Neural Network Application for Risk Factors Estimation in Manufacturing Accidents (original) (raw)
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The safety and protection of people, equipment and the environment is a serious concern in the Engineering industries. Many industries have recognized the advantages of Safe Work Environments and are progressively adopting Safety Management System to prevent hazardous events, avoid production & manpower losses and other fallouts associated with industrial accidents. Safety Management is an organizational function which ensures that all the safety risks have been identified, assessed and satisfactorily mitigated. Thus the safety of the workers is the most important factor in dealing with the industrial safety. The present work utilizes artificial neural network (ANN) modelling technique for modelling. The proposed paper work envisages minimizing industrial accidents by identifying the various factors responsible for industrial accidents and developing the approximate model to correlate the causes of accidents with the severity and the man days lost.
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Taking continuous and efficient precautions to possible accidents that may occur in all industrial organizations is the main requirement for the health and safety of employees. For this reason, Occupational Health and Safety Management Systems are practiced to improve systematically every type of hazard, risk, and risk assessment process. Organizations are expected to use quantitative risk assessment methodologies to minimize possible accidents at workplaces. In our study, Artificial Neural Networks (ANN) approach was designed to support occupational health and safety management systems as a risk assessment by a quantitative method. Within the scope of the study, the number of incapacities to work forecasting models was developed for the employees who have compulsory insurance by using artificial neural networks. The number of employees with compulsory insurance from 1970-2018, the number of workplaces, work accidents, and the number of people who died consequently because of work accidents, and permanent disability data were the input data for this study. Multi-Layer Perceptron (MLP), which is one of the ANN topologies, is developed, and the performance was tested according to its (Mean Squared Error) MSE and Root Mean Squared Error (RMSE) values. It has been observed that the MLP network produces better results with the best MSE value as 0.2250 and the best RMSE value as 0.1125.
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Artificial neural networks (ANN) are a powerful tool in the decision-making process, especially in solving the complex problems with a large number of input data. The possibility to predict the work-related injuries in the underground coal mines, based on application of the neural networks, is analyzed in this work. the input data for the network were obtained based on a survey of 1300 respondents. After analyzing the input data influence on the network output, 14 most influential inputs were selected, with help of which the network correctly predicted whether the worker would suffer the work-related injury or not, with 80% precision. The two models were developed, based on the multilayer perceptron (MLP) and radial basis function (RBF) networks. The two models’ results were compared to each other. The sensitivity analysis was used to select the most influential parameters, like mine, age of miners, as well as their work experience. The parameters were further analyzed by use of the...
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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 ...
Traffic accident risk classification using neural networks
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The article deals with the current issue of traffic accident risk classification in urban area. In connection with the increase in traffic in the Czech Republic, a higher probability of risks of traffic excesses can be expected in the future. If there is a traffic excess in the city, the aim is to propose a meaningful traffic management solution to minimize the social losses. The main needs are the early identification and classification of the cause of the traffic excess, finding a suitable alternative solution, quick application of that solution, and the rapid ability to resume operations in the area of congestion. Traffic prediction is one of the tools for the early identification of traffic excess. The article describes extensive research focused on the classification and prediction of the output variable of accident risk based on own programmed neural networks. The research outputs will be subsequently used for the creation of a traffic application for a selected urban area in ...
Prediction of accident severity using artificial neural networks
In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity in urban highways. This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural networks models. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the p
A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces
The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73). The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance.
Safety Science, 2017
This paper describes a procedure to evaluate the risk of occupational accidents combining SKM (SOM & K-Means) and FAP (Fuzzy Logic approach) to explicitly take into account the impact that harsh productive environments can have on the occupational accident dynamics. The procedure proposed consists of an advanced approach based on two evaluation steps: (1) the investigation of occupational accidents dynamics in a productive sector through the SKM approach, based on neural networks; (2) the occupational accidents risk assessment for a specific plant through the Fuzzy Logic approach, exploiting the knowledge inherent in the accident analysis of an industrial sector to the benefit of the risk assessment in a specific work environment. The application of SKM and FAP approach respectively to the metallurgical sector and to an Italian industrial plant allowed the validation of the proposed procedures. Taking into account the accidents occurred in the Piedmont metallurgical sector from 2006 to 2013, the risk assessment for an Italian steel-manufacturing company was carried on, highlighting how a critical work environment can affect the accident dynamics, and how to correctly take into account their impact during the risk assessment could bring to an effective operational safety improvement.
ROAD TRAFFIC ACCIDENTS ANALYSIS USING NEURAL NETWORK APPLICATION
Road traffic accidents are among the top leading causes of deaths and injuries of various levels in the world. Ethiopia is taking highest rate of such accidents resulting in fatalities and various levels of injuries. Oromiya Region, the East-South of Ethiopia, takes the biggest share of the risk having higher number of vehicles and traffic resulted from the country port being in this line i.e Addis Ababa to Debreziet, Modjo, Nathert and Hawassa lines and the cost of these fatalities and injuries has a great impact on the socio-economic development of a society. This article is focused on developing neural network analysis in identifying the major causes to handle road traffic accident analysis for Ormiya region main roads from Addis Ababa, Ethiopia. The study focused on injury severity levels resulting from an accident using real data obtained from the different towns of Oromiya zone Offices.
Accident Modeling in Small-Scale Construction Projects Based on Artificial Neural Networks
Journal of Human Environment and Health Promotion, 2019
"Background: Several factors contribute to accidents in small-scale construction projects (SSCPs). The present study aimed to assess the influential factors in SSCP accidents and introduce a model to predict their frequency. Methods: In total, 38 SSCPs were within the scope of this investigation. The safety index of accident frequency rate (AFR) causing 452 injury construction accidents during 12 years (2007-2018) was analyzed and modeled. Data analysis was performed based on feature selection using Pearsonchr('39')s χ2 coefficient and SPSS modeler, as well as the artificial neural networks (ANNs) in MATLAB software. Results: Mean AFR was estimated at 26.32 ± 14.83, and the results of both approaches revealed that individual factors, organizational factors, training factors, and risk management-related factors could predict the AFR involved in SSCPs. Conclusion: The findings of this research could be reliably applied in the decision-making regarding safety and health construction issues. Furthermore, Pearsonchr('39')s correlation-coefficient and ANN modeling are considered to be reliable tools for accident modeling in SSCPs."