说，技术是组织与社会之间共生关系的关键催化剂，特别是在塑造自然环境方面。因此，技术必须在这些复杂的相互作用的框架内进行审查[16，17]。在数字化转型的背景下，建筑信息建模（BIM）已成为建筑行业的重要工具。当代的研究主要集中在旨在增强这一技术的各种方法上。根据Boje et al. [18]，今天产生的3D模型不仅仅是一个模型;它还包括组件级别的特定信息和数字化工具，便于数据挖掘和许多利益相关者之间的沟通。根据Lim等人[19]的说法，术语“数字孪生”是指一种已经广泛用于工业领域的数学建模，仿真和优化的技术。在智能制造的背景下，它促进了从内部到外部利益相关者的信息流[20]。
Gunderson [15]总结说，技术是组织与社会之间共生关系的关键催化剂，特别是在塑造自然环境方面。因此，技术必须在这些复杂的相互作用的框架内进行审查[16，17]。在数字化转型的背景下，建筑信息建模（BIM）已成为建筑行业的重要工具。当代的研究主要集中在旨在增强这一技术的各种方法上。根据Boje et al. [18]，今天产生的3D模型不仅仅是一个模型;它还包括组件级别的特定信息和数字化工具，便于数据挖掘和许多利益相关者之间的沟通。根据Lim等人[19]的说法，术语“数字孪生”是指一种已经广泛用于工业领域的数学建模，仿真和优化的技术。在智能制造的背景下，它促进了从内部到外部利益相关者的信息流[20]。
The concept of digital twin technology– integrating components, machinery, and systems within a given infrastructure – has garnered increasing attention in global urban planning dialogues [21]. The growing consensus is that applying digital twin technology to urban and national landscapes could revolutionize the construction sector. This entails real-time data analysis and the virtual testing of complex systems before their physical implementation [22]. The primary objective of this research is to rigorously examine the efficacy and reliability of digital twin technology as a vehicle for enhancing productivity, optimizing operational effectiveness, and promoting sustainability in construction processes and built environments. This investigation is motivated by the fundamental problems confronting the construction industry to improve and integrate digitalization [12]. There is now a hole in the study of digital twin technology for the entirety of the manufacturing process involved in building. This research intends to bridge the gap between analysing digital twin technology for urban planning, construction sites, and site logistics and researching digital twin technology for the built environment, intelligent buildings, and smart cities.
数字孪生技术的概念--在给定的基础设施中集成组件、机械和系统--在全球城市规划对话中获得了越来越多的关注。越来越多的共识是，将数字孪生技术应用于城市和国家景观可以彻底改变建筑行业。这需要在物理实现之前对复杂系统进行实时数据分析和虚拟测试[22]。 这项研究的主要目标是严格审查数字孪生技术作为提高生产力、优化运营效率和促进建筑过程和建筑环境可持续性的工具的有效性和可靠性。这项调查的动机是建筑业面临的基本问题，以改善和整合数字化[12]。现在，数字孪生技术在建筑所涉及的整个制造过程中的研究有一个漏洞。本研究旨在弥合分析城市规划、建筑工地和现场物流的数字孪生技术与研究建筑环境、智能建筑和智慧城市的数字孪生技术之间的差距。
数字孪生技术的概念--在给定的基础设施中集成组件、机械和系统--在全球城市规划对话中获得了越来越多的关注。越来越多的共识是，将数字孪生技术应用于城市和国家景观可以彻底改变建筑行业。这需要在物理实现之前对复杂系统进行实时数据分析和虚拟测试[22]。 这项研究的主要目标是严格审查数字孪生技术作为提高生产力、优化运营效率和促进建筑过程和建筑环境可持续性的工具的有效性和可靠性。这项调查的动机是建筑业面临的基本问题，以改善和整合数字化[12]。现在，数字孪生技术在建筑所涉及的整个制造过程中的研究有一个漏洞。本研究旨在弥合分析城市规划、建筑工地和现场物流的数字孪生技术与研究建筑环境、智能建筑和智慧城市的数字孪生技术之间的差距。
2. Methodology 2.方法
This study adheres to rigorous systematic review standards, as Saieg et al. [23] outlined. The systematic review methodology enables the research team to identify, curate, and evaluate all extant literature relevant to a designated quality threshold, a process underscored by Booth & Papaioannou, (2012). One salient advantage of employing this procedural framework is its capacity to impose a coherent and standardized investigative protocol on the subject area of focus, as S. Li et al. [24] emphasized. Consequently, this approach affords the deliverance of findings transparently devoid of subjective bias, a claim substantiated by Jesson et al. [25]. There are some problems with the technique. Because journals tend to publish publications with findings that have a substantial influence, valuable studies with outcomes that are not significant, as well as articles written in a language other than English, will be overlooked [24]. The outline of this thorough systematic review may be seen in Fig. 1.
正如Saieg et al. [23]所述，本研究遵循严格的系统评价标准。系统性综述方法使研究团队能够识别，策划和评估与指定质量阈值相关的所有现存文献，Booth Papaioannou强调了这一过程（2012）。采用这一程序框架的一个突出优点是，它能够对重点领域的主题实施连贯和标准化的调查协议。李等人[24]强调。因此，这种方法提供了透明的结果，没有主观偏见，这一主张得到了Rehon等人的证实。[25]。技术上有一些问题。 由于期刊倾向于发表具有重大影响力的研究结果的出版物，因此结果不显著的有价值的研究以及以英语以外的语言撰写的文章将被忽略[24]。这一全面系统性综述的概要见图1。
正如Saieg et al. [23]所述，本研究遵循严格的系统评价标准。系统性综述方法使研究团队能够识别，策划和评估与指定质量阈值相关的所有现存文献，Booth Papaioannou强调了这一过程（2012）。采用这一程序框架的一个突出优点是，它能够对重点领域的主题实施连贯和标准化的调查协议。李等人[24]强调。因此，这种方法提供了透明的结果，没有主观偏见，这一主张得到了Rehon等人的证实。[25]。技术上有一些问题。 由于期刊倾向于发表具有重大影响力的研究结果的出版物，因此结果不显著的有价值的研究以及以英语以外的语言撰写的文章将被忽略[24]。这一全面系统性综述的概要见图1。
Fig. 1. Methodology framework.
Fig. 1.方法框架。
In accordance with the guidelines for conducting a systematic review [23,24], this review: (1) utilizes a systematic method to analyze a certain quality and number of studies on the topic of digital twin technology and construction 4.0; (2) provides readers with an objective, transparent, and standardized technical roadmap, which includes information on the selection of databases, study retrieval, and the selection criteria of the target studies; (3) is both replicable and updatable; The primary components of this methodical strategy are as follows: (1) the framing of a question; (2) the collecting of relevant studies; (3) the selection and assessment of relevant studies; (4) the content analysis of relevant studies; and (5) the reporting of the results and conclusions [24].
根据系统评价指南[23，24]，本综述：（1）利用系统的方法分析一定质量和数量的关于数字孪生技术和构建4.0主题的研究;（2）为读者提供客观、透明和标准化的技术路线图，包括数据库选择、研究检索和目标研究选择标准等信息;（3）可复制和可更新;这一系统策略的主要组成部分如下：（1）问题的框架;（2）收集相关研究;（3）选择和评估相关研究;（4）相关研究的内容分析;（5）报告结果和结论[24]。
根据系统评价指南[23，24]，本综述：（1）利用系统的方法分析一定质量和数量的关于数字孪生技术和构建4.0主题的研究;（2）为读者提供客观、透明和标准化的技术路线图，包括数据库选择、研究检索和目标研究选择标准等信息;（3）可复制和可更新;这一系统策略的主要组成部分如下：（1）问题的框架;（2）收集相关研究;（3）选择和评估相关研究;（4）相关研究的内容分析;（5）报告结果和结论[24]。
2.1. Search strategy and the selection of studies
2.1.检索策略和研究选择
2.1.1. Source database selection
2.1.1.源数据库选择
The researcher searched for relevant literature using Scopus databases because some excellent research representing the condition of digital twin technology and construction 4.0 in New Zealand has been published in international publications. Thus, our research endeavours seek to expound the gaps in digital twin technology. We are particularly interested in delineating their contributions and potential implications. As a result, the datasets by S. Li et al. [24] were identified as particularly salient to our inquiry due to their comprehensive scope and academic gravitas. The decision to employ Scopus as our primary resource was not arbitrary. It stands unrivalled in terms of abstract and citation collections, boasting coverage of a staggering 15,000 journals from 4000 distinct publishers – some of which are academic stalwarts such as Elsevier, Emerald, Taylor and Francis, the Institution of Electrical Engineers, John Wiley, Springer, Nature, and the American Chemical Society. Our curated literature spans work published from 2018 through July 2023, offering a corpus of information that is both expansive in volume and exemplary in academic rigour, thus ensuring a holistic review of digital twin technology implementations within the construction industry.
研究人员使用Scopus数据库检索相关文献，因为一些代表新西兰数字孪生技术和建设4.0状况的优秀研究已在国际出版物上发表。因此，我们的研究工作旨在阐述数字孪生技术的差距。我们特别感兴趣的是描绘他们的贡献和潜在的影响。因此，S.李等人[24]被认为是我们调查中特别突出的，因为他们的全面范围和学术庄严。决定使用Scopus作为我们的主要资源并不是武断的。 它在摘要和引文收集方面无与伦比，拥有来自4000家不同出版商的惊人的15，000种期刊-其中一些是学术中坚力量，如Elsevier，Emerald，Taylor和弗朗西斯，电气工程师学会，John Wiley，Springer，Nature和美国化学学会。我们策划的文献涵盖了从2018年到2023年7月出版的作品，提供了一个信息语料库，该语料库在数量上是广泛的，在学术严谨性上是典范，从而确保了对建筑行业数字孪生技术实施的全面审查。
研究人员使用Scopus数据库检索相关文献，因为一些代表新西兰数字孪生技术和建设4.0状况的优秀研究已在国际出版物上发表。因此，我们的研究工作旨在阐述数字孪生技术的差距。我们特别感兴趣的是描绘他们的贡献和潜在的影响。因此，S.李等人[24]被认为是我们调查中特别突出的，因为他们的全面范围和学术庄严。决定使用Scopus作为我们的主要资源并不是武断的。 它在摘要和引文收集方面无与伦比，拥有来自4000家不同出版商的惊人的15，000种期刊-其中一些是学术中坚力量，如Elsevier，Emerald，Taylor和弗朗西斯，电气工程师学会，John Wiley，Springer，Nature和美国化学学会。我们策划的文献涵盖了从2018年到2023年7月出版的作品，提供了一个信息语料库，该语料库在数量上是广泛的，在学术严谨性上是典范，从而确保了对建筑行业数字孪生技术实施的全面审查。
2.1.2. Paper retrieval 2.1.2.论文检索
A search of the databases was performed every month, beginning in January 2023 and continuing through July 2023, using the same search technique. The terms "title," "abstract," and "keywords" were used as the criterion for Scopus with keyboard "Digital Twin" AND "Construction industry" OR "BIM".
