One in every five occupational deaths occurs in the construction sector. A proactive approach for improving on-site safety is identifying and analyzing accident precursors, such as near-misses, that provide early warnings of accidents. Despite the importance of near-misses, they are frequently left unreported and unrecorded in the construction sector. The adoption of modern technologies can prevent accidents by automated data collection and analysis. This study aims to develop near-miss detection metrics to facilitate the automated detection of near-misses through sensors. The study adopted a mixed method approach including both qualitative and quantitative approaches. First, a quantifiable definition of near-misses was developed from the literature. Hazards, accidents, and the causes of accidents were identified. Through empirical and statistical analyses of accidents from the OSHA repository, combinations of unsafe acts and conditions responsible for a near-miss were identified. The identified factors were analyzed using a frequency analysis, correlation, and a lambda analysis. The results revealed twelve significant near-misses, such as A1—approach to restricted areas and C2—unguarded floor/roof openings, A5—equipment and tool inspection was incomplete and C8—unsafely positioned ladders and scaffolds, A2—no or improper use of PPE and C2—unguarded floor or roof openings, etc. Lastly, measurable data required by sensors for autonomous detection of near-misses were determined. The developed metric set the basis for automating near-miss reporting and documentation using modern sensing technology to improve construction safety. This study contributes to improving construction safety by addressing the underreporting of near-miss events. Overall, the developed metrics lay the groundwork for enhancing construction safety through automated near-miss reporting and documentation. Furthermore, it helps for the establishment of safety management schemes in the construction industry, specifically in identifying near-misses. This research offers valuable insight into developing guidelines for safety managers to improve near-miss reporting and detection on construction sites. In sum, the findings can be valuable for other industries also looking to establish or assess their own safety management systems.

Students are rapidly losing interest in STEM education due to the minimal incorporation of media into learning and the complexity of teaching methods, which ultimately results in a lack of motivation. However, despite these challenges, incorporating active learning methods in STEM can ultimately reignite students’ interest in STEM education. This paper reports a case study presenting the effects of simulation games on enhancing the learning experiences of civil engineering students through an intervention method. It develops a framework for enhancing learning through game-based learning. Intervention included the following two groups: a control group exposed to conventional teaching methods and an experimental group introduced to the simulation game. Following the learning sessions, a survey was conducted to gauge students’ perceptions about the proposed framework. Results indicated a strong positive response from students toward the game-based learning approach. They expressed satisfaction with its effectiveness in improving their understanding. Independent t-tests found that the mean score (4.13) for entertainment via the game exceeded that of traditional teaching methods (3.72). Furthermore, 85% of students acknowledged the game’s utility in reinforcing civil engineering concepts, compared to 67% for traditional methods. Many students suggested expanding the game to cover a wider range of topics in future versions to enable a more comprehensive learning experience.

Selecting the right maintenance contractor is crucial for efficient operation and project success. Traditionally, this selection has been cost-driven, but the ever-growing complexity of projects has led to a shift towards best-value selection. The best value selection criteria evaluate the contractors based on factors like experience and past performance, along with the proposed cost. However, this approach lacks substantiated knowledge of these factors and often includes factors that cannot be validated at the time of procurement. This paper proposes a framework that applies the Analytical Hierarchy Process (AHP) to the maintenance contractor selection process. A detailed literature review was carried out to identify factors involved in maintenance selection. Data were collected from experts through a questionnaire developed based on the identified factors, facilitating AHP implementation. Substantiation strategies were identified using expert judgments. Our findings reveal that past performance criteria hold the maximum weight in the selection process. The proposed framework offers a more comprehensive approach for selecting maintenance contractors, ensuring both value and efficiency.

Surveillance cameras are becoming an integral part of the buildings due to their ability to ensure security, as well as to promote safety and overall well-being. Finding the optimal camera configuration remains a challenge, as current practices depend heavily on professional experience and subjective judgment. These practices have several limitations which can adversely impact camera coverage. Building information modelling (BIM) usage is growing in the industry due to its ability to generate accurate spatial data. Therefore, this study proposes a BIM-based framework to optimize camera placement process using optimization algorithms (OAs). Firstly, the framework extracts spatial data of the target area from the BIM based on user defined requirements. Secondly, it adopts an optimization algorithm to find the optimal camera positions for the target area based on the user requirement. The selection of optimization algorithm was made following a comparative evaluation between Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Lastly, it helps visualize the optimized results within the building using BIM. The framework was validated on a hospital building, revealing 27% increase in coverage, a significant reduction in overlap, and a lower camera requirement compared to experience-based camera configuration.