Understanding the causal relationships between safety management system (SMS) factors and accident precursors helps construction organizations identify which factors require improvement upon observing an accident precursor. Previous research has not clearly distinguished between SMS factors and accident precursors. This background examines the relationships between SMS factors and accident precursors using empirical data. Specifically, five structural equation models (SEMs) were developed to map causal paths between SMS factors and accident precursors. Each of the SEMs helps identify what specific SMS factors would have a significant influence on the occurrence of a particular type of accident precursor. These models can thus help describe what specific SMS factors would need to be improved when a certain type of accident precursor appears on site. The SEM results show in particular that the occurrence of accident precursors can be attributed largely to adverse project conditions such as project schedule pressure, reworks, and change orders. Construction organizations may capitalize on these findings by prioritizing safety management resources to address specific observed accident precursors in a more informed and targeted manner.

Constructive alignment is a learning design approach that emphasizes the direct alignment of the intended learning outcomes, instructional strategies, learning activities, and assessment methods to ensure students are engaged in a meaningful learning experience. This pedagogical approach provides clarity and coherence, aiding students in understanding the connection of their learning activities and assessments with the overall course objectives. This paper explores the use of constructive alignment principles in designing a graduate-level Introduction to Project Management course by leveraging Large Language Models (LLMs), specifically ChatGPT. We introduce an innovative framework that embodies an iterative process to define the course learning outcomes, learning activities and assessments, and lecture content. We show that the implemented framework in ChatGPT was adept at autonomously establishing the course's learning outcomes, delineating assessments with their respective weights, mapping learning outcomes to each assessment method, and formulating a plan for learning activities and the course's schedule. While the framework can significantly reduce the time instructors spend on initial course planning, the results demonstrate that ChatGPT often lacks the specificity and contextual awareness necessary for effective implementation in diverse classroom settings. Therefore, the role of the instructor remains crucial in customizing and finalizing the course structure. The implications of this research are vast, providing insights for educators and curriculum designers looking to infuse LLMs systems into course development without compromising effective pedagogical practices.

The performance of a safety management system (SMS) is influenced by a large number of internal and external factors. Loss of control of these factors can create unsafe conditions, which can eventually lead to worksite accidents. Identification and prioritization of these factors is crucial for optimal SMS performance. However, because of the complex and dynamic nature of accident occurrence, identification and control of critical SMS factors and accident precursors remains difficult in practice. This paper proposes the use of a risk-rating approach for understanding the complexity between SMS factors and accident precursors from a holistic perspective. This approach allows for not only the identification of high-priority SMS factors and accident precursors, but also the evaluation of associations between these groups. To demonstrate the effectiveness of the proposed approach, risk ratings for a comprehensive set of SMS factors and accident precursors were collected from 96 construction professionals in Alberta, Canada. The results demonstrated that contract schedule, skilled worker availability, and change orders were the highest-priority SMS factors in the region. Results also found the highest-priority accident precursors to be workers’ failure to identify hazards, congestion, and workers’ neglect of hazards. Associations between factors and precursors were also examined and were found to be present between several SMS factors and accident precursors. Overall, this method allows practitioners to identify high-priority accident precursors and to determine which SMS factors are associated with these precursors. These findings can assist practitioners in determining which SMS factors should be controlled if certain accident precursors are observed and, conversely, which accident precursors are most likely to occur if critical SMS factors are present on site.

Although methods for assessing and simulating the influence of safety-related measures on safety performance have been proposed, practical applications remain limited. Data required by these methods are dispersed across departments, necessitating the development or redesign of data warehouses. This research proposes a simulation-based analytics approach to enhance safety management system (SMS) decision making using distributed simulation to overcome limitations associated with previous approaches. This distributed simulation approach is used to (1) integrate historical data without modifying data-warehouse structures (i.e., data fusion component), (2) link data to an artificial neural network–based analysis component for determining the influence of safety-related measures on incident levels, (3) connect data and analysis components to existing simulation components, and (4) combine the outputs, resulting in a comprehensive safety performance evaluation system to examine incident levels. Results demonstrate that this approach successfully fuses and integrates data from several sources with analysis and simulation components in a cost-, labor-, and time-efficient manner. A distributed simulation–based analytics approach represents a considerable opportunity for industrial construction companies to more effectively use historical data, analysis tools, and simulation models.

Although the use of safety-related measures (SRM) has been advocated in the literature, construction companies often rely on reactive indicators instead of SRM to evaluate safety performance. A challenge with using SRM in practice arises when faced with the uniqueness of construction organizations. Distinctive practices and cultures affect the availability and quantity of data, which in turn limits the reliability of conventional SRM-based evaluation methods. This study proposes a case-based reasoning (CBR) approach that makes efficient use of existing SRM available within an organization to reliably evaluate safety performance. The model was validated using statistical tests to compare model output with actual performance. To test model applicability, we conducted a case study: based on information available in an industrial construction organization, including schedule performance and payroll data, 27 measures were identified. The results demonstrated that the proposed approach is able to evaluate safety performance trends in consideration of unique organizational characteristics using SRM. We expect that the model approach and results will support managers in tailoring safety goals and programs to particular contexts.