从2023年1月开始至2023年7月，使用相同的检索技术每月对数据库进行检索。术语“标题”、“摘要”和“关键词”被用作Scopus的标准，键盘为“数字孪生”和“建筑业”或“BIM”。
从2023年1月开始至2023年7月，使用相同的检索技术每月对数据库进行检索。术语“标题”、“摘要”和“关键词”被用作Scopus的标准，键盘为“数字孪生”和“建筑业”或“BIM”。
In any scholarly investigation, it was imperative to comprehensively explore extant literature to capture the broadest scope of relevant studies. Initially, our data retrieval phase involved an exhaustive collection from three distinct databases, specifically targeting studies that elucidated the implementation of digital twin technology within the construction industry. Subsequently, we judiciously applied select filters to distil the most pertinent results. This study underscored an apparent gap in our refined retrieval process: a conspicuous absence of research emphasizing lean construction within the New Zealand context, underscoring an avenue ripe for further scholarly inquiry. Two independent researchers meticulously appraised the remaining articles after the dual-phase data retrieval to ensure rigour and mitigate bias. This entailed a granular analysis of the titles, abstracts, keywords, and the main content of the papers.
在任何学术调查中，必须全面探索现存文献，以获取最广泛的相关研究。最初，我们的数据检索阶段涉及从三个不同的数据库中进行详尽的收集，特别是针对阐明数字孪生技术在建筑行业中实施的研究。随后，我们明智地应用选择过滤器来过滤最相关的结果。这项研究强调了一个明显的差距，在我们完善的检索过程中：一个明显的缺乏研究，强调精益建设在新西兰的背景下，强调一个途径成熟，进一步的学术研究。两名独立研究人员在双阶段数据检索后仔细评估了剩余文章，以确保严谨性并减轻偏倚。这需要对论文的标题、摘要、关键词和主要内容进行粒度分析。
在任何学术调查中，必须全面探索现存文献，以获取最广泛的相关研究。最初，我们的数据检索阶段涉及从三个不同的数据库中进行详尽的收集，特别是针对阐明数字孪生技术在建筑行业中实施的研究。随后，我们明智地应用选择过滤器来过滤最相关的结果。这项研究强调了一个明显的差距，在我们完善的检索过程中：一个明显的缺乏研究，强调精益建设在新西兰的背景下，强调一个途径成熟，进一步的学术研究。两名独立研究人员在双阶段数据检索后仔细评估了剩余文章，以确保严谨性并减轻偏倚。这需要对论文的标题、摘要、关键词和主要内容进行粒度分析。
In order to narrow down the number of publications considered, we employed both inclusion and exclusion criteria [24]. The requirements were as follows: (1) any duplicated literature should be deleted; (2) any literature that was unrelated to the subject matter of implementing digital twin technology in the construction industry should be deleted; and (3) any literature that mentioned the idea of a digital twin technology but did not investigate it in depth should be deleted. The evaluation findings were broken down into three groups following these criteria: pertinent, reasonably pertinent, and not pertinent. Two researchers first discussed the literature that was classified as pertinent and not pertinent with inconsistent evaluation results and the literature that was classified as moderately relevant. They then attempted to agree on what should be included and excluded from the study. Concerning the literature on which they could not reach a consensus, the members of the lean construction team debated it and, in the end, came to an understanding. A presentation of the first search in the Scopus database is displayed in Table 1.
为了缩小所考虑的出版物数量，我们采用了纳入和排除标准[24]。要求如下：（1）应删除任何重复的文献;（2）应删除任何与在建筑行业实施数字孪生技术的主题无关的文献;（3）应删除任何提及数字孪生技术概念但未对其进行深入研究的文献。评价结果按照这些标准分为三组：相关、合理相关和不相关。两位研究者首先讨论了被归类为相关和不相关且评价结果不一致的文献以及被归类为中度相关的文献。然后，他们试图就研究中应该包括和排除的内容达成一致。 关于他们无法达成共识的文献，精益建设团队的成员对此进行了辩论，最终达成了谅解。Scopus数据库中首次检索的介绍见表1。
为了缩小所考虑的出版物数量，我们采用了纳入和排除标准[24]。要求如下：（1）应删除任何重复的文献;（2）应删除任何与在建筑行业实施数字孪生技术的主题无关的文献;（3）应删除任何提及数字孪生技术概念但未对其进行深入研究的文献。评价结果按照这些标准分为三组：相关、合理相关和不相关。两位研究者首先讨论了被归类为相关和不相关且评价结果不一致的文献以及被归类为中度相关的文献。然后，他们试图就研究中应该包括和排除的内容达成一致。 关于他们无法达成共识的文献，精益建设团队的成员对此进行了辩论，最终达成了谅解。Scopus数据库中首次检索的介绍见表1。
Table 1. Initial search results and the number of papers that appeared for Insight.
表1. Insight的初始搜索结果和出现的论文数量。
| Search Engines and Database 搜索引擎和数据库 | Keywords 关键词 | Results (no. of articles) 结果（文章数量） | Limit to 限于 | Document Type 文档类型 |
|---|---|---|---|---|
| Initial search result 初始检索结果 | ||||
| Scopus | "Digital Twin" AND "Construction industry" OR "BIM" “数字孪生”与“建筑业”或“BIM” | 244 | Title, abstract, and the keywords 标题、摘要和关键词 | Conference papers, books, book chapters, and articles 会议论文、书籍、书籍章节和文章 |
| Deleting duplicates 删除重复项 | ||||
| Scopus | 130 | Title, abstract, and the keywords 标题、摘要和关键词 | Articles 文章 | |
A list of articles, book chapters, and conference papers is the result of the first search. Subsequently, the items mentioned above were deleted except for the articles. The search was, therefore, limited to only article. Thus, 244 publications were kept as articles following the initial purifying procedure, as seen in Fig. 2.
文章、书籍章节和会议论文的列表是第一次搜索的结果。随后，除条款外，删除了上述项目。因此，检索仅限于文章。因此，244篇出版物在初始纯化程序后作为文章保留，如图2所示。
文章、书籍章节和会议论文的列表是第一次搜索的结果。随后，除条款外，删除了上述项目。因此，检索仅限于文章。因此，244篇出版物在初始纯化程序后作为文章保留，如图2所示。
Fig. 2. Publications by Year.
图二.按年份出版。
2.2. Content analysis 2.2.内容分析
The study team studied the topic clusters to examine the articles' contributions to the growth and feasible future content trends in New Zealand. The team discovered, via reading the 130 chosen publications, that the study encompassed a wide variety of subjects, including the following: Some examine the application of digital twin technology to construction 4.0, supply chain management, and other study fields, while others present the concepts, principles, technologies, and methodologies associated with lean construction [24,26]. While others examine the efficacy of digital twins, some discuss the elements that influence the application of digital twin technology in the construction industry [12,27]. Reading the titles, abstracts, and keyword lists of the 130 chosen articles led to their encoding. The complete text had to be read and encoded if the necessary information could not be gleaned from the article's title, abstract, or keywords. These articles were managed using Microsoft Word [28,29]. Articles related to the same subtopic were compiled onto a single page, and the articles' subtopics were colour-coded according to the category to which they belonged. Following the in-depth analysis of all 130 articles by hand, we determined four distinct subject matter groups. The coding mostly concentrated on the fundamental aspects of the literature's substance and information. (1) the paper's title, (2) the author's name, (3) the publication date, and (4) the title of the journal the article was published in were the fundamental pieces of information that were encoded. The subjects that were discussed in the research literature were classified into the following four groups: (1) digital twin technology theory and application; (2) implementing digital twin technology in construction industry research domains; (3) digital twin technology influence factors; and (4) the effect assessment of digital twin. Each subject cluster included several subtopics. Verifying that the coding and classification are consistent is essential to guarantee the accuracy of the content analysis [[29], [30]]. As a result, in this study, a scholar with substantial research expertise in implementing digital twin technology in the construction industry led a training session that included a systematic training component and coding activities for two coders.
