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When exposed to fire, load-bearing mass timber structures, such as cross-laminated timber (CLT), soften and decompose, which causes significant deformation and weakening, ultimately leading to failure. A combined experimental and modeling analysis is presented to predict the postfire residual compression properties of CLT. Multiple experimental fire exposure and postfire mechanical tests were performed under identical conditions to respectively characterize the internal and external thermal response of the CLT and assess the residual postfire compression properties. An analytical modeling strategy based on the measured through-the-thickness temperature profile during fire exposure is presented to predict the residual postfire compression properties of the CLT. X-ray computed tomography analysis revealed the charring rate to be nonconstant, which is in line with current research findings but not in line with Eurocode design suggestions. The analytical analysis revealed the transverse (cross) lamina to provide minor contribution to the axial compression properties but critically to the thermal protection and structural integrity of longitudinal (load-aligned) laminae during fire exposure. [ABSTRACT FROM AUTHOR]

• A novel data augmentation method is proposed for enhancing DL model performance under occlusion. • A lightweight DL model is formulated for real-time worker and machinery detection. • A real-time proximity detection application was developed by integrating DL and image post-processing techniques. • The developed application manifests significant merits in risk mitigation. Automated recognition of image patterns in surveillance cameras is beneficial for safety monitoring. A representative application is visual proximity detection for accident prevention. However, existing deep learning (DL)-based proximity detection methods are likely to be inaccurate due to congestion, background clutter, and occlusion in construction sites. Another problem is that most of these methods cannot suffice computing capability, network training efficiency, and performance accuracy at the same time. This study first presents a novel context-guided data augmentation method that addresses the performance loss issue due to object occlusion. This method places new instances on images based on contextual image information rather than randomly erasing images to simulate occlusion circumstances. Next, a light-weight and less-costly DL model named YOLOv4-EFNB0 is formulated for multi-class construction resource detection in real-time. Moreover, this study presents a proactive proximity detection application by integrating YOLOv4-EFNB0 with homography transformation. Based on the experiment, YOLOv4-EFNB0 demonstrates a better object detection outcome when trained on the augmented dataset over the Moving Objects in Construction Sites (MOCS) baseline dataset. This study also conducts a feasibility test for the visual proximity detection application. The results indicate the application has a high accuracy rate and a fast speed, namely, 96.76% in 25 Frames Per Second (FPS), which can facilitate the automation of safety monitoring and inspection. [ABSTRACT FROM AUTHOR]

Spatial locations of personnel, equipment, and materials are constantly changing as construction projects progress. The dynamic nature of the construction industry affects workers' performance of identifying hazards. Even though a great deal of effort has been made to improve construction safety, the construction industry still witnesses a high accident rate. In order to complement the existing body of knowledge relating to construction safety, this paper integrates Digital Twin (DT), Deep Learning (DL), and Mixed Reality (MR) technologies into a newly developed real-time visual warning system, which enables construction workers to proactively determine their safety status and avoid accidents. Next, system tests were conducted under three quasi-on-site scenarios, and the feasibility was proven in terms of synchronising construction activities over a large area and visually representing hazard information to its users. These evidenced merits of the development testing scenarios can improve workers' risk assessment accuracy, reinforce workers' safety behaviour, and provide a new perspective for construction safety managers to analyse construction safety status. • The proposed system generates simulated virtual construction sites based on the digital twin concept. • The virtual construction sites integrate BIM models and live video data for hazard identification. • Wearable mixed reality devices can present real-time hazard information to individual workers intuitively. • Three preliminary experiments have demonstrated the feasibility of the proposed system. [ABSTRACT FROM AUTHOR]

High-rise building fires can pose a significant threat to occupants and firefighters. The state-of-the-art technologies such as sensor-based Internet of Things (IoT), Building Information Modelling (BIM), Virtual Reality (VR) and Augmented Reality (AR) may offer great potential to improve building fire safety and rescue efficiency, primarily through ameliorating the level of situational awareness. This study proposes an innovative technology integrated framework for prototyping a proof-of-concept BIM, IoT and AR/VR system based upon the rationale of situational awareness. A pilot test based on a simulated fire scenario is conducted to evaluate the functionality of the framework. The outcomes reveal that the data generated by the system can be leveraged by the firefighting department to quickly locate the whereabouts of the indoor fires, and the VR gamification scenarios can expedite the development of situational awareness for the trainees. The limitations and future works are also discussed at the end of this paper. • Integrating Building Information Modelling and Internet of Things can provide fire monitoring. • Virtual Reality based fire training can increase situational awareness. • Augmented Reality can facilitate indoor pathfinding efficiency. • Firefighters' efficiency can be improved through this framework. [ABSTRACT FROM AUTHOR]

There is a growing interest in exploring the use of wearable Augmented Reality (AR) devices to improve the task performance of construction workers. However, user interaction with AR has not been well understood in the current literature, which may result in poor usability, occupational hazards, and low acceptance. To bridge this gap, this study introduced cognitive ergonomics theory to design and develop an AR application for improving the kinaesthetic performance of construction workers conducting onsite assembly tasks. The methodology of this study is three-fold. First, articles in relation to cognitive ergonomics were reviewed to propose a unique cognitive model that reveals the cognitive mechanisms of construction workers, including human information processing, selective attention, and attention resources. Second, the characteristics of existing AR application functions were synthesised to develop a customised and user-friendly wearable AR application that aligns with the identified cognitive mechanisms. Third, a rebar-tying experiment was conducted to validate the developed AR application. The results indicate that the experimenters instructed by the application can complete the task independently without the need to seek after expert assistance; the application has a potential to foster the skill development of construction workers and enhance their kinaesthetic performance; and the proposed cognitive model and the AR development principles are well aligned from the perspective of cognitive ergonomics, which could promote the uptake of wearable AR in the construction industry. • Cognitive ergonomics theories were introduced to design and develop the AR application. • A cognitive model was proposed to reveal the impact of the application on construction worker performance. • The development assisted the workers in assembling rebars more independently. • The kinaesthetic performance of workers significantly increased as well. [ABSTRACT FROM AUTHOR]