Building Information Modeling (BIM) has unique features that improve safety management in construction by visually identifying potential risks. Integrating BIM with a real-time game engine is a cutting-edge idea for more effective safety management. This study aims to conduct two case studies by integrating BIM data with game engines from two aspects: 1) Construction Safety Training and 2) Pre-construction Safety Management. A framework that covers techniques for extraction of safety ideas, managing the game engine, and character modeling tools and resources is used to carry out the case studies. In the first case study, a construction site was created by Revit, and a real-life scaffolding failure accident was simulated by Unity to warn workers to prevent similar future events. The second case study was conducted on the procedure of evacuation modeling in an emergency, integrating a BIM model and Unity following distinct pathways. This evacuation modeling can be used as a training platform for the occupants to acquaint themselves with the inside facility, show directions of the shortest evacuation path from specific points, and provide necessary information on emergency equipment. Finally, the study explains how the integration of the BIM model and game engine applications can be applied for effective, straightforward, and helpful safety management with the most efficient BIM data transition.

Effective pavement management systems are essential for accurately predicting pavement conditions and efficiently planning and scheduling maintenance, rehabilitation, and reconstruction activities. Significant efforts are dedicated to developing accurate pavement condition prediction models using machine learning (ML) at the state level. Conversely, insufficient investment, poor quality, and large variations in local roads data have resulted in less attention to modeling local pavement conditions. This study develops eight Bayesian-optimized single-estimator and ensemble ML classification models to predict local pavement roughness. Moreover, the classification granularity of pavement condition was investigated to assess its impact on the predictive power of various ML models. The results reveal that ML classification models with fewer classes exhibit higher accuracy and more stability in precision over recall values, in contrast to models with larger number of classes. The ensemble ML models surpass their single-estimator counterparts, with the category boosting algorithm demonstrating the highest performance, achieving testing accuracies of 0.77 and 0.65 for the three-level and five-level classifications, respectively. Hence, it is recommended to employ ensemble ML algorithms and a smaller number of classes to develop reliable, accurate, and stable predictive models for local roads with imbalanced condition data. This research helps transportation agencies improve their pavement condition prediction, thereby optimizing pavement management and resource allocation.

Buildings consume approximately 43% of their electricity for space heating and cooling, emphasizing the need for energy-efficient solutions. Among the strategies to reduce this demand, phase change materials (PCM) have been recognized for their potential to enhance thermal performance. While PCM has been extensively studied in building envelopes, its integration with cross-laminated timber (CLT) remains unexplored. Additionally, the optimal placement of PCM within wall assemblies lacks consensus, as previous studies have reported inconsistent findings. This study addresses these research gaps by investigating the performance of PCM-integrated CLT (PCM-CLT) wall systems across 17 climate zones in the United States. Using EnergyPlus simulation, five wall configurations were analyzed, including three PCM-CLT configurations with PCM positioned at different locations within the assembly. The results demonstrate that the PCM-CLT system significantly enhances energy efficiency, achieving cooling energy savings of up to 72.48% and heating energy savings of up to 96.94% in certain locations. Moreover, the findings reveal that placing PCM on the interior side of CLT walls consistently outperforms other configurations across all climate zones. Furthermore, PCM-CLT walls help reduce peak energy loads, alleviating stress on power grids. This research contributes to enhancing building energy performance through PCM-CLT integration, providing valuable insights for both retrofitting and new construction, and advancing sustainable building design. Doi: 10.28991/CEJ-2025-011-05-05 Full Text: PDF

Purpose: Despite ongoing safety efforts, construction sites remain some of the most hazardous workplaces. This study introduces an innovative occupational safety and health administration (OSHA) training approach by creating a realistic virtual construction environment using unmanned aerial vehicle (UAV) imagery and a game engine. Integrating OSHA regulations makes safety instructions more effective than traditional training. Design/methodology/approach: The research employs UAV-derived photogrammetry to generate a 3D textured mesh model of an active construction site. This model is integrated into a game engine to develop an interactive, first-person simulation where users explore the site and receive safety instructions at hazard points. Validation was conducted through questionnaire surveys of 13 construction professionals and 25 undergraduate students. Findings: The study shows that interactive game-based learning significantly improves trainees’ ability to identify and understand site-specific hazards. Survey responses from students and construction professionals indicated that the game is more effective in teaching safety protocols than traditional OSHA 30 training. Practical implications: The study demonstrates that integrating UAV photogrammetry with game engines enhances construction safety training by improving hazard recognition and knowledge retention. Survey results show higher effectiveness than traditional training. This approach enables realistic, site-specific safety instruction, supporting OSHA compliance and reducing accidents through interactive, immersive learning. Originality/value: This research enhances safety training by integrating high-fidelity 3D models from UAV photogrammetry with a game engine to develop an interactive learning platform. Unlike traditional methods with generic simulations, this approach reflects the specific conditions and hazards of active construction sites, offering tailored safety instructions.