研究小组研究了主题组，以检查文章对新西兰增长的贡献和未来可行的内容趋势。该团队通过阅读130份选定的出版物发现，该研究涵盖了各种各样的主题，包括以下内容：一些研究了数字孪生技术在建筑4.0，供应链管理和其他研究领域的应用，而另一些则提出了与精益建筑相关的概念，原则，技术和方法[24，26]。虽然其他人研究了数字孪生的功效，但有些人讨论了影响数字孪生技术在建筑行业应用的因素[12，27]。 通过阅读130篇文章的标题、摘要和关键词列表，得出了它们的编码。如果不能从文章的标题、摘要或关键词中收集到必要的信息，就必须阅读全文并进行编码。这些文章使用Microsoft Word管理[28，29]。与同一副主题相关的文章被汇编到一个页面上，文章的副主题根据它们所属的类别用颜色编码。在对所有130篇文章进行深入分析之后，我们确定了四个不同的主题组。编码主要集中在文献的内容和信息的基本方面。(1)论文标题，（2）作者姓名，（3）发表日期，（4）发表论文的期刊名称是编码的基本信息。 研究文献中讨论的主题分为以下四组：（1）数字孪生技术理论和应用;（2）数字孪生技术在建筑业研究领域的实施;（3）数字孪生技术的影响因素;（4）数字孪生的效果评估。每个主题组包括几个副主题。编码和分类的一致性对于保证内容分析的准确性至关重要[[29]，[30]]。因此，在这项研究中，一位在建筑行业实施数字孪生技术方面具有丰富研究专长的学者领导了一次培训课程，其中包括两名编码员的系统培训和编码活动。
研究小组研究了主题组，以检查文章对新西兰增长的贡献和未来可行的内容趋势。该团队通过阅读130份选定的出版物发现，该研究涵盖了各种各样的主题，包括以下内容：一些研究了数字孪生技术在建筑4.0，供应链管理和其他研究领域的应用，而另一些则提出了与精益建筑相关的概念，原则，技术和方法[24，26]。虽然其他人研究了数字孪生的功效，但有些人讨论了影响数字孪生技术在建筑行业应用的因素[12，27]。 通过阅读130篇文章的标题、摘要和关键词列表，得出了它们的编码。如果不能从文章的标题、摘要或关键词中收集到必要的信息，就必须阅读全文并进行编码。这些文章使用Microsoft Word管理[28，29]。与同一副主题相关的文章被汇编到一个页面上，文章的副主题根据它们所属的类别用颜色编码。在对所有130篇文章进行深入分析之后，我们确定了四个不同的主题组。编码主要集中在文献的内容和信息的基本方面。(1)论文标题，（2）作者姓名，（3）发表日期，（4）发表论文的期刊名称是编码的基本信息。 研究文献中讨论的主题分为以下四组：（1）数字孪生技术理论和应用;（2）数字孪生技术在建筑业研究领域的实施;（3）数字孪生技术的影响因素;（4）数字孪生的效果评估。每个主题组包括几个副主题。编码和分类的一致性对于保证内容分析的准确性至关重要[[29]，[30]]。因此，在这项研究中，一位在建筑行业实施数字孪生技术方面具有丰富研究专长的学者领导了一次培训课程，其中包括两名编码员的系统培训和编码活动。
3. Findings
3.1. Industry 4.0 and construction 4.0
According to Popkova & Zmiyak [31], the initial phases of the fourth industrial revolution, often known as Industry 4.0, are now being played out across a variety of business sectors throughout the world. According to Khan et al. [32], the fourth industrial revolution, also known as Industry 4.0, is characterized by its heavy reliance on digitalization and its utilization of big data and process automation. This contrasts the previous three industrial revolutions, which also introduced new technology. According to Popkova & Zmiyak [31], the fourth industrial revolution, also known as Industry 4.0, is distinct from other revolutions in several unheard-of aspects, such as eliminating human involvement and manual decision-making from the processes involved. According to Khan et al. [32], Popkova & Zmiyak [31], and Bonnet & Westerman [33], the automation of processes is essential to the success of Industry 4.0. According to Qi et al. [34], the evolution of digitalization has moved through the phases of digital enablement and digital support, and it is now positioned to enter the stage of digital control, connecting and combining the physical and digital worlds. Khan et al. [32] have outlined six design ideas that are the foundation for Industry 4.0. (1) Interoperability, also known as "widened communication across technology and humans," is accomplished by utilizing newly developed technologies such as Cyber-Physical Systems (CPS) and the Internet of Things (IoT). (2) Virtualization is the visualization and simulation of real-world activities within a digital environment. (3) Decentralization refers to removing choices that must be made by a centralized command and replacing them with CPS systems that can make decisions autonomously. (4) Real-time capability refers to analyzing and locating problems in systems and processes using data acquired in real-time. (5). Service orientation rfacilitatesdecision-making for managers, operators, and consumers by utilizing services connected to CPS. (6). Modularity refers to the capability of quickly adding additional processes, machines, and modules [32].
A framework in support of Construction 4.0 was presented by Sawhney et al. [35]. It uses the design ideas of Industry 4.0 and applies them to the construction industry. The authors provided the following definition of Construction 4.0: Construction 4.0 is a paradigm that uses cyber-physical systems, and the Internet of Things, Data, and Services to link the digital layer consisting of BIM and Common Data Environments (CDE) and the physical layer consisting of the asset over its whole life to create an interconnected environment integrating organizations, processes, and information to design, construct, and operate assets efficiently [35].
An in-depth knowledge of the technologies facilitating this change is essential to understand the numerous layers that comprise Construction 4.0 [36]. This article concentrates on nine Construction 4.0 technologies that have been regularly mentioned in the current research corpus, whilst other articles in the corpus explore a variety of Construction 4.0 technologies [37]. An overview of each of these technologies is provided in the following paragraphs: Augmented reality (AR) is both an information aggregator and a data publishing platform that enables the user to view displayed information passively. Virtual reality (VR) involves the creation of an immersive virtual experience in which consumers use headsets with 360-degree visualization, thus providing the consumer with a distinct expertise [38,39].
Integrated Building Information Modelling (iBIM) is considered to be at a higher level than traditional Building Information Modelling (BIM). Robotics makes use of robots that are capable of doing or imitating human behaviours. Creating a sophisticated, actual 3D item from a computer-aided design (CAD) model is the process referred to as 3D printing or additive manufacturing [40]. The phrase "artificial intelligence" (AI) refers to a computer program's ability to simulate human cognitive performance [36]. Unmanned aerial vehicles (UAVs), more commonly referred to as drones, are unmanned, small-scale aircraft that may be controlled remotely.
The Internet of Things (IoT) is a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols [36]. The term Internet of Things, commonly abbreviated as IoT, derives its name from the fusion of two key words: Internet and Things. The Internet is a vast, global system comprising interconnected computer networks that employ the standard Internet protocol suite (TCP/IP) to cater to billions of users across the globe [41]. It serves as a network of networks encompassing millions of private, public, academic, business, and government networks of varying scales, all linked through diverse electronic, wireless, and optical networking technologies. The concept of things encompasses a broad spectrum of distinguishable objects or entities in the real world. These can range from everyday items like electronic devices and technologically advanced products to more unconventional entities that may not be immediately perceived as electronic, such as food, clothing, furniture, materials, parts, equipment, merchandise, specialized items, landmarks, monuments, works of art, and the myriad elements of commerce, culture, and sophistication that surround us [42]. According to Bilal et al. [43], one definition of Big Data is the ability to process large amounts of data and derive useful insights from data. Big data refers to extensive datasets characterized by their substantial size and diverse and intricate structures, presenting storage, analysis, and visualization challenges for subsequent processes or outcomes [44]. A lifetime perspective on the use of Construction 4.0 technology is considered at the first level of the integration efforts for Construction 4.0. After its preliminary planning stage, a project proceeded to the design, construction, and facility management stages. According to Eadie et al. [45], using a technology's full potential occurs when the technology is incorporated at each appropriate stage of the lifetime of a building project. According to Teisserenc & Sepasgozar [17], the second phase of the integration efforts requires enhanced communication and interaction among the Construction 4.0 technologies.
According to Aleksandrova et al. [46], the comprehensive incorporation of digital technology represents a significant paradigm shift in the building process that establishes a unified digital ecosystem. The transition to Construction 4.0 necessitates a change in both the way of thinking and the techniques used. The growth and tailoring of digital technologies to the building industry's requirements is highly dynamic, costly, and highly customizable [36]. In addition, implementing new technologies requires employees and workers to be given additional training to adapt to their new roles. Because of this, the only way for construction businesses to earn an excess value is if the new technologies are incorporated into the company's operations and can be employed on various projects [47]. To successfully transition to Construction 4.0, one must adopt a process-oriented perspective rather than the more conventional project-oriented approach [48]. This shift in mentality, on the other hand, compels construction organizations to digitize the procedures they already use, which presents an extra barrier for two reasons: 1) The current procedure was created, for the most part, long before the currently accessible digital technologies were even conceived of, and 2) Not every procedure can be immediately digitized. As a result, all of the processes now in place need to be re-engineered to suit the shift in attitude and support the transformation to 4.0 that construction organizations are through. Sawhney et al. [35] also present a complete framework for Construction 4.0 based on the ideas underpinning Industry 4.0. The digital layer acts as the interface for linking multiple physical layers through digital tools. This establishes a robust connection between the physical world and the digital one.
The writers emphasize the importance of Building Information Modelling (BIM) and Common Data Environments (CDE) in developing a DT [49]. While CDE provides a constant stream of data and data management, Building Information Modelling (BIM) permits a model-centric approach and visible 3D connectivity [[50], [51], [52]]. These digital layers not only help the design and building of assets, but they also facilitate downstream activities over the entirety of an asset's life cycle, all the way up until the asset is demolished. This is true even after they have supported the design and construction of an investment [1].
3.2. Role of technology in construction
In order to conduct an efficient investigation into the management of companies, it is necessary to consider the part that technology plays and critically reflect on that part [15,16]. This function of technology may shift in accordance with various theoretical frameworks of technology. Orlikowski & Scott, (2008) stated that the constructive intertwining of organisational activity is usually done in some specific situations. As a result, it is impossible to comprehend technology apart from its context, meaning, and consequence. Concepts ought to be fluid and often change to keep pace with both the progression of technology and the application sector [53,54]. This will ensure that no intentionality or qualities are ascribed to technical creations. Since how work in organizations is connected to technology has not yet been conceptualized, a more sophisticated theoretical lens will need to be developed over time. In order to grasp the integrated, multifaceted, and ever-evolving function of technology, it is likely necessary to draw from various technological views and conceptual frameworks.
Gunderson [15] posits that as technology becomes more embedded in society, its overt presence may diminish over time. This subtly hints at the complex interplay between technology and its social implications. Similarly, Lee et al. [55] argue that theoretical frameworks examining the impact and dynamics of technology offer valuable lenses for inquiry, even when the technological element is not explicitly tied to organizational functions. Within this paradigm, technology transcends its material form to embody the contexts of its utilization and intended objectives. Consequently, the concept of digital twin technology should not be isolated to its technological architecture but should be understood in terms of its interactive potential with organizational processes, as well as its relevance to existing or forthcoming construction methodologies. According to Orlikowski & Scott [56], the influence and interaction of technology are frequently the driving forces for the concentration on technology in organizational research. According to Gunderson [15], technology influences not just the interaction of organizations with one another but also the interaction of societies with the built and natural environments in which they live. While digital twin technologies are increasingly integrated into various situational practices, their adoption as a critical element within organizational activities in the construction sector remains less than ubiquitous.
Peine [57] claims that a technological paradigm can describe certain aspects of technological development in response to certain situations, but it cannot explain the innovation process as a whole. It is a technological paradigm is described by Peine(2008) as a dominating design, mutual obligation, and mentality. This definition is derived from scientific paradigms, which are expected answers to a problem that all parties generally accept. The technological paradigm can largely depict the cumulative technical progress within an industry if the dominant design has been properly established throughout that industry. Nonetheless, achieving coordination about a common commitment and mentality is more challenging. Cantwell & Hayashi [58] characterize the technological paradigm in a manner analogous to that of Peine [57]. They define it as the commonalities found in a cluster of discoveries and efforts to innovate that occur throughout time and within an age in which scientific principles and organizational practices are comparable. The best description of social evolution and the innovation process is exemplified in the paradigm changes and the integration of major aspects of technology and innovative types of information, institutions, and production factors. According to Cantwell & Hayashi [58], these technological, socioeconomic, and political paradigms may be implemented in the manufacturing sector, technological domains, or in society.
