Trends of occupational fatal and nonfatal injuries in electrical and mechanical specialty contracting sectors : necessity for a learning investigation system

The specialty electrical and mechanical contracting sectors provide crucial services and perform functions that are vital to the products delivered by the construction industry. The main purpose of this study is to investigate the causes of fatal and nonfatal injuries in these specialty construction sectors over time as well as their effects on the level of safety performance in the industry. Accordingly, the most prevalent causes of fatal and nonfatal incidents in the mechanical and electrical sectors are investigated and presented as a longitudinal study from 2005 to 2015. The trends in occupational injuries in these sectors over this period of time are also compared with the trends reported in previous studies. The results from this study show that the direct causes of fatal and nonfatal injuries in the electrical and mechanical sectors differ from those found in the construction industry in general. In addition, the electrical and mechanical construction industry trends identified in this study are similar to previously reported trends. The similarities between the current findings and those of previous studies highlight real shortcomings in the safety management approaches within the construction industry. Based on the findings of this study, a learning investigation system has been recommended to improve safety performance among electrical and mechanical specialty contractors

Bayesian Network Models for Evaluating the Impact of Safety Measures Compliance on Reducing Accidents in the Construction Industry

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance

Bayesian Network Models for Evaluating the Impact of Safety Measures Compliance on Reducing Accidents in the Construction Industry

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance