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In the digital transformation era in the Architecture, Engineering, and Construction (AEC) industry, Cognitive Digital Twins (CDT) are introduced as part of the next level of process automation and control towards Construction 4.0. CDT incorporates cognitive abilities to detect complex and unpredictable actions and reason about dynamic process optimization strategies to support decision-making in building lifecycle management (BLM). Nevertheless, there is a lack of understanding of the real impact of CDT integration, Machine Learning (ML), Cyber-Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Internet of Things (IoT), all connected to self-learning hybrid models with proactive cognitive capabilities for different phases of the building asset lifecycle. This study investigates the applicability, interoperability, and integrability of an adapted model of CDT for BLM to identify and close this gap. Surveys of industry experts were performed focusing on life cycle-centric applicability, interoperability, and the CDT model’s integration in practice besides decision support capabilities and AEC industry insights. The evaluation of the adapted model of CDT model support approaching the development of CDT for process optimization and decision-making purposes, as well as integrability enablers confirms progression towards Construction 4.0.

Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems. This study seeks to investigate the potential fusion of KGs into CMP to bolster decision-making processes on the production line. Empirical data were collected through a computerized self-administered questionnaire (CSAQ) survey, with a specific emphasis on exploring the potential benefits of incorporating KGs into CMP. The quantitative analysis findings underscore the effectiveness of integrating KGs into CMP, particularly through the utilization of visual representations that depict the relationships between diverse components and subprocesses within a virtual environment. This fusion facilitates the real-time monitoring and control of the physical production process. By harnessing the power of KGs, CMP can attain a comprehensive understanding of the manufacturing process, thereby supporting interoperability and decision-making capabilities within modular production systems in the industrialized building industry.

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.

The architecture, engineering, and construction (AEC) industry stands to benefit tremendously from the integration of lean construction (LC), building information modeling (BIM), and extended reality (XR) technologies at all stages of a project. These technologies enable multidimensional content viewing and collaboration through cloud-based systems and in real-scale environments, resulting in higher levels of efficiency. The aim of this research is to offer an integrative approach that combines project management philosophies, systems, technologies, and tools. The sections containing the results of this study are as follows. (1) A concise review of the benefits of LC, BIM, and XR technologies in the AEC industry, including BIM-based visualization support for LC (Lean-BIM) and BIM visualization in XR (BIM-XR). This section also presents an overview of the most commonly used wearable XRs on the market. (2) The presentation of an LBX process flow diagram and an IDEF0 diagram for the LBX project delivery management system at each stage of AEC projects, including design, construction, and operation. (3) Two possible scenarios for integrated lean, BIM, and XR implementation are suggested, referred to as “in the office” and “online or semi-online LBX meetings”. (4) An analysis of the strengths and weaknesses of the LBX management system, practical implications, and open challenges of applying LBX to project management tasks. Overall, this study presents an enormous opportunity to increase the quality of construction project planning, understanding, and performance, and provides a roadmap for future efforts to implement the integration of LC, BIM, and XR technologies in the AEC industry.

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.

Purpose The concept of Construction 5.0 has emerged as the next frontier in construction practices and is characterized by the integration of advanced technologies with human-centered approaches, sustainable practices and resilience considerations to build smart and future-ready buildings. However, there is currently a gap in research that provides a comprehensive perspective on the opportunities and challenges of facilitating Construction 5.0. This study aims to explore the opportunities and challenges in facilitating Construction 5.0 and its potential to implement smart, sustainable and resilient buildings. Design/methodology/approach The structural equation modeling (SEM) method was used to evaluate the research model and investigate the opportunities and challenges related to Construction 5.0 in its implementation for smart, sustainable and resilient buildings. Findings The results show that adopting human-centric technology, sustaining resilience and maintaining sustainability in the architecture, engineering and construction (AEC) industry seizes the opportunities to overcome the challenges for facilitating Construction 5.0 in the implementation of smart, sustainable and resilient buildings. Practical implications The AEC industry facilitating Construction 5.0 has the potential to redefine the future of construction, creating a built environment that is not only intelligent, sustainable and resilient but also deeply connected with the well-being and values of the communities it serves. Originality/value The research illuminates the path forward for a holistic understanding of Construction 5.0, envisioning a future where smart, sustainable and resilient buildings stand as testaments to the harmonious collaboration between humans and technology.