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

Abstract

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

“…In most previous studies, simply predicting one dependent variable with ML or analyzing the relationship between one dependent variable and the remaining independent variables through chi-square test [8,37,48]. Moreover, current studies of causal inference have been performed by a complex algorithm [49,50], so reasonable inference results have not effectively been applied to qualitative and subjective accident data in construction field.…”

“…In order to apply the accident analysis results to the safety measures at the construction site, it is necessary to pay attention to the correlation between variables contributing to the accident, rather than simply increasing the prediction accuracy [37,48].…”

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

“…The reason is the compatibility of the ReLu networks with any sampling algorithms, since the training process of ReLu is not dependent on the distribution of the data [39]. The number of hidden layers, the type of activation function, the number of nodes, and the learning rate of the algorithm should be considered while developing ANNs [40]. In this paper, the structure included for the artificial neural networks one input layer, nine hidden layers, and one output layer.…”

Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

“…Davoudi Kakhki and colleagues used cluster modeling to identify high-risk groups of occupational incidents with severe injuries [ 17 ]. Previous studies on occupational safety outcomes have shown the superior performance of machine learning techniques, such as classification trees, support vector machines, and artificial neural networks (ANNs) [ 18 , 19 ].…”

“…Due to the advantages of machine learning techniques, they have been successfully used in many fields, such as e-commerce, healthcare, and banking [ 20 , 21 , 22 ]. Studies on occupational safety studies have also used machine learning techniques to analyze and generate actionable insights [ 12 , 15 , 18 , 23 ]. Similarly, the use of machine learning techniques can significantly benefit the mining industry.…”