3.3. Digitalization level in the construction industry
There is an obvious distinction in the construction sector at the moment between businesses that have transitioned to digital processes and technologies. Examples are Building Information Modelling (BIM), Tekla, and other CAD-related applications, as well as others that continue to rely on more conventional strategies. According to Ayinla & Adamu [59], this "digital divide" may be ascribed to several different constraints and limits that organizations confront while attempting to implement digitalization. Ayinla & Adamu [59] investigated the notion that financial considerations play a significant part in the process by which organizations adopt digitalization. This process may be prompted by demands or requirements imposed from the outside by customers, project owners, or the government. According to Bosch-Sijtsema et al. [60], governments have become increasingly mandating the use of BIM, particularly for more significant contractors, and as a format of delivery, which has led to an increase in the amount of digitalization in projects [53].
On the other hand, transitioning to adopting BIM has been more gradual for SMEs. According to this supposed value, BIM is the primary factor in determining whether or not it is implemented [60]. Actual users find great value in the technology and advocate for its usage, but non-users do not see the value to the same degree. In addition, implementing BIM affects small and medium-sized enterprises (SMEs), which do not have the means to invest in BIM technology to the same degree that bigger actors may, despite the development of rules that enhance the necessity of BIM. According to Dainty et al. [61], the expenses of training and software are two of these obstacles. According to Ayinla & Adamu [59], organizations' ability, particularly those established upon "innovation thinking" as a driving force, is a critical component in determining the impact of these elements. According to Dainty et al. [61], the digital divide is particularly common within bigger organizations, where certain workers are influenced by the barrier of BIM's motivational and skill requirements.
According to Davies & Harty [62], an individual's readiness to embrace BIM is connected to their perceived opinion of whether or not they believe BIM may benefit them in their line of work. Employees will decide whether or not to embrace BIM based on their newly acquired knowledge of what defines performance in their present function and how the implementation of BIM will either improve or degrade that performance [63,64]. Even among those adopting digital tools, there is still a variance in the extent of actual digitalization, with some organizations believing that simply switching from physical to digital documents is sufficient to meet their requirements for digitalization. However, the digital transformation of the industry entails a far more complicated set of circumstances, and for organizations to attain saturation, there is a requirement for both internal and external pressure to adapt for them to evolve [65,66]. According to Bosch-Rekveldt et al. [67], the construction industry is distinctive in several respects. For example, it has a project-based structure, in which individual construction projects function virtually identically to independent businesses, and the completion of huge construction projects can take decades. In an organization structured from the top down, project leaders fulfil a function analogous to that of the CEO. The construction business is considered to have a vertical structure, in contrast to the horizontal structures found in industries such as manufacturing, which results in restricted knowledge and data transfer across projects [54,68].
3.4. The idea of digital twins
The idea of DTs is very recent and continuously being developed further. However, the idea has been utilized in different sectors since the 1960s, with its genesis in NASA's space missions [69]. Dr. Michael Grieves is credited with coining the DT model phrase in 2002. However, the concept can be said to have been applied in numerous industries since the 1960s. One way to explain DTs is to describe it as the combination of a physical object, its digital rend, and how both are connected despite the conventional definition of DTs. A real DT is accomplished when a bidirectional link exists between the two, giving an accurate and valid representation of the physical asset [70]. The connectivity between the physical and digital products may vary depending on the application. Still, a true DT is obtained when a link exists between the two in both directions. DTs are a concept that is particularly important in the construction sector because of their considerable potential benefits. Fig. 3 displays the concept of digital twin technology.
Fig. 3. Digital twin technology.
Sources: Structville.comHowever, because there is no universally accepted definition, many different definitions of DTs may be found in this sector. It is vital to investigate these often-used definitions. According to Glaessgen & Stargel [71], a digital twin technology is an integrated multi-physics, multi-scale, probabilistic simulation of a complex product. This simulation uses the best available physical models, sensor updates, and other such things to mirror the life of its corresponding twin. According to Schroeder et al. [72], a cyber-physical system product lifecycle model represents a real virtual product that contains information on the product from the start of its lifecycle to its disposal. According to Leng et al. [73], the linked representation is a digital twin technology of the machine running in the cloud platform and imitating the health status based on combined data-driven analytical algorithms and other accessible physical knowledge. In their Gemini Principles, the Centre for Digital Built Britain (CDBB) suggested two unique definitions of a DT, the first of which emphasized the model's dynamism and the second of which emphasized the strategic importance. A dynamic model of an asset that receives current performance data from its physical twin in the form of live data flows from sensors and provides feedback to the physical twin in the form of real-time control [74]. A system model for the system's static strategic planning gives a reaction into the physical twin through capital investment process using imputed long-term data from the physical twin via corporate strategies.
As the demand for enhanced efficiency and competitiveness in construction grows, adopting Digital twin technology (DT) solutions becomes more prevalent [12]. This involves integrating various Industry 4.0 technologies tailored to construction contexts [75]. The following sections delve into implementing Industry 4.0 technologies relating to DT's four basic features. These features comprise the acquisition of data, process of data, simulation and modelling, and decision support enablers [12].
Modelling and Simulation: Modelling and Simulation are fundamental components of DT technologies. Both utilise 3D high-fidelity models and simulations to provide detailed visualizations for evaluating some particular scenarios to validate automatically computed solutions. These aspects synergistically complement other construction-related technologies presented in Table 2.
Table 2. Modelling and simulation in Construction industry.
| Technology | Construction Applications | References |
|---|---|---|
| Building Information Modelling (BIM) | To enhance structural health monitoring (SHM), it is vital to incorporate disaster planning and damage inspection capabilities. By integrating these aspects, the SHM system can better predict and respond to potential structural issues, ensuring safety and mitigating the impact of disasters. | [63,64] |
| Building lifecycle management (BLM), comfort, and energy efficiency are all improved via facility management. Include decision support systems, maintenance work, and anomaly detection. | [50,52] | |
| Designing and optimizing assets. Automate construction-related productions by using configure-to-order and lean manufacturing strategies. Maximize the output of precast components. Establish sustainable behaviours. | [76,77] | |
| Virtual/Augmented Reality | The partnership between humans and robots. Makes job planning and supervision easier through two-way communication and asset management. | [38] |
| Design and planning of cities. Several perspectives and usability testing from individuals participating in the construction process who are not experts. | [38] | |
| Point cloud | Visualization and design of assets. Creates city and building models utilizing as-built reconstruction techniques, gestalt design principles, and LiDAR. ML/DL-based point cloud interpretation is used to categorize models. | [54,68] |
| It examines how well-established a structure is, predicts possible damage that may occur in a structure, and inspects services for digitized structures in a VR environment. | [53,54] | |
| Simulation | Optimizing the design of a structure. Using parametric geometric modelling and high-resolution analysis, prototype development time and cost may decrease. | [65,66] |
| Optimizing building performance. Activate infrastructure visualizations for monitoring electricity and the environment. | [53] |
Data processing: Data processing plays a crucial role in handling the vast amounts of real-time, diverse data collected. It involves converting and treating raw data to extract meaningful information for modelling and analysis purposes. Table 3 provides an overview of the enabling technological tools adopted in various scholarly works in addressing challenges peculiar to the construction industry.
Table 3. Data processing in the Construction industry.
| Technology | Construction Applications | References |
|---|---|---|
| Data Mining | Management of projects. The development of greater automation and intelligence | [78] |
| Optimizing building performance. Enhance the energy effectiveness for both old and new structures. | [79] | |
| Blockchain | Create sustainable habits. Creates a blockchain-integrated intelligent platform to sustain built-up residential structures. | [80] |
| Management of projects. Enhance efficiency by implementing contracts, stakeholder cooperation, and improved service. | [55] | |
| Semantic Modelling | Designing and optimizing assets. Re-/configuration of equipment should be permitted for managing any form of interference. Locate a panorama with a localization error of not less than 1 m. Boost the representation of assets. | [48] |
Decision support enablers: Decision support is a critical aspect of construction systems, empowering them to handle disruptions and ensure smooth lifecycle transitions. Implementing semantic solution production using different tools and approaches is key to this capability, as outlined in Table 4. Emphasizing the significance of artificial intelligence (AI) in decision support, specific AI domains play a pivotal role in this context.
Table 4. Decision support enablers in the Construction industry.
| Technology | Construction Applications | References |
|---|---|---|
| Machine Learning | Monitoring of construction equipment. Analyze how an asset performs under various circumstances. | [81] |
| Maximizing on-site construction. Optimize building component structures and the construction process timetable. | [82] | |
| Control over safety. Create a DT-based interior safety management system as well as a security system for an elevator with three floors in a commercial building. | [83] | |
| Computer Vision | Management of facilities such as the renovation of structures of the 3D structure from CAD sketches and street view pictures, as well as movement recognition for maintenance activities. | [40] |
| System for maintaining bridges. Image recognition is used to improve inspection procedures. | [84] |
Data acquisition: The extraction of unprocessed data is initiated by the data acquisition process and concludes with the information transmission to a cloud-based server or database. In Table 5, we have outlined the key technologies along with their associated construction applications.
Table 5. Data acquisition in construction industry.
| Technology | Construction Applications | References |
|---|---|---|
| Internet of Things (IoT) | Improving structural health monitoring (SHM). Infrastructure preventive maintenance should be included. | [85] |
| Optimizing building performance. Include interior safety management, sustainability evaluation, energy efficiency, and an improved FM system in the BLM process. | [86] | |
| Wireless Sensor Network | Enhance lifetime management and energy saving to reduce building costs. | [87] |
| It uses cyber-physical systems in order to improve structural health monitoring (SHM) | [88] | |
| Social Media | Boost the management of the construction lifecycle. Plan, design, construction, and usage elements are included. | [89] |
In the context of advanced construction, the process revolves around digitalizing assets and resources through a meticulous approach, transforming them into a virtual space [78,79]. This comprehensive digitalization entails acquiring data from multiple sources, real-time two-way connectivity for monitoring and control, and seamless cyber-physical information exchange. At construction sites, physical data is gathered using an array of sensors and communication devices, adhering to industrial communication protocols. Techniques like point cloud mapping and BIM modelling facilitate mapping this data onto cyber entities [86]. In the cyber component, various tools such as BIM, simulation, point cloud, and 3D models are employed to ensure accurate representation and simulation of construction activities [85,88]. The subsequent data processing and computational layers are vital in converting raw data into valuable information and knowledge. This is achieved by treating data, storage, retrieval, and modules relating to analytical processing. Data fusion and semantic modelling are utilized to seamlessly integrate multiple data sources and establish meaningful relationships between data nodes [89]. core construction applications are being located by the functional layer while incorporating domain-specific knowledge, including safety protocols, regulations, and stakeholder preferences [84]; [81]. This knowledge is meticulously refined and presented to end-users through intuitive visualization interfaces. These interfaces empower users to interact with and manage physical assets while implementing system-generated solutions. By integrating these modules, specific construction challenges can be effectively addressed and overcome [83].
