Search for: All records

Virtual site visits are increasingly becoming a viable educational tool for educators to supplement or replace traditional visits when these are challenged by logistical issues, inaccessibility, or safety hazards. Recent research has explored the integration of theory-based learning strategies, such as collaborative problem-solving and multimedia learning, in online construction site visits to support construction students’ collaborative skill development and learning effectiveness. However, there remains a lack of understanding of how to guide students systematically from conceptual knowledge to more complex, hands-on, or procedural knowledge, which often leads to a fragmented learning experience in current online site visit designs. This study aims to integrate active learning approaches (i.e., systematic learning progression) into online site visits to facilitate students’ development of situated knowledge. In this project, a collaborative online site visit focused on building mechanical systems was created, where students worked in pairs to achieve four specific learning objectives, progressing from conceptual to procedural knowledge regarding building mechanical systems. The findings provide insights into the integration of systematic learning progression within virtual collaborative spaces for online site visits and demonstrate the effectiveness of such site visits in supporting students’ situated knowledge. 

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management.