Segmentation of Severe Occupational Incidents in Agribusiness Industries Using Latent Class Clustering

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

Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining safety-related data are available, and machine learning algorithms can be used to analyze the data. The use of machine learning techniques can significantly benefit the mining industry. Decision tree, random forest, and artificial neural networks were implemented to analyze the outcomes of mining accidents. These machine learning models were also used to predict days away from work. An accidents dataset provided by the Mine Safety and Health Administration was used to train the models. The models were trained separately on tabular data and narratives. The use of a synthetic data augmentation technique using word embedding was also investigated to tackle the data imbalance problem. Performance of all the models was compared with the performance of the traditional logistic regression model. The results show that models trained on narratives performed better than the models trained on structured/tabular data in predicting the outcome of the accident. The higher predictive power of the models trained on narratives led to the conclusion that the narratives have additional information relevant to the outcome of injury compared to the tabular entries. The models trained on tabular data had a lower mean squared error compared to the models trained on narratives while predicting the days away from work. The results highlight the importance of predictors, like shift start time, accident time, and mining experience in predicting the days away from work. It was found that the F1 score of all the underrepresented classes except one improved after the use of the data augmentation technique. This approach gave greater insight into the factors influencing the outcome of the accident and days away from work.

Characteristic Analysis of Occupational Confined Space Accidents in Taiwan and Its Prevention Strategy

According to the US Bureau of Labor Statistics (BLS), 882 people were killed or injured due to confined space accidents in 2011–2017. Occupational accident statistics published in 2008–2018 by the Taiwan Occupational Safety and Health Administration (OSHA, Taiwan) show that 70 people suffered from disasters and 52 were injured in the 64 accident reports involving confined spaces. In the US, on average, 126 people die each year in accidents related to confined spaces, and in Taiwan, an average of 8 people per year are casualties of accidents involving confined spaces, proving that it is an area of concern that cannot be neglected. When misjudgments occur in confined spaces, not only can people be hurt, but they can even lose their lives, and the risks associated with confined spaces can subsequently result in rescue personnel also being killed or injured. This study was conducted via the systematic causal analysis technique (SCAT), which was proposed and developed by the International Loss Control Institute (ILCI), with the intention of identifying the critical basic causes of the confined space accidents that have occurred over the years in the Taiwan area, in order to propose corresponding improvement strategies. After investigating the statistics in Taiwan, it was determined that hydrogen sulfide was involved in 45% of accidental deaths in confined spaces, followed by 11% involving carbon dioxide, 9% involving carbon monoxide, and 7% involving toluene. Additional analysis of non-standard acts identified “failure of operating procedures” as being involved in 27% of accidents, followed by 25% involving “improper personal protective equipment” and 23% involving “incorrect position”. The analysis of non-standard conditions revealed that “dangerous workplace” was involved in 39% of accidents, “improper protective measures” in 30%, and “inadequate ventilation” in 27%. In accordance with our analysis results, it could be suggested that hazard prevention strategies for confined spaces, in addition to encouraging avoidance of non-standard acts by personnel, should also strive to improve these non-standard conditions. Otherwise, if not prevented deliberately and in a fundamental, relevant accidents will remain inevitable.

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

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.