3.5. The transition from BIM to digital twins
Within the construction context, DTs would stand for a conceptual strategy that uses various technological tools, part of which is Building Information Modelling (BIM), to bring about improvement in the process of deciding the sector [50,52]. According to Linderoth [90], BIM is presently the most extensively adopted digital modelling technology in the construction industry. It has long been used to produce 3D representations of assets. However, with the advent of the Industry Foundation Classes (IFC) standard, BIM has evolved over time to enable more integration. According to Petrova-Antonova & Spasov [91], providing a semantic 3D model that acts as a database of asset data is a key benefit of BIM technology. However, due to several technological, informational, and organizational difficulties, BIM data cannot be easily incorporated into other systems, including IoT devices [40]. The development of a CDE in tandem with BIM, which enables the semantic integration of diverse datasets, characteristics, and instances, was suggested by Lu et al. [40] as a solution to this problem. This system architectural paradigm presents a considerable opportunity to improve decision-making assistance, particularly during the lifecycle's design phase.
Despite these improvements, integration difficulties, which prevent useful information and data from being linked to other systems, continue to exist. Camposano et al. [92] stated that to address the difficulties, the idea of DT has recently surfaced as a potential option. The similarity of both BIM and DTs makes it possible for them to give 3D visualizations of assets; however, DTs provide higher complexity and integration possibilities since they focus on establishing a human-centric, useable platform. BIM provides 3D visualizations of assets. According to Camposano et al. [92], DT focuses on reflecting individuals’ interactions with the asset, while BIM concentrates primarily on the asset. Because the adoption of BIM has been so prevalent, it is suggested that a variety of potential applications that BIM can provide to the construction process be carried out in conjunction with the DT concept. Potential applications include the detection of clashes, quality control, cost estimation, safety management, construction simulation, construction logistics, visual communication, scheduling, and site monitoring [93].
The digital model is an essential component that enables many facets of the building and construction business. However, realizing the full potential of these features will require overcoming the limitations of BIM's integration capabilities. Because of this, creating more integrated platforms capable of integrating without any hitches with many other processes and systems is required [94]. The ultimate purpose of DTs is to accomplish the most advanced degree of digital maturity that is humanly achievable. However, because no example of a DT has reached its complete potential, the notion of DTs is not totally defined, making it challenging to ascertain whether a DT has reached its full potential [92]. In addition, the asset in and of itself to which a DT idea is applied greatly impacts the degree to which it has matured, seeing as how the use case determines maturity. Because there is no one agreed-upon meaning for the term, there are many distinct ways in which it may be interpreted. As a result, different stakeholders have differing expectations for the amount of connection they need and the information that must be sent to them. Therefore, it is essential to make certain that the deployment of DT is beneficial to all parties involved in the building business [94].
The value that would eventually be added by digital twin technology would be the wealth of dynamic data that it could handle, its meaning (semantics), and its ongoing knowledge accumulation regarding the physical world [93]. This would be the case since a digital twin technology would represent the physical world. This brings long-term benefits to the built environment because of the smarter and more efficient building process and more competent lifecycle management. A society becoming increasingly environmentally conscious would automatically lead to reduced lifespan costs, increased asset resilience, and decreased carbon emissions [93]. The government acknowledges the potential benefit of altering its built environment by utilizing DT principles because of its potential value to the stakeholders in the industry. Everybody knows that digital transformation is happening, which is mentioned in the Gemini principles, revealing the UK's vision for using digitalization in the sector. Through various forms of government assistance, the government intends to back the transition comprehensively.
3.6. Smart construction site
Site managers can use digital twin technology to organize the building process at the construction site. Monitoring and supervising projects on the construction site is possible by utilizing virtual reality (VR), augmented reality (AR), the Internet of Things (IoT), radio frequency identification (RFID), and other digital tools [78]. Insights may be gained on different aspects such as material logistics, management of workflow, and cost prediction by utilizing technologies such as data mining and modelling of processes. UAVs (unmanned aerial vehicles) and other picture-gathering vehicles are possibly adopted to compare the construction process and the structure model, which can provide a clearer idea of how far the site has progressed. Kifokeris & Koch [95] compiled a list of how blockchain technology may be integrated into various stages of the building process. Research has shown that using a digital model allows for safety concerns and potential risks in the workplace to be addressed in advance and reduced [93,96]. The construction workers and machine operators might benefit from mixed-reality simulation by identifying potential dangers related to the building stages and machine operations in advance.
Utilizing virtual reality (VR) for training on specialized construction tasks, such as assembling and disassembling tower cranes, effectively minimizes the risks traditionally associated with hands-on training. According to Zhang et al. [97], integrating Building Information Modeling (BIM) with hazard-detection algorithms facilitates the precise identification of fall risks, enhancing overall safety during construction. Furthermore, Zhang et al. [97] emphasized the necessity of regular updates to the algorithm-generated safety plans, given that not all construction elements may be comprehensively modelled in the initial phases. This indicates that gaps may exist between the real practice of construction activities and the stated assumptions in the model, thus requiring frequent algorithm updates. It was proposed by Boje et al. [93] that the digital twin technology paradigm should be adopted to address similar challenges through the linking of sensors to life activity for monitoring locations and workers’ locations to identify and prevent potentially dangerous situations.
Greif et al. [98] have developed a lightweight digital twin technology tailored for sectors not typically classified as high-tech. In conjunction with various sensors, this digital twin technology is employed to monitor metrics such as time intervals, quantities, and silo utilization. The collected data is then integrated with restrictions on truck transportation and scheduled silo rotations. The study is conducted within the context of a bulk material provider. The digital twin technology employs artificial intelligence and other algorithms to analyze this composite data, allowing for a nuanced evaluation encompassing historical and current information. It then recommends the most effective courses of action and calculates dividends for each individual client based on this knowledge. Therefore, the operators have the option of either accepting the offered plan of action as is or making adjustments to it. The business can improve its predictability and ability to save money since it is always aware of its equipment's location and the overall fill amount. Kifokeris & Koch's [95] study suggests that when material, information and financial flows are seamlessly and transparently integrated within the supply chains, the logistics and success of the building project can be achieved. It is further argued that the requirement for accuracy and reliability on the various flows, in conjunction with the requirement for transparency and accountability, makes blockchains an appropriate technology for use as a validator of the aforementioned characteristics. According to Boje et al. [93], this helps various stakeholders better understand one another.
3.7. Built environment
Recently, traditional thinking of the architectural, engineering, and construction sectors has been shifting to include facility management and operation as part of its scope of responsibility [99]. This connects important construction building stakeholders among themselves to enable the reconsidering of the workflow across the whole delivery process and enable a larger view of the established environment's lifespan [100]. In the research on digital twin technology used in the established environment, the term AECO (engineering, construction, architecture, and operation) or AEC/FM (architecture, engineering, construction, and facility management) is frequently used to refer to the industry [101,102]. The previous study focusing on digital twin technology by Urban Planning [103], has attracted attention to digital twin technology concerning the established environment [101]. An evolutional ladder was proposed by Deng et al. [101] for the established environment. The proposal went from building information modelling (BIM) to digital twins, in which simulation, sensors, and artificial intelligence supplement BIM.
The ability of a structure to get to the digital twin technology ladder category facilitates communication and interaction with the established environments. In order for buildings to exchange real-time data, Deng et al. [101] claim that the digital twin technology of the future generation is scalable from individual buildings to multi-building communities and even to the level of an entire city [1]. On the other hand, the existing body of research simply discusses many unattainable features and significant ideas relating to the digital twin technology of the future generation. The BIM level is usually categorised into different phases, such as the designing, structuring, and operating segments [104]. Conversely, the sophistication of simulation techniques enables precise evaluations of energy performance, thereby facilitating the emulation of future building processes [101]. Integrating the Internet of Things (IoT) enhances the granular control of energy operations, spatial deployment, and thermal comfort settings. This capability streamlines construction activities and enables meticulous risk assessment at individual and collective scales. The employment of artificial intelligence augments these simulation and monitoring processes, delivering real-time predictive analytics [105].
3.7.1. Buildings
The description given by scholars [1,40,101] outlined the move towards digital twin technology from BIM in managing assets, activities, and repairs. The managing of assets involving the use of BIM is lacking in several areas. These areas include how well coordinated the various technical aspects involving detailed information and LOD, the management aspect involving the amalgamation of the flow of duty and learning, and the setting of standards which has to do with harmonizing different procedures, technologies, and developmental stages by ensuring disciplines [105]. Compared to BIM, the digital twin technology contains more information and has a larger capacity for analysis [106]. Additionally, the digital twin technology must meet the requirements of intelligence, integration, efficiency, and interoperability. In the building process, involving the procedural and repairing stages, smart asset management is the level of a digital twin technology that represents the building and infrastructure level [107].
During the period of building a structure, various phases such as the designing, constructing, retrofitting and maintenance stages, together with the managerial and quality assurance level reports of digital twin technology, already exist [108]. Within the context of a case study of a university campus, a framework relating to digital twin technology has been described to be scalable from the managerial to the construction and the societal level. Different layers, such as the collection of data, the transmitting layer, the digital modelling layer, and the layer involving data integration and models, are all part of the framework [109]. In examining a building's digital twin technology and infrastructure, it is better to consider the digital twin technology subset, the neighbourhood, and the city. Through this relationship, an additional understanding of the social and economic repercussions and opportunities to improve city services such as trash management and transportation might be gained. Transportation, space deployment, healthiness, safety, power, happenings and failure forecasts, and asset and management of the environment are all parts of the dynamic building and city digital twin's service layer [109].
3.7.2. Cities
Yang et al. [110] propose that IoT, which has to do with managing building systems intelligently, should be adopted for various construction situations. They do not discuss digital twins. It is anticipated that intelligent buildings will have high energy efficiency, which will help conserve energy and supply smart services to create a city and manage energy that is sustainable and add value to an IoT environment chain. In the same manner, Woodhead et al. [111] also proposed that the central ecosystem component produced by an IoT network continues to function even after a construction project has been traditionally finished. In addition, Yang et al. [112] suggest that governments should prioritize eliminating regulatory bottlenecks and providing rules, user privacy, and security to improve investment incentives and the development of appropriate technologies. Lehner & Dorffner [113] research demonstrates that digital twin technology for urban environments can be effectively scaled, ranging from individual buildings or districts to entire cities, to deliver value to residents and stakeholders alike. Meanwhile, Deren et al. [114] advocate for forming such city-scale digital twin technology via human expertise and artificial intelligence synergy, aiming to enhance urban management protocols. To realize optimal energy utilization, sustainability goals, and operational efficiencies, this digital twin technology must engage in a symbiotic relationship with the infrastructural frameworks of smart cities, encompassing sectors like transportation, meteorology, and energy distribution.
