Synthesizing Personalized Construction Safety Training Scenarios for VR Training.

Construction industry has the largest number of preventable fatal injuries, providing effective safety training practices can play a significant role in reducing the number of fatalities. Building on recent advancements in virtual reality-based training, we devised a novel approach to synthesize construction safety training scenarios to train users on how to proficiently inspect the potential hazards on construction sites in virtual reality. Given the training specifications such as individual training preferences and target training time, we synthesize personalized VR training scenarios through an optimization approach. We validated our approach by conducting user studies where users went through our personalized guidance VR training, free exploration VR training, or slides training. Results suggest that personalized guidance VR training approach can more effectively improve users' construction hazard inspection skills.

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007-2013.

This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration's fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings, new construction, and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type, source of injury, cause of injury, injured part of body, nature of injury, and eventtype. In other words, the statistical probability of a fatal accident-given an accident occurrence-changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is parts and materials; followed by whether the source of injury is tools, instruments, and equipment. In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is parts and materials and whether the source of injury is tools, instruments, and equipment are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.

Examining the Relationship between Mindfulness, Personality, and National Culture for Construction Safety.

The construction industry still leads the world as one of the sectors with the most work-related injuries and worker fatalities. Considering that one of the barriers to improving construction safety is its stressful working environment, which increases risk of inattentiveness among construction workers, safety managers seek practices to measure and enhance worker focus and reduce stress, such as mindfulness. Considering the important role of mindfulness in curbing frequency and severity of incidents, researchers are interested in understanding the relationship between mindfulness and other common, more static human characteristics. As a result, this study examines the relationship between mindfulness and such variables as personality and national culture in the context of construction safety. Collecting data from 155 participants, this study used elastic net regression to examine the influence of independent (i.e., personality and national culture) variables on the dependent (i.e., mindfulness) variable. To validate the results of the regression, 10-fold cross-validation was conducted. The results reveal that certain personality traits (e.g., conscientiousness, neuroticism, and agreeableness) and national cultural dimensions (e.g., uncertainty avoidance, individualism, and collectivism) can be used as predictors of mindfulness for individuals. Since mindfulness has shown to increase safety and work performance, safety managers can utilize these variables to identify at-risk workers so that additional safety training can be provided to enhance work performance and improve safety outcomes. The results of this study will inform future work into translating personal and mindfulness characteristics into factors that predict specific elements of unsafe human behaviors.

Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors.

The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a "vehicle" than when it is a "tool, instrument, or equipment". After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in "fencing, installing lights, signs, etc." tasks in "alteration and rehabilitation" projects where the source of injury was "parts and materials". The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.

PARS: Using Augmented 360-Degree Panoramas of Reality for Construction Safety Training.

Improving the hazard-identification skills of construction workers is a vital step towards preventing accidents in the increasingly complex working conditions of construction jobsites. Training the construction workforce to recognize hazards therefore plays a central role in preparing workers to actively understand safety-related risks and make assertive safety decisions. Considering the inadequacies of traditional safety-training methods (e.g., passive lectures, videos, demonstrations), researchers have employed advanced visualization techniques such as virtual reality technologies to enable users to actively improve their hazard-identification skills in a safe and controlled environment. However, current virtual reality techniques sacrifice realism and demand high computational costs to reproduce real environments. Augmented 360-degree panoramas of reality offers an innovative alternative that creates low-cost, simple-to-capture, true-to-reality representations of the actual construction jobsite within which trainees may practice identifying hazards. This proof-of-concept study developed and evaluated a platform using augmented 360-degree panoramas of reality (PARS) for safety-training applications to enhance trainees' hazard-identification skills for four types of sample hazards. Thirty subjects participated in a usability test that evaluated the PARS training platform and its augmented 360-degree images captured from real construction jobsites. The usability reviews demonstrate that the trainees found the platform and augmentations advantageously to learning hazard identification. The results of this study will foreseeably help researchers in developing engaging training platforms to improve the hazard-identification skills of workers.