Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm

Article type: Research Article

Authors: Mohammadian, Farough^a | Sadeghi, Mehran^b | Hanifi, Saber Moradi^c | Noorizadeh, Najaf^d | Abedi, Kamaladdin^a | Fazli, Zohreh^{e; *}

Affiliations: [a] Department of Occupational Health Engineering, Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran | [b] Computer Department, Institute of Higher Education of Bahmanyar, Kerman, Iran | [c] Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran | [d] Instructor of Occupational Health Engineering Department of Health, Safety and Environmental Management, School of Health Abadan Faculty of Medical Sciences, Abadan, Iran | [e] Instructor of Occupational Health Engineering, Student Research Committee, School of Public Health, Bam University of Medical Sciences, Bam, Iran

Correspondence: [*] Address for correspondence: Zohreh Fazli, Instructor of Occupational Health Engineering, Student Research Committee, School of Public Health, Bam University of Medical Sciences, Bam, Iran. Email: Zohreh.fazli@outlook.com.; Tel.: +989183426200; ORCID: 0000-0002-5206-2137

Abstract: BACKGROUND:Many occupational accidents annually occur worldwide. The construction industry injury is greater than the average injury to other industries. The severity of occupational accidents and the resulting injuries in these industries is very high and severe and several factors are involved in their occurrence. OBJECTIVE:Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm. METHODS:In this study, occupational accidents were analyzed and modeled during five years at construction sites of 5 major projects affiliated with a gas turbine manufacturing company based on census sampling. 712 accidents with all the studied variables were selected for the study. The process was implemented in MATLAB software version 2018a using combined artificial neural network and genetic algorithm. Additional information was also collected through checklists and interviews. RESULTS:Mean and standard deviation of accident severity rate (ASR) were obtained 283.08±102.55 days. The structure of the model is 21, 42, 42, 2, indicating that the model consists of 21 inputs (selected feature), 42 neurons in the first hidden layer, 42 neurons in the second hidden layer, and 2 output neurons. The two methods of genetic algorithm and artificial neural network showed that the severity rate of accidents and occupational injuries in this industry follows a systemic flow and has different causes. CONCLUSION:The model created based on the selected parameters is able to predict the accident occurrence based on working conditions, which can help decision makers in developing preventive strategies.

Keywords: Construction accident, data mining, workplace, neural network, genetic algorithm

DOI: 10.3233/WOR-205271

Journal: Work, vol. 73, no. 1, pp. 189-202, 2022