3.8. Project delivery
In the industrial sector, a proposition for carrying out smart contracts has been made [115]. A study in this particular application of digital twin technology and blockchain technology was conducted to create protection for intellectual property rights (IPR) to ensure trust among various stakeholders [116]. This was accomplished by demonstrating that the implemented work by a subcontract producer's machines exists within the level of the contracted tolerance levels, thus ascertaining the quality of the end products[117]. They argue in their study that a predetermined degree of trust needs to be established before beginning any new kind of collaboration. The firm that owns the intellectual property rights is obligated to provide the company that is doing the contracting with access to confidential information, and the company doing the contracting is required to provide specifics on the operation of their manufacturing processes [98]. According to Nielsen et al. [115], trust may be established and preserved across different businesses by making provision for accessibility for all stakeholders regarding the advancement being achieved through the use of the machines and the utilization of the block-chain technology to verify the proper usage of data [118]. In addition to this, they argue that the trust eliminates the need for attorneys to review and approve contracts. Rawat et al. [119] propose and verify a framework relating to blockchain technology in support of combined delivery of the project. In addition, they explain the rising literature on smart contracts in the construction industry [120]. This is to allow and develop confidence between customers, contractors, and subcontractors, so making it feasible for agreed-upon milestone payments to be tied to real work done at the site; in part, this is to ensure the reflection of findings based on the study conducted by Nielsen et al. [115].
In their study, Dounas et al. [121] explore the intersection of blockchain technology and nD BIM, detailing how these technologies document and validate the evolution and modifications of building designs throughout the construction process. Likewise, Kifokeris & Koch, (2020) research in the construction field underscores the role of blockchain as an enabler for smart contracts. However, they note the scarcity of cases where this technology has been effectively implemented. Mahmoodian et al. [122] highlight the substantial risk of information attrition across various stages of a construction project, particularly during the transition from the construction to the operational phase. Adding to this discourse, Waqar et al. [123] provide a timeline delineating the methods for information linkage across multiple models, culminating in an operational BIM that captures the requisite data. Tchana et al. [21] agree that various models must communicate with one another. However, they suggest connecting the utilized models with a digital twin technology to protect traceable choices and the model record (Zhang et al., 2022). This will be done to prevent information from being overwritten or lost. The study by Love & Matthews [14] revealed that the true worth of digital twin technology utility technologies only becomes apparent when utilized from the manufacturing stage through asset management. Love et al. [124] explain why asset owners and organizations should use digital technology and how this should be done to produce value and the projected advantages. At handover time, asset owners may stipulate the need for a digital twin technology to facilitate real-time operations and maintenance procedures and receive the asset more effectively and efficiently (Wu et al., 2022). Adopting digital technologies driven by a need and want is preferable to be forced or presented with the latest technology to accept it [125]. The automation, extension, and transformational changes brought about by the installation of digital technology are enhanced by the benefits found in practice and the lifecycle implementation. Several different players investigate the necessary components before beginning construction on a structure [126]. When it comes to making growth plans within the municipality and when it comes to making future development plans for real estate businesses.
In this stage of the process, comparing the design digital twin technology very early in support of construction may be done automatically with current development plans to ensure that the proposed project satisfies the municipality requirements [127]. When the model is complete enough to move on with the project, the following phase may begin applying for a building permit [128]. The original design is modified to serve as a model for the finished structure, and it is used in both the preparation and the construction processes. Making a connection between the structure and the surrounding district ensures seamless logistics. It permits future events that will take place near the building location to be sought to automatically maintain it depending on the progress that is being made with the structure [129,130]. The reduction of disagreement in the construction site and other continuing activities in the neighbourhood can be achieved if the information is managed so all parties can easily see it [131]. During the process of delivering the project, a final check is performed to ensure that the building complies with all of the requirements and expectations. At this point, the structure has become an inbuilt digital twin technology which is visually connected to the city at the operational level (Li et al., 2021). Leveraging blockchain technology and secure authentication mechanisms, stakeholders across multiple levels gain access to both contextual information and generated data from the building. The evolution of multifaceted sensor systems is intrinsically tied to advancements in technology and system design. These state-of-the-art systems, equipped with cellular networking, GPS, and robotics capabilities, facilitate data collection in complex and demanding environments through sophisticated miniaturization and integration techniques (Wu et al., 2022).
Process efficiency dramatically increases by implementing AI-enhanced functions, including machine learning (ML), computer vision (CV), and optimization algorithms, leading to improved analysis and solutions (Zhang et al., 2022). Moreover, multi-function and integrated Digital twin technology(DT) systems seek to improve operational performance by including extra project concerns inside the same platform. This covers building evacuation, safety management, and environmental monitoring [132]. Expanding the breadth of implementation allows DT systems to perform more functionally, offering industrial relevance and comprehensively resolving pain issues [133]. City-scale DT systems shift the focus of building-oriented solutions to virtual cities’ mapping and management to support a large administrative system and urban planning. The circular economy, underscored by its commitment to sustainability, remains integral to modern construction practices [130]. It aims for resource conservation, emission curbing, and effective waste management throughout the entire lifecycle of a building – encompassing its construction, operation, and eventual decommissioning. Integrating lean concepts within prefabricated production frameworks amplifies efficiency and minimizes environmental footprint [131]. When subjected to temporal analysis, digital twin technology (DT) technology offers transformative avenues for project management by optimizing scheduling, reducing disruptions, and mitigating delay risks. Financial viability within the chosen business model is ensured through meticulous economic evaluations (Li et al., 2021).
Additionally, integrating DT systems with other innovative methods – stemming from prior research on complex environmental route planning and Building Information Modeling (BIM)-enabled detection tools – enhances the resilience and robustness of construction endeavours [129]. This multi-dimensional approach paves the way for augmenting DT functionalities, marking a paradigm shift within the construction sector. As outlined in Table 6, the categorization of Digital twin technology application benefits extends across six key dimensions: Method, Milieu, Measurement, Material, Machine and Manpower.
Table 6. Benefits of digital twin technology applications categorised based on construction lifecycle stages.
| Applications categorised | Lifecycle Stage | Construction Function | DT-Enabled Benefits | Reference |
|---|---|---|---|---|
| Method |
|
|
| [98,116,118] |
| Milieu |
|
|
| [128,134,135] |
| Measurement |
|
|
| [117,120,122] |
| Material | Operations and maintenance
|
|
| [[125], [126], [127]] |
| Machine |
|
|
| [[129], [130], [131],136] |
| Manpower |
|
|
| [132,133,137] |
From Table 6 above, the machine aspect encompasses all physical assets associated with machinery and equipment used in the construction sector, such as trucks and cranes. Embracing Digital Transformation (DT) technologies for high-value assets has become a common practice to boost productivity and minimize breakdowns during their operational lifespan. On the other hand, the human workforce, engaged in various roles throughout the construction phase, from designers to machine operators, is referred to as the "manpower" component. Despite the rapid adoption of DT in the construction process, a recent study primarily focuses on the on-site building stage. While there have been notable advances in material performance and tracking, encompassing raw materials and intermediary products like precast models, there is room for further exploration and innovation. The measurement element is very useful in understanding the value, cost, and pricing of construction work by efficiently converting drawing information into detailed descriptions and quantities. This process is crucial for accelerating the construction of infrastructures in a digitalized world, ensuring speed, reliability, and sustainability. Collecting and monitoring data pertaining to physical objects and target environments are vital for seamless operations. The milieu component considers ambient data, terrain type, and surrounding layout, providing insights into the physical environment where construction activities occur. Combining DT with the level of extension of details in Building Information Modelling (BIM), as demonstrated by Jiaying Zhang et al. [135], offers a robust framework for effective on-site construction site monitoring and management. Efforts to improve building and construction efficiency are encompassed under the Method aspect. Building form optimization is a practical approach for planners and architects to minimize the environmental impact while designing and engineering structures. Therefore, the construction sector is witnessing a significant transformation by integrating technologies. To harness its full potential, researchers and industry professionals must expand their focus beyond the on-site stage, explore new avenues for material performance, and optimize building processes. Combining human expertise with cutting-edge technologies can revolutionize the construction industry to build better, faster, and more sustainable infrastructures in the digital era.
4. Conclusion
A digital twin technology is a virtual counterpart to a tangible entity existing exclusively within the digital sphere. Within the construction industry context, this could extend from individual structures and infrastructures to expansive systems such as cities, nations, or even the entire planet. For a digital model to merit the designation of a twin, it must co-exist alongside its physical counterpart throughout its lifecycle. This implies that the inception of a digital twin technology is intrinsically linked to the conception of a new physical structure. Furthermore, both digital and physical entities mature synchronously, paralleling their respective functional roles. However, the digital twin technology does not need to mimic every facet of its physical counterpart. Instead, both entities must share attributes that contribute to the sustainability of the built environment, which may encompass enhancements in efficiency and project completion within the construction sector.
The digital twin's tolerance in supporting construction projects should not only be in the early design stage through project completion. It can be connected with the city's digital twin technology very early in the design process throughout the construction period and while the building is in operation, thanks to the ecosystem of digital twins. At the initial stage, the digital twin technology used in supporting the building is connected to the city's digital twin technology in relation to the planning of the urban and the involved procurement procedures. During the building phase, the scalable city digital twin technology can be coupled with the site logistics, including transportation plans and the work environment. If this were to happen, the site would become more analogous to the smart manufacturing factories. In addition, the structure's digital twin technology can connect with the city's digital twin technology regarding the management of predicted health and energy consumption. The digital is more than just a collection of newly developed technology; it also encompasses a new manner of doing business activities and a different mentality. The mentality of working together for the sake of future generations and the world.
Findings revealed that DT may be a foundation for a data-driven lifecycle that accumulates ever-increasing amounts of information and data and ultimately facilitates informed decision-making. Therefore, procedures that enable the collection of data that is both impactful and vital ought to be given a high priority. Circular construction is made possible by DTs' capacity to encompass the entirety of a building's lifecycle. This data-driven approach differentiates DTs from more conventional BIM models and other technologies. It can also build new business models relating to the DT platform's capabilities, especially for the O&M phase. Because of DTs' data-capability and digital platform can give clients and customers one-of-a-kind benefits, making it an appealing value proposition. Additionally, the degree to which DTs are able to facilitate the transition to Construction 4.0 was investigated in this study.
