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.

Purpose: The 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/approach: A 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.Findings: Based 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 implications: The 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/value: The 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).

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.

Introduction. A variety of approaches exist to achieve better construction safety performance, but only a few consider a combination of building information modeling (BIM) and unmanned aerial vehicles (UAVs). Method. This article presents a four-dimensional (4D) BIM/UAV-enabled safety management model based on IDEF0 language. In the first step, potential hazards are identified with the help of safety specialists’ experiences and BIM software used in the design of the structure. Then, a UAV monitors the location of the potential hazards. The third step involves the integration of the 4D BIM-based model and the information obtained from the UAV. Finally, the combined data are analyzed and interpreted, and site safety staff are notified about measures to be put in place to prevent accidents. Results. This model shows a strong relationship between the design and construction phases by using BIM in the design phase and UAVs in the construction phase. The proposed safety model was evaluated by construction safety specialists in a two-pronged approach. Conclusion. The number of fatal, non-fatal and property damage-causing accidents may be significantly lower when the proposed system was used. Practical application. This model allows safety specialists to identify hazards and develop suitable mitigation strategies.

Building Information Modeling (BIM) and Virtual reality (VR) have attracted growing attention within the architecture, engineering, and construction (AEC) industry in recent years. Integration of BIM and VR technology can develop workflow efficiency through enhanced common understanding and prepare students in architecture and engineering programs to become leaders of the AEC industry. However, the current shortage of AEC professionals trained in BIM and VR is still a barrier to collaborative working practice in this industry. This paper reviews previous work on the BIM, VR, and BIM-into-VR in AEC education/training to bridge this gap. It also presents an advanced framework to clarify creating and using the BIM model into VR workflow in the AEC industry through the integrated definition function (IDEF0) model. The authors further evaluated the BIM-into-VR applications in literature and real-life by surveying students’ learning performance in terms of eight characteristics relevant to the VR environment and students’ performance within two projects, one involving the “NASA Mars Habitat Project” and the other involving the “Norris Center Project” at Northwestern University. The results confirmed that BIM-into-VR usability and efficiency in improving students’ main learning performance characteristics: Learnability, Interoperability, Visualization, Real-world, Interaction, Creativity, Motivation, and Comfort. This study addresses the advantages of using BIM-into-VR in AEC programs. It also offers suggestions to AEC educators and students in implementing BIM-into-VR in different courses and creating a roadmap for their future as professionals in the AEC industry.