Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries

Abstract

IntroductionOccupational injuries as a workforce’s health problem are very important in large-scale workplaces. Analysis and modeling the health-threatening factors are good ways to promote the workforce’s health and a fundamental step in developing health programs. The purpose of this study was ANN modeling of the severity of occupational injuries to determine the health-threatening factors and to introduce a model to predict the severity of occupational injuries.MethodsThis analytical chain study was conducted in 10 large construction industries during a 10-year period (2005–2014). Nine hundred sixty occupational injuries were analyzed and modeled based on feature weighting by the rough set theory and artificial neural networks (ANNs). Two analytical software programs, i.e., RSES and MATLAB 2014 were used in the study.ResultsThe severity of occupational injuries was calculated as 557.47 ± 397.87 days. The findings of both models showed that the injuries’ severity as a health problem resulted in various factors, including individual, organizational, health and safety (H&S) training, and risk management factors, which could be considered as causal and predictive factors of accident severity rate (ASR).ConclusionThe results indicated that ANNs were a reliable tool that can be used to analyze and model the severity of occupational injuries as one of the important health problems in large-scale workplaces. Additionally, the combination of rough set and ANNs is a good and proper chain approach to modeling the factors that threaten the health of workforces and other H&S problems.

Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries

“…One of the most important indices of occupational injuries is the severity and consequence of occupational injuries (17, 18). The severity of traumatic occupational injuries can be evaluated by using the number of lost working days due to the injuries, some indices such as the accident severity rate (ASR), and the amount of damages to body limbs (19, 20).…”

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

“…A single perceptron, as the simplest feed-forward ANN unit, is only capable of learning a linear bi-class separation problem (Pradhan & Lee, 2010; Mohammadfam et al, 2015; Alizadeh et al, 2015). However, when a number of perceptrons are combined with each other in the layered structure, they emerge as a powerful mechanism with nonlinear separability called a multilayer perceptron, which is the most famous form of ANNs (Fig.…”

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

“…Predictive modeling is the use of data to forecast future events by capturing relationships between explanatory variables and predicted variables from past events, and applying them to predict future outcomes [34]. Artificial neural networks (ANNs) are among the most popular predictive methods in analyzing occupational incidents [1,35,36] predicting causes and severity of injuries [37,38], and determining the underlying factors that influence workplace incidents [39][40][41].…”

“…Despite extensive and ongoing efforts to reduce occupational incidents, the World Health Organization (WHO) has introduced occupational injuries as a public heath epidemic [1]. Occupational injuries are one the most significant health threats in largescale workplaces [1], and are associated with suffering and loss at individual, community, societal and organizational levels [2]. At the organization level, occupational accidents have a significant negative influence on the financial performance of a company [3].…”