The research reveals that DT has the ability to promote this change. This conclusion is established concerning the Six Keys to Success Framework [138]. The interviews, however, shed light on the fact that this shift will not take place overnight. The DT idea is still in its infancy as it relates to the construction sector, and it is possible that the industry consequently may never accomplish its full potential. Despite this, although gradually adding capabilities to DTs and improving integration across systems is possibly the ideal approach to move ahead, the goalposts will never be reached in terms of the DT concept. The many players in an industry need to be on board with data-sharing and the standardization of tools and techniques for DT to reach its full potential. Because of this, everyone can access and contribute information to a single platform, ultimately resulting in less fragmentation within the building industry. This study expounds on the pivotal role of data within the Digital twin technology(DTs) paradigm, functioning both as an enabler and a catalyst. Yet, data exchange emerges as a crucial variable requiring further scholarly attention to deploy DTs successfully. Achieving the full potential of data-centric components within DTs is contingent upon the willingness of multiple stakeholders to engage in data sharing. Concurrently, ethical considerations, such as individual privacy risks, necessitate scrutiny, given the extensive data sharing inherent to DTs. Moreover, the issue of data ownership becomes particularly salient when transmitting data across disparate projects and divergent DT models. To enrich our comprehension of these complexities, future research endeavours could employ qualitative or quantitative methodologies complemented by case studies to assess stakeholders' readiness for data exchange and to quantify the prevalence of data-sharing practices.
CRediT authorship contribution statement
Taofeeq D. Moshood: Conceptualization, Writing – original draft. James OB. Rotimi: Writing – review & editing. Wajiha Shahzad: Supervision, Validation. J.A. Bamgbade: Methodology.
Acknowledgments
The authors would like to thank the School of Built Environment, Massey University, for their financial assistance with the CanConstruct Research Project under the MBIE-funded Research Program.
References
- [1]Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environmentBuildings, 11 (4) (2021), p. 151
- [2]Dynamic model of implementation efficiency of Building Information Modelling (BIM) in relation to the complexity of buildings and the level of their safetyMATEC Web of Conferences, 146 (2018), p. 1010
- [3]Implications of Construction 4.0 to the workforce and organizational structuresInternational Journal of Construction Management, 22 (2) (2022), pp. 205-217
- [4]Appraisal of stakeholders' willingness to adopt construction 4.0 technologies for construction projectsBuilt. Environ. Proj. Asset. Manag., 10 (4) (2020), pp. 547-565
- [5]The rise of PropTech: emerging industrial technologies and their impact on real estateJ. Property Invest. Finance, 39 (2) (2020), pp. 157-169
- [6]Mining 4.0—the impact of new technology from a work place perspectiveMining, Metallurgy & Exploration, 36 (2019), pp. 701-707
- [7]Engineering education 4.0:—proposal for a new Curricula2018 IEEE Global Engineering Education Conference (EDUCON) (2018), pp. 1273-1282
- [8]Digital Twin enabled mass personalization: a case study of a smart wetland maintenance systemInternational Manufacturing Science and Engineering Conference, vol. 84263 (2020), Article V002T07A025
- [9]A maturity model for manufacturing 4.0 in emerging countriesOperations Management for Social Good: 2018 POMS International Conference in Rio (2020), pp. 393-402
- [10]Information technology investment and digital transformation: the roles of digital transformation strategy and top managementBus. Process Manag. J., 29 (2) (2023), pp. 528-549
- [11]An investigation for integration of deep learning and digital twins towards Construction 4.0Smart and Sustainable Built Environment, 12 (3) (2023), pp. 461-487
- [12]Digital twin and industry 4.0 enablers in building and construction: a SurveyBuildings, 12 (11) (2022), p. 2004
- [13]McKinsey's state of machine learning and AI, 2017Forbes (2017)Forbes. Com/Sites/Louiscolumbus/2017/07/09/Mckinseys-State-of-Machine-Learning-and-Ai-2017Accessed 17th Dec 2020
- [14]The ‘how’of benefits management for digital technology: from engineering to asset managementAutom. ConStruct., 107 (2019), Article 102930
- [15]The sociology of technology before the turn to technologyTechnol. Soc., 47 (2016), pp. 40-48
- [16]10 sociomateriality: challenging the separation of technology, work and organizationAcad. Manag. Ann., 2 (1) (2008), pp. 433-474
- [17]Adoption of blockchain technology through digital twins in the construction industry 4.0: a PESTELS approachBuildings, 11 (12) (2021), p. 670
- [18]A 4D BIM system architecture for the semantic webProceedings of the 18th International Conference on Computing in Civil and Building Engineering: ICCCBE 2020 (2021), pp. 561-573
- [19]Home sharing in marketing and tourism at a tipping point: what do we know, how do we know, and where should we be heading?J. Bus. Res., 122 (2021), pp. 534-566
- [20]A review of the roles of digital twin in CPS-based production systemsProcedia Manuf., 11 (2017), pp. 939-948
- [21]Designing a unique Digital Twin for linear infrastructures lifecycle managementProcedia CIRP, 84 (2019), pp. 545-549
- [22]Digital twins for cities: a state of the art reviewBuilt. Environ., 46 (4) (2020), pp. 547-573
- [23]Interactions of building information modeling, lean and sustainability on the architectural, engineering and construction industry: a systematic reviewJ. Clean. Prod., 174 (2018), pp. 788-806
- [24]A systematic review of lean construction in Mainland ChinaJ. Clean. Prod., 257 (2020), Article 120581
- [25]Doing Your Literature Review: Traditional and Systematic Techniques(2011)
- [26]Digital twins driven supply chain Visibility within logistics: a new paradigm for future logisticsApplied System Innovation, 4 (2) (2021), 10.3390/asi4020029
- [27]Sustainable system for supply chain management in the Malaysian manufacturing industries: a review and research directionInternational Journal of Supply Chain and Operations Resilience, 5 (1) (2021), pp. 79-98
- [28]Barriers and benefits of ICT adoption in the Nigerian construction industry. A comprehensive literature reviewApplied System Innovation, 3 (4) (2020), p. 46
- [29]An integrated paradigm for managing efficient knowledge transfer: towards a more comprehensive philosophy of Transferring knowledge in the construction industryConstruction Economics and Building, 22 (3) (2022), pp. 65-98, 10.5130/AJCEB.v22i3.8050
- [30]Knowledge transfer and management in the construction industry: trends and future challengesInternational Journal of Construction Supply Chain Management, 12 (1) (2022), pp. 72-102, 10.14424/ijcscm120122-72-102
- [31]Priorities of training of digital personnel for industry 4.0: social competencies vs technical competenciesHorizon, 27 (3/4) (2019), pp. 138-144
- [32]Big data challenges and opportunities in the hype of Industry 4.02017 IEEE International Conference on Communications (ICC) (2017), pp. 1-6
- [33]The new elements of digital transformationMIT Sloan Manag. Rev., 62 (2) (2020)
- [34]Enabling technologies and tools for digital twinJ. Manuf. Syst., 58 (2021), pp. 3-21
- [35]A. Sawhney, M. Riley, J. Irizarry (Eds.), Construction 4.0 (2020)
- [36]Integrating construction 4.0 technologies: a four-layer implementation planFrontiers in Built Environment, 144 (2021)
- [37]Engineering Optimization: Theory and PracticeJohn Wiley & Sons (2019)
- [38]Interactive and immersive process-level digital twin for collaborative human–robot construction workJ. Comput. Civ. Eng., 35 (6) (2021), Article 4021023
- [39]Cognitive digital twins for facilitating construction 4.0: challenges and opportunities for implementationFrontiers in Built Environment, 9 (2023), Article 1130115
- [40]Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawingsAutom. ConStruct., 115 (2020), Article 103183
- [41]The Advent of the Internet(2012)12th April, Courses
- [42]Internet of things (IoT): a literature reviewJ. Comput. Commun., 3 (5) (2015), p. 164
- [43]Big Data in the construction industry: a review of present status, opportunities, and future trendsAdv. Eng. Inf., 30 (3) (2016), pp. 500-521
- [44]Big data: a review2013 International Conference on Collaboration Technologies and Systems (CTS) (2013), pp. 42-47
- [45]BIM implementation throughout the UK construction project lifecycle: an analysisAutom. ConStruct., 36 (2013), pp. 145-151
- [46]Integration of digital technologies in the field of construction in the Russian FederationEngineering Management in Production and Services, 11 (3) (2019), pp. 38-47
- [47]Industry 4.0 and the digital twin for building industryIndustry 4.0, 5 (1) (2020), pp. 29-32
- [48]Multi-level voxel representations for digital twin models of tunnel geological environmentInt. J. Appl. Earth Obs. Geoinf., 112 (2022), Article 102887
- [49]An adapted model of cognitive digital twins for building lifecycle managementAppl. Sci., 11 (9) (2021), p. 4276
- [50]Bim and iot sensors integration: a framework for consumption and indoor conditions data monitoring of existing buildingsSustainability, 13 (8) (2021), p. 4496
- [51]Construction 4.0–digital transformation of one of the oldest industriesEcon. Bus. Rev., 21 (3) (2019), p. 4
- [52]Aproximación inicial al enfoque basado en BIM-based Digital Twinning en la toma de decisiones sobre Seguridad y Salud Laboral en la industria de la construcciónDirección y Organización, 76 (2022), pp. 5-12
- [53]A parametric scan-to-FEM framework for the digital twin generation of historic masonry structuresSustainability, 13 (19) (2021), Article 11088
- [54]Mapping the urban forest in detail: from LiDAR point clouds to 3D tree modelsUrban For. Urban Green., 74 (2022), Article 127637
- [55]Integrated digital twin and blockchain framework to support accountable information sharing in construction projectsAutom. ConStruct., 127 (2021), Article 103688
- [56]The Entanglement of Technology and Work in Organizations(2008)
- [57]Technological paradigms and complex technical systems—the case of smart homesRes. Pol., 37 (3) (2008), pp. 508-529
- [58]Paradigm Shift in Technologies and Innovation SystemsSpringer (2019)
- [59]Bridging the digital divide gap in BIM technology adoptionEng. Construct. Architect. Manag., 25 (10) (2018), pp. 1398-1416
- [60]Barriers and facilitators for BIM use among Swedish medium-sized contractors-“We wait until someone tells us to use it.”Visualization in Engineering, 5 (2017), pp. 1-12
- [61]BIM and the small construction firm: a critical perspectiveBuild. Res. Inf., 45 (6) (2017), pp. 696-709
