Visualizing What’s Missing: Using Deep Learning and Bow-Tie Diagrams to Identify and Visualize Missing Leading Indicators in Industrial Construction and Reducing Serious Injuries and Fatalities in Industrial Construction: Application of Machine Learning to Analyze Emotional Intelligence and Psychosocial Factors
Creators: Charuvil Elizabeth, Rose Marie
Publisher: name: University of Alberta Library
Year: 2024

<p>Occupational health and safety within the construction industry, especially in industrial construction (construction of factories, power plants, industrial units, and other large-scale facilities) is a top concern because of the dynamic work environment. Hazards on-site continually change since work is performed at multiple sites with a mix of different contractors and workers and managing such multi-disciplinary teams becomes challenging. Therefore, managing a safe environment necessitates further analysis to proactively measure and enhance safety performance.While incident reports are frequently used to enhance safety performance, safety inspections are not often analyzed to identify any unsafe patterns or used as a measure to enhance safety performance. The safety performance is typically only operationalized as either the presence or absence of an indicator rather than quality assurance. Furthermore, there has been little interconnection with industrial and organizational psychological variables like interpersonal skills in the construction industry. To address these challenges, this thesis discusses two aspects –(1) identifying the missing leading indicators in safety inspections that lead to incidents, and (2) investigating incidents through the lens of interpersonal skills such as emotional intelligence (EI) and psychosocial factors (PS).The first study focused on linking incidents with safety inspection records to identify the missing leading indicators not captured in inspections. Natural language processing (NLP) and machine learning (ML) techniques were utilized to analyze 731 incidents and 10,625 inspection reports of a construction project from 2015 to 2018 in Alberta, Canada. Each incident was linked to its respective past one-month inspections, and sentence bidirectional encoder representations from transformers (SBERT) were used to generate embeddings and compute the similarity between incident and inspection descriptions. Root cause analysis was conducted on selected incidents with low similarity with inspection descriptions using a customized human and organizational framework. Bow-tie and Sankey&#39;s diagrams were used to illustrate and visualize what leading indicators miss capturing hazards during inspections that lead to incidents. In addition, n-gram models and co-occurrence networks were used for validation to extract meaningful information and identify patterns from incident and inspection reports.The second study of the research focused on analyzing EI and PS factors using incident reports of an industrial construction company. For the first section of the study, a qualitative analysis using NVivo software was employed to classify 1000 incidents from 2018-2020 into EI factors. Since PS factors were not observed in the dataset, the second section of the study looked to identify PS factors using a quantitative approach (machine learning) to analyze 45,603 incidents from 2014-2023. In addition, co-occurrence networks were used to investigate any patterns in incident reports.The results of both these studies provide some pivotal contributions to enhancing safety performance and reducing incidents at human, organizational, and system-level. The NLP and ML models developed by linking and comparing the similarities of incident and inspection descriptions, provide new perspectives on identifying the blind spots in safety inspections to enhance the safety management system. Assessment of EI and PS factors using ML techniques highlights the intricate interplay between organizational, technical, and human factors that shape safety outcomes. In addition, these studies glean some theoretical and empirical insights into how organizations fail and succeed to inform stakeholders about the strategies needed to foster safe work environment.</p>