This study investigates the perceptions of artificial intelligence (AI) among undergraduate construction management students who are poised to become future leaders in the construction industry. As the industry increasingly adopts AI technologies to enhance project planning, design, site management, and safety, understanding students’ attitudes toward these innovations becomes crucial. This research employs a mixed-methods approach, combining quantitative survey data with qualitative data to capture the students’ insights on AI’s potential applications, benefits, and challenges within the construction sector. Findings indicate that while students recognize AI’s transformative potential to improve efficiency and safety in construction processes, they also express concerns regarding ethical implications, job displacement, and the necessity of new skills to effectively integrate AI into their future careers. Additionally, this study reveals a significant gap in students’ knowledge about AI technologies and their applications in the construction industry. These insights underscore the importance of incorporating AI-focused curricula in construction management programs to better prepare students for the evolving landscape of the industry. Ultimately, this research contributes to the understanding of how the next generation of construction professionals perceives AI and highlights the need for educational institutions to adapt their programs to equip students with the competencies required for a technology-driven future.

Deep learning-based semantic segmentation of point clouds is a vital tool for efficiently generating accurate as-built Building Information Models (BIM) by automating the identification and classification of building components. While traditional deep learning models have primarily focused on segmenting visible structural elements such as columns, walls, doors, windows, and furniture, the growing popularity of open ceiling designs presents unique challenges. Open ceiling environments expose Mechanical, Electrical, and Plumbing (MEP) components, including pipes, ducts, and electrical conduits, which are often characterized by overlapping geometries, visual similarities, and intricate spatial arrangements. These complexities raise questions about whether existing deep learning-based semantic segmentation models can effectively classify MEP components in such challenging environments. This study addresses this gap by evaluating the performance of RandLA-Net, a lightweight deep learning-based semantic segmentation model, for accurately classifying MEP components in open ceiling environments. Using LiDAR technology, 3D point cloud data was collected within a building featuring an open ceiling design, where components were manually annotated to train and test the model. The performance of the model was evaluated through metrics such as overall accuracy (OA) and mean Intersection over Union (mIoU). The results showed an OA of 90.86% and a mean IoU of 83.1%, indicating that the existing RandLA-Net model is capable of effectively segmenting MEP components alongside structural elements, even within the challenging context of open ceiling designs. The findings suggest that RandLA-Net has strong potential for addressing the complexities of MEP component classification in open ceiling environments. Furthermore, the study highlights the opportunities for enhancing semantic segmentation methods to achieve even greater precision in as-built BIM generation, supporting improved maintenance planning and operational efficiency in complex architectural settings.

Ground subsidence in urban areas often reflects hidden failures within water and sewer infrastructure, amplified by hydrologic and seasonal conditions. This study analyzes 303 documented subsidence incidents in Gyeonggi Province, South Korea, from 2018 to 2024, focusing on infrastructure-related causes and their spatial and seasonal patterns. Incident records were reviewed to identify root causes, geographic distribution, seasonal trends, and impacts, including human injury and vehicle damage. Descriptive analysis showed that sewer pipe damage (39.3%) was the leading cause, followed by poor compaction or backfilling (22.8%) and excavation-related damage (14.2%). Subsidence linked to sewer systems occurred disproportionately during the summer monsoon, highlighting interactions between rainfall, pipe deterioration, and soil erosion. Statistical analysis using the Extended Fisher’s Exact Test revealed significant associations between subsidence causes and seasonality, vehicle damage, and regional location, but no significant link with human injury. Defective pipe construction contributed to elevated regional vulnerability, particularly in eastern municipalities, while excavation-related incidents were more common in spring. These results underscore the need for seasonally adaptive inspections, targeted rehabilitation of aging water and sewer networks, and region-specific asset management. By connecting subsurface failures with hydrologic conditions and infrastructure performance, this study offers data-driven insights to enhance proactive water infrastructure management and urban resilience.