With updated equipment and maturing technology, the applications of augmented and virtual reality (AR/VR) technologies in the architecture, engineering, and construction (AEC) industry are receiving increasing attention rapidly. Especially in education and training, an increasing number of researchers have started to implement AR/VR technologies to provide students or trainees with a visual, immersive, and interactive environment. In this article, a systematic review of AR/VR technologies for education and training in the AEC industry is conducted. First of all, through comprehensive analysis, 82 related studies are identified from two databases, namely Scopus and Web of Science. Secondly, the VOSviewer is used to analyze the current status of AR/VR for education and training in the AEC industry. Thirdly, the identified studies are classified into different categories according to their application domains by qualitative analysis. Fourthly, after a further filtering, 17 out of the 82 studies are included in the meta-analysis to quantify the actual impact of AR/VR. The results indicate that there are some limitations in the applications of AR/VR for education and training in the AEC industry. Finally, to further explore the reasons for the existence of limitations, the 82 studies are summarized to analyze the current challenges of AR/VR for education and training in the AEC industry. This study also provides insights into future trends in AR/VR for education and training in the AEC industry.

Evacuation navigation in emergencies such as fires is one of the most important operational considerations for a building. The large and complicated interior spaces, as well as the intensive population significantly increase the difficulty of fire evacuation in large-scale buildings. The environmental changes such as the spread of a fire and the flow of evacuees exacerbate the difficulties of fire evacuation. Therefore, this research aims to develop an adaptive approach for path planning against the rapid environmental changes in fires. In this paper, a graph-based network is formed by integrating MAT with VG, with the addition of a buffer zone. The network uses real-time videos from closed-circuit television (CCTV) cameras facilitated by deep learning algorithms to detect and tally the number of people in a target area. According to the tally of people and a proposed walkability model, the congestion conditions of an area can be analysed so that evacuees can avoid any areas that are congested. An Internet of things sensor network is also established to detect the presence of hazardous areas. The proposed solution allows evacuation navigation to be done in real time. An illustrative example is provided to demonstrate the functionality and features of this proposed methodology.

Smart construction has become popular because it improves the efficiency of various activities on construction sites. Data sharing is crucial for multiple stakeholders to achieve smart construction management. However, a unified framework is absent for data sharing between stakeholders. This study aims to develop a unified framework for data sharing in smart construction at the project level. A two-pronged design method is adopted to develop a unified framework for data sharing in smart construction using text analysis and semi-structured interviews. Then, a case study is incorporated for the application of the smart construction management framework. The unified framework for data sharing is developed as a five-layer architecture, consisting of the primary layers of perception, transmission, database, application, and presentation. The function modules of the unified framework include on-site monitoring, personnel, safety, quality, material, digital file, environment, and supervision, supported by the implementation of BIM, cloud computing, and IoT techniques. As illustrated in the case study, the construction period can be saved by 50 days and the cost can be saved by 4.2% after implementing the proposed framework in the project. This study provides a benchmark for related studies on data management and collaborative innovation from various industries and stakeholders. Also, a useful framework has been developed by identifying the key elements, defining the primary functions, and developing the essential layers based on structural consistency.

Prestressed concrete (PC) continuous beam bridges are widely used in transportation infrastructure. However, their construction involves substantial material consumption, raising sustainability concerns amid increasing environmental pressures. This study aims to address the urgent need for resource-efficient bridge design by developing a comprehensive optimization framework that minimizes material usage while ensuring structural safety, durability, and compliance with engineering standards. The proposed methodology integrates a Genetic Algorithm (GA) with a Backpropagation (BP) neural network to optimize both the cross-sectional geometry and the overall alignment of PC continuous beam bridges. The GA is utilized to identify optimal cross-sectional parameters within regulatory constraints, while the BP neural network, trained on extensive design data, refines the bridge bottom height profile to enhance structural performance. The integrated GA-BP framework is validated through a case study of a continuous beam bridge, demonstrating a 94% improvement in design efficiency, a 14% reduction in concrete consumption, and a 34% reduction in prestressed steel usage during the preliminary design stage. These results highlight the framework’s significant potential in advancing sustainable and intelligent bridge design, offering a novel approach to combining artificial intelligence with structural optimization for practical engineering applications.