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Purpose>The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).Design/methodology/approach>IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.Findings>The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.Practical implications>DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.Originality/value>The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

Artificial Intelligence (AI) simulation models and digital twins (DT) are used in designing and treating the activities, layout, and functions for the new generation of buildings to enhance user experience and optimize building performance. These models use data about a building’s use, configuration, functions, and environment to simulate different design options and predict their effects on house function efficiency, comfort, and safety. On the one hand, AI algorithms are used to analyze this data and find patterns and trends that can guide the design process. On the other hand, DTs are digital recreations of actual structures that can replicate building performance in real time. These models would evaluate alternative design options, the performance of the building, and ways to improve user comfort and building efficiency. This study examined the important role of intelligent building design aspects, such as activities using multi-layout and the creation of particular functions based on AI simulation models, in developing DT-based smart building systems. The empirical data came from a study of architecture and engineering firms throughout the globe using a CSAQ (computer-administered, self-completed survey). For this purpose, the study employed structural equation modeling (SEM) to examine the hypotheses and build the relationship model. The research verifies the relevance of AI-based simulation models supporting the creation of intelligent building design features (activities, layout, functionalities), enabling the construction of DT-based smart building systems. Furthermore, this study highlights the need for further exploration of AI-based simulation models’ role and integration with DT in smart building design.

PurposeThe purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.Design/methodology/approachA mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.FindingsBased on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.Practical implicationsThe DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.Originality/valueThe research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

The Construction 5.0 paradigm is the next phase in industrial development that aims to combine the skills of human experts in partnership with efficient and precise machines to achieve production solutions that are resource-efficient and preferred by clients. This study reviewed the evolution of the Construction 5.0 paradigm by defining its features and diverse nature. It introduced the architecture, model, and system of Construction 5.0 and its key enablers: Operator 5.0, Society 5.0, human-centricity, sustainability, and resilience. The study used the SEM method to evaluate the research model and investigate the causal relationships among the key enablers of the Construction 5.0 paradigm. Nine vital hypotheses were proposed and assessed comprehensively. The critical enablers’ variables were measured to examine the constructs’ reliability and validity. The key findings showed that Construction 5.0 prioritizes collaboration between humans and machines, merges cyberspace with physical space, and balances the three pillars of sustainability (economy, environment, and society), creating a relationship among Operator 5.0, Society 5.0, human-Ccentricity, sustainability, and resilience. The study also discussed the limitations and challenges and offered suggestions for future research. Overall, Construction 5.0 aims to achieve sustainable development and become a robust and resilient provider of prosperity in an industrial community of a shared future. The study expects to spark debate and promote pioneering research toward the Construction 5.0 paradigm.

The building industry is one of the most resource-intensive sectors in industrialized countries, requiring a shift from a linear to a more sustainable circular economic model. Nevertheless, there are several major challenges, such as the management of information regarding used materials and products, the lack of cross-sector documentation tools, and sales operations for implementing a dynamic circular economy in the building industry. To overcome these challenges, blockchain technology for documentation, tracing used materials and products, and the use of multi-criteria decision-making approaches for the ranking and selection of optimal used materials and products have emerged as crucial facilitators, with the potential to address the technological, organizational, environmental, and economic requirements. The purpose of this study is to develop a theoretical framework of a digital platform ecosystem for implementing a dynamic circular economy in the building industry through the integration of blockchain technology and a multi-criteria decision-making approach built upon their synergy. The priority order of two alternatives of used materials and products was determined according to the AHP method, leading to selection of the most sustainable alternative. This research study contributes to dynamic circular economies by (1) facilitating cross-sector information transparency and the tracing of used materials and products from their sources to their end-of-life stages and through (2) the ranking and selection of used materials and products based on their overall properties.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. Modern building techniques have been made more efficient and long-lasting because of AEC Industry 6.0’s cutting-edge equipment, cutting-edge digitalization, and ecologically concerned methods. The academic community has thoroughly dissected the many benefits of AEC Industry 5.0. Examples are increased stakeholder involvement, automation, robotics for optimization, decision structures based on data, and careful resource management. However, the difficulties of implementing AEC Industry 6.0 principles are laid bare in this research. It calls for skilled experts who are current on the latest technologies, coordinate the technical expertise of many stakeholders, orchestrate interoperable standards, and strengthen cybersecurity procedures. This study evaluates how well the principles of Industry 6.0 can create smart, long-lasting, and ecologically sound structures. The goal is to specify how these ideas may revolutionize the building industry. In addition, this research provides an in-depth analysis of how the AEC industry might best adopt AEC Industry 6.0, underscoring the sector-wide significance of this paradigm change. This study thoroughly analyzes AEC Industry 6.0 about big data analytics, the IoT, and collaborative robotics. To better understand the potential and potential pitfalls of incorporating AEC Industry 6.0 principles into the construction of buildings, this study examines the interaction between organizational dynamics, human actors, and robotic systems.