- [62]Implementing ‘Site BIM’: a case study of ICT innovation on a large hospital projectAutom. ConStruct., 30 (2013), pp. 15-24
- [63]BIM based cyber-physical systems for intelligent disaster preventionJournal of Industrial Information Integration, 20 (2020), Article 100171
- [64]Methods for the automated assignment and comparison of building damage geometriesAdv. Eng. Inf., 47 (2021), Article 101186
- [65]Numerical simulation and evaluation of the world's first metal additively manufactured bridgeStructures, 42 (2022), pp. 405-416
- [66]Coupled simulation of thermally active building systems to support a digital twinEnergy Build., 202 (2019), Article 109298
- [67]Comparing project complexity across different industry sectorsComplexity, 2018 (2018)
- [68]From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principlesISPRS J. Photogrammetry Remote Sens., 167 (2020), pp. 418-431
- [69]Digital twin-based smart production management and control framework for the complex product assembly shop-floorInt. J. Adv. Des. Manuf. Technol., 96 (2018), pp. 1149-1163
- [70]Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systemsTransdisciplinary Perspectives on Complex Systems, Springer (2017), pp. 85-113
- [71]The digital twin paradigm for future NASA and US Air Force vehicles53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, vol. 1818 (2012)
- [72]Digital twin data modeling with automationml and a communication methodology for data exchangeIFAC-PapersOnLine, 49 (30) (2016), pp. 12-17
- [73]Digital twins-based remote semi-physical commissioning of flow-type smart manufacturing systemsJ. Clean. Prod., 306 (2021), Article 127278
- [74]A socio-technical perspective on urban analytics: the case of city-scale digital twinsJ. Urban Technol., 28 (1–2) (2021), pp. 263-287
- [75]Integration of industry 4.0 related technologies in construction industry: a framework of cyber-physical systemIEEE Access, 8 (2020), pp. 122908-122922
- [76]Automating construction manufacturing procedures using BIM digital objects (BDOs) Case study of knowledge transfer partnership project in UKConstruct. Innovat., 20 (3) (2020), pp. 345-377
- [77]BIM4D-based scheduling for assembling and lifting in precast-enabled constructionAutom. ConStruct., 133 (2022), Article 103999
- [78]A BIM-data mining integrated digital twin framework for advanced project managementAutom. ConStruct., 124 (2021), Article 103564
- [79]Optimizing legacy building operation: the evolution into data-driven predictive cyber-physical systemsEnergy Build., 148 (2017), pp. 257-279
- [80]A blockchain-and IoT-based smart product-service system for the sustainability of prefabricated housing constructionJ. Clean. Prod., 286 (2021), Article 125391
- [81]Applications of deep learning in intelligent constructionStructural Health Monitoring Based on Data Science Techniques (2022), pp. 227-245
- [82]Intelligent action planning for well construction operations demonstrated for hole Cleaning optimization and automationEnergies, 15 (15) (2022), p. 5749
- [83]Digital triplet approach for real-time monitoring and control of an elevator security systemDesigns, 4 (2) (2020), p. 9
- [84]Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin modelStructure and Infrastructure Engineering, 15 (10) (2019), pp. 1319-1332
- [85]Structural health monitoring and prognostic of industrial plants and civil structures: a sensor to cloud architectureIEEE Instrum. Meas. Mag., 23 (9) (2020), pp. 21-27
- [86]A framework for an indoor safety management system based on digital twinSensors, 20 (20) (2020), p. 5771
- [87]Dense Indoor Sensor Networks: towards passively sensing human presence with LORAWANPervasive Mob. Comput., 84 (2022), Article 101640
- [88]Survey and study on intelligent monitoring and health management for large civil structureInternational Journal of Intelligent Robotics and Applications, 3 (2019), pp. 239-254
- [89]A social–product–process framework for constructionBuild. Res. Inf., 48 (7) (2020), pp. 747-762
- [90]Understanding adoption and use of BIM as the creation of actor networksAutom. ConStruct., 19 (1) (2010), pp. 66-72
- [91]Towards a semantic 3D model of Sofia cityComputational Science and its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part IV, vol. 21 (2021), pp. 230-241
- [92]Seven metaphors to understand digital twins of built assetsIEEE Access, 9 (2021), pp. 27167-27181
- [93]Towards a semantic construction digital twin: directions for future researchAutom. ConStruct., 114 (2020), Article 103179
- [94]Digital twin research in the AECO-FM industryJ. Build. Eng., 40 (2021), Article 102730
- [95]A conceptual digital business model for construction logistics consultants, featuring a sociomaterial blockchain solution for integrated economic, material and information flowsJ. Inf. Technol. Constr., 25 (29) (2020), pp. 500-521
- [96]Technologies for safety and health management in construction: current use, implementation benefits and limitations, and adoption barriersJ. Build. Eng., 29 (2020), Article 101212
- [97]BIM-based fall hazard identification and prevention in construction safety planningSaf. Sci., 72 (2015), pp. 31-45
- [98]Peeking into the void: digital twins for construction site logisticsComput. Ind., 121 (2020), Article 103264
- [99]Towards a digital twin-based smart built environmentBIM-Enabled Cognitive Computing for Smart Built Environment (2021), pp. 21-44
- [100]Digital twins in built environments: an investigation of the characteristics, applications, and challengesBuildings, 12 (2) (2022), p. 120
- [101]From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industryJ. Inf. Technol. Construct., 26 (2021)
- [102]From smart construction objects to cognitive facility ManagementCyber-Physical Systems in the Built Environment (2020), pp. 273-296
- [103]Digital twin of a city: review of technology serving city needsInt. J. Appl. Earth Obs. Geoinf. (2022), Article 102915
- [104]Digital Twins for the built environment: learning from conceptual and process models in manufacturingAdv. Eng. Inf., 49 (2021), Article 101332
- [105]Digital twin-enabled built environment sensing and monitoring through semantic enrichment of BIM with SensorMLAutom. ConStruct., 144 (2022), Article 104625
- [106]Digital twinning of the built environment–an interdisciplinary topic for innovation in didacticsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4 (2020), pp. 231-237
- [107]Automated generation of digital twin for a built environment using scan and object detection as input for production planningJournal of Industrial Information Integration, 33 (2023), Article 100462
- [108]Digital twins for performance management in the built environmentJournal of Industrial Information Integration, 33 (2023), Article 100445
- [109]Digital twin for sustainability evaluation of railway station buildingsFrontiers in Built Environment, 4 (2018), p. 77
- [110]A mixed activity-based costing and resource constraint optimal decision model for IoT-oriented intelligent building management system portfoliosSustain. Cities Soc., 60 (2020), Article 102142
- [111]Digital construction: from point solutions to IoT ecosystemAutom. ConStruct., 93 (2018), pp. 35-46
- [112]Appropriate technology and energy security: from the perspective of biased technological changeTechnol. Forecast. Soc. Change, 177 (2022), Article 121530
- [113]Digital geoTwin Vienna: towards a Digital Twin City as Geodata HubSpringer (2020)
- [114]Smart city based on digital twinsComputational Urban Science, 1 (2021), pp. 1-11
- [115]Digital twins and blockchain–proof of conceptProcedia Cirp, 93 (2020), pp. 251-255
- [116]Cyber physical system for safety management in smart construction siteEng. Construct. Architect. Manag., 28 (3) (2021), pp. 788-808
- [117]Carbon emission reduction in prefabrication construction during materialization stage: a BIM-based life-cycle assessment approachSci. Total Environ., 723 (2020), Article 137870
- [118]Digital twin-based safety risk coupling of prefabricated building hoistingSensors, 21 (11) (2021), p. 3583
- [119]Blockchain technology: emerging applications and use cases for secure and trustworthy smart systemsJournal of Cybersecurity and Privacy, 1 (1) (2020), pp. 4-18
- [120]Structural digital twin framework: Formulation and technology integrationAutom. ConStruct., 140 (2022), Article 104333
- [121]Framework for decentralised architectural design BIM and Blockchain integrationInt. J. Architect. Comput., 19 (2) (2021), pp. 157-173
- [122]Development of digital twin for intelligent maintenance of civil infrastructureSustainability, 14 (14) (2022), p. 8664
- [123]Barriers to Building Information Modeling (BIM) deployment in small construction projects: Malaysian construction industrySustainability, 15 (3) (2023), p. 2477
- [124]Is it just too good to be true? Unearthing the benefits of disruptive technologyInt. J. Inf. Manag., 52 (2020), Article 102096
- [125]City Regeneration through modular phase change materials (PCM) Envelopes for Climate Neutral buildingsSustainability, 14 (14) (2022), p. 8902
- [126]Digital twins and road construction using secondary raw materialsJ. Adv. Transport., 2021 (2021), pp. 1-12
- [127]BIM-enabled computerized design and digital fabrication of industrialized buildings: a case studyJ. Clean. Prod., 278 (2021), Article 123505
- [128]The digital twin of the city of Zurich for urban planningPFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88 (1) (2020), pp. 99-112
- [129]Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic constructionAdv. Eng. Inf., 53 (2022), Article 101710
- [130]Towards fully BIM-enabled building automation and robotics: a perspective of lifecycle information flowComput. Ind., 135 (2022), Article 103570
- [131]A cyber–physical system (CPS) for planning and monitoring mobile cranes on construction sitesProceedings of the Institution of Civil Engineers-Management, Procurement and Law, 171 (6) (2018), pp. 240-250
- [132]Digital twin and web-based virtual gaming technologies for online education: a case of construction management and engineeringAppl. Sci., 10 (13) (2020), p. 4678
- [133]Cyber-physical postural training system for construction workersAutom. ConStruct., 117 (2020), Article 103272
- [134]Digital twins and artificial intelligence in transportation infrastructure: classification, application, and future research directionsComput. Electr. Eng., 101 (2022), Article 107983
- [135]Digital twins for construction sites: concepts, LoD definition, and applicationsJ. Manag. Eng., 38 (2) (2022), Article 4021094
- [136]Digital twin-driven virtual sensor approach for safe construction operations of trailing suction hopper dredgerAutom. ConStruct., 132 (2021), Article 103961
- [137]Real-time mixed reality-based visual warning for construction workforce safetyAutom. ConStruct., 139 (2022), Article 104252
- [138]The transformative business modelHarv. Bus. Rev., 94 (10) (2016), pp. 91-98
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