Despite substantial efforts to improve construction safety training, the accident rate of migrant workers is still high. One of the primary factors contributing to the inefficacy of training includes information delivery gaps during training sessions (knowledge-transfer). In addition, there is insufficient evidence that these training programmes alone are effective enough to enable migrant workers to transfer their skills to the jobsite (training-transfer). This research attempts to identify and evaluate additional interventions to improve the transfer of acquired knowledge to the workplace. For this purpose, this study presents the first known experimental effort to assess the effect of interventions on migrant work groups in a multinational construction project in Qatar. Data analysis reveals that the adoption of training programmes with the inclusion of interventions significantly improves training-transfer. Construction safety experts can leverage the findings of this study to enhance training-transfer by increasing workers' safety performance and hazard identification ability.

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.

Construction safety education plays a crucial role in improving the safety performance in the construction industry. Many research works have successfully adopted computerized three-dimensional model-based virtual reality (3D-VR) to provide students with adequate safety knowledge and skills before they enter construction sites. Despite the advantages of improving learning outcomes, 3D-VR has limitations not only in reflecting real-world visibility but also in consuming significant energy and requiring strict user-device compatibility. Therefore, this research methodology was initiated with a thorough investigation of VR application in construction safety education. On the basis of a literature review, the study subsequently analyzes the energy-consumption problems of conventional VR systems. Initial findings motivate the development of an energy-efficient learning system (the interactive constructive safety education (eCSE)) using Web-based panoramic virtual photoreality technology for interactive construction safety education. The eCSE system provides three key interactive modules, namely, lesson delivery (LD), practical experience (PE), and knowledge assessment (KA), for use in mobile devices. The trial system has been developed and validated through scenarios derived from real construction sites. The preliminary evaluation reveals that the eCSE system not only overcomes the 3D-VR limitations in terms of energy efficiency, user device adaptability, and easy implementation, but also improves learning usability.

Hazard investigation education plays a crucial role in equipping students with adequate knowledge and skills to avoid or eliminate construction hazards at workplaces. With the emergence of various visualization technologies, virtual photoreality as well as 3D virtual reality have been adopted and proved advantageous to various educational disciplines. Despite the significant benefits of providing an engaging and immersive learning environment to promote construction education, recent research has also pointed out that virtual photoreality lacks a 3D object anatomization tools to support learning, while 3D-virtual reality cannot provide a real-world environment. In recent years, research efforts have studied virtual reality applications separately, and there is a lack of research integrating these technologies to overcome limitations and maximize advantages for enhancing learning outcomes. In this regard, the paper develops a construction hazard investigation system leveraging object anatomization on an Interactive Augmented Photoreality platform (iAPR). The proposed iAPR system integrates virtual photoreality with 3D-virtual reality. The iAPR consists of three key learning modules, namely Hazard Understanding Module (HUM), Hazard Recognition Module (HRM), and Safety Performance Module (SPM), which adopt the revised Bloom's taxonomy theory. A prototype is developed and evaluated objectively through interactive system trials with educators, construction professionals, and learners. The findings demonstrate that the iAPR platform has significant pedagogic methods to improve learner's construction hazard investigation knowledge and skills, which improve safety performance.

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