PurposeRapid advancements in blockchain technology transform various sectors, attracting the attention of industrialists, practitioners, policymakers and academics, and profoundly affect construction businesses through smart contracts and crypto-economics. This paper explores the blockchain innovation ecosystem in construction.Design/methodology/approachThrough a qualitative study of 23 diverse interviewees, the study explores how open or closed the blockchain innovation ecosystem in construction is and who its emerging orchestrators are.FindingsThe data showed that construction aims towards an open innovation blockchain ecosystem, although there are elements of hybridisation and closedness, each system pointing out to different orchestrators.Practical implicationsThe study has implications for governments and large companies in construction, showing that open innovation initiatives need to be encouraged by policymakers through rules, regulations and government-sponsored demonstrator projects.Social implicationsThe data showed that there is lack of readiness for business model change to support open innovation blockchain ecosystems in construction.Originality/valueThis is the first study applying the open innovation theory in the construction industry and sheds light into the phenomenon of blockchain, suggesting routes for further democratisation of the technology for policymakers and practitioners.

The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era of architectural concepts. AI simulation models can improve home functions and users’ comfort and significantly cut energy consumption through better control, increased reliability, and automation. This article highlights the potential of using artificial intelligence (AI) models to improve the design and functionality of smart houses, especially in implementing living spaces. This case study provides examples of how artificial intelligence can be embedded in smart homes to improve user experience and optimize energy efficiency. Next, the article will explore and thoroughly analyze the thorough analysis of current research on the use of artificial intelligence (AI) technology in smart homes using a variety of innovative ideas, including smart interior design and a Smart Building System Framework based on digital twins (DT). Finally, the article explores the advantages of using AI models in smart homes, emphasizing living spaces. Through the case study, the theme seeks to provide ideas on how AI can be effectively embedded in smart homes to improve functionality, convenience, and energy efficiency. The overarching goal is to harness the potential of artificial intelligence by transforming how we live in our homes and improving our quality of life. The article concludes by discussing the unresolved issues and potential future research areas on the usage of AI in smart houses. Incorporating AI technology into smart homes benefits homeowners, providing excellent safety and convenience and increased energy efficiency.

Hybrid learning is a complex combination of face-to-face and online learning. This model combines the use of multimedia materials with traditional classroom work. Virtual hybrid learning is employed alongside face-to-face methods. That aims to investigate using Artificial Intelligence (AI) to increase student engagement in hybrid learning settings. Educators are confronted with contemporary issues in maintaining their students’ interest and motivation as the popularity of online and hybrid education continues to grow, where many educational institutions are adopting this model due to its flexibility, student-teacher engagement, and peer-to-peer interaction. AI will help students communicate, collaborate, and receive real-time feedback, all of which are challenges in education. This article examines the advantages and disadvantages of hybrid education and the optimal approaches for incorporating Artificial Intelligence (AI) in educational settings. The research findings suggest that using AI can revolutionize hybrid education, as it enhances both student and instructor autonomy while fostering a more engaging and interactive learning environment.

The integration of blockchain and digital twins (DT) for better building-lifecycle data management has recently received much attention from researchers in the field. In this respect, the adoption of enabling technologies such as artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), cloud and edge computing, Big Data analytics, etc., has also been investigated in an abundance of studies. The present review inspects the recent studies to shed light on the foremost among those enabling technologies and their scope, challenges, and integration potential. To this end, 86 scientific papers, recognized and retrieved from the Scopus and Web of Science databases, were reviewed and a thorough bibliometric analysis was performed on them. The obtained results demonstrate the nascency of the research in this field and the necessity of further implementation of practical methods to discover and prove the real potential of these technologies and their fusion. It was also found that the integration of these technologies can be beneficial for addressing the implementation challenges they face individually. In the end, an abstract descriptive model is presented to provide a better understanding of how the technologies can become integrated into a unified system for smartening the built environment.