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Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak

Occupational safety and health (OSH) risk management research is common in industries such as construction, chemical production, electrical and electronic production. However, to date there is a lack of research performed on the OSH risk management related to shipyard industry. OSH practitioners oft...

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Main Authors: Calvin Yen Chih, Chin, David Sing Ngie, Chua, Soh Fong, Lim
Format: Thesis
Language:English
Published: International Journal of Integrated Engineering 2023
Subjects:
HD61 Risk Management
TJ Mechanical engineering and machinery
Online Access:http://ir.unimas.my/id/eprint/47422/4/Thesis%20Meng_CALVIN%20CHIN%20YEN%20CHIH.pdf
http://ir.unimas.my/id/eprint/47422/
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spelling my.unimas.ir-474222025-02-05T08:14:57Z http://ir.unimas.my/id/eprint/47422/ Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak Calvin Yen Chih, Chin David Sing Ngie, Chua Soh Fong, Lim HD61 Risk Management TJ Mechanical engineering and machinery Occupational safety and health (OSH) risk management research is common in industries such as construction, chemical production, electrical and electronic production. However, to date there is a lack of research performed on the OSH risk management related to shipyard industry. OSH practitioners often make preventive decisions based on physical site audit in the shipyard and relying on individual experience. The aforementioned task is difficult to execute due to multiple influencing factors that could affect the decision-making. Therefore, this project aims to propose a risk management framework by utilising a supervised machine learning Artificial Neural Network (ANN) to predict occupational risk in an identified Malaysia shipyard. Optimization and classification with the appropriate architecture of neuron layers were trained for the risk prediction at shipyards. In this research, 10 independent input variables and 1 single layer-dependent output variable (severity of risk) were defined. 7 networks modeling were developed between 19–31 neuron nodes. Accuracy performance of the developed networks were compared by using evaluation criteria, such as correlation of coefficient (R2), mean square error (MSE) and mean absolute percentage error (MAPE), to select the best model. The best selected ANN network no.4 achieves the highest accuracy performance of 90.22%. However, in terms of sensitivity analysis, input factors such as working hours (28.38%), and workplace factors (21.32%), have significant effects onto the OSH risk prediction. Then machine learning was intervened into the proposed framework which entails identification of hazards, risk assessment, safety decision-making, risk control, and risk prediction. With this, a dynamic risk management framework is formed and continuously improved. International Journal of Integrated Engineering 2023-09 Thesis PeerReviewed text en http://ir.unimas.my/id/eprint/47422/4/Thesis%20Meng_CALVIN%20CHIN%20YEN%20CHIH.pdf Calvin Yen Chih, Chin and David Sing Ngie, Chua and Soh Fong, Lim (2023) Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak. Masters thesis, Universiti Malaysia Sarawak.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic HD61 Risk Management
TJ Mechanical engineering and machinery
spellingShingle HD61 Risk Management
TJ Mechanical engineering and machinery
Calvin Yen Chih, Chin
David Sing Ngie, Chua
Soh Fong, Lim
Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak
description Occupational safety and health (OSH) risk management research is common in industries such as construction, chemical production, electrical and electronic production. However, to date there is a lack of research performed on the OSH risk management related to shipyard industry. OSH practitioners often make preventive decisions based on physical site audit in the shipyard and relying on individual experience. The aforementioned task is difficult to execute due to multiple influencing factors that could affect the decision-making. Therefore, this project aims to propose a risk management framework by utilising a supervised machine learning Artificial Neural Network (ANN) to predict occupational risk in an identified Malaysia shipyard. Optimization and classification with the appropriate architecture of neuron layers were trained for the risk prediction at shipyards. In this research, 10 independent input variables and 1 single layer-dependent output variable (severity of risk) were defined. 7 networks modeling were developed between 19–31 neuron nodes. Accuracy performance of the developed networks were compared by using evaluation criteria, such as correlation of coefficient (R2), mean square error (MSE) and mean absolute percentage error (MAPE), to select the best model. The best selected ANN network no.4 achieves the highest accuracy performance of 90.22%. However, in terms of sensitivity analysis, input factors such as working hours (28.38%), and workplace factors (21.32%), have significant effects onto the OSH risk prediction. Then machine learning was intervened into the proposed framework which entails identification of hazards, risk assessment, safety decision-making, risk control, and risk prediction. With this, a dynamic risk management framework is formed and continuously improved.
format Thesis
author Calvin Yen Chih, Chin
David Sing Ngie, Chua
Soh Fong, Lim
author_facet Calvin Yen Chih, Chin
David Sing Ngie, Chua
Soh Fong, Lim
author_sort Calvin Yen Chih, Chin
title Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak
title_short Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak
title_full Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak
title_fullStr Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak
title_full_unstemmed Application of Artificial Neural Network to Predict Occupational Safety and Health Risk in Shipyards – a Cases Study of 2 Shipyards in Sibu, Sarawak
title_sort application of artificial neural network to predict occupational safety and health risk in shipyards – a cases study of 2 shipyards in sibu, sarawak
publisher International Journal of Integrated Engineering
publishDate 2023
url http://ir.unimas.my/id/eprint/47422/4/Thesis%20Meng_CALVIN%20CHIN%20YEN%20CHIH.pdf
http://ir.unimas.my/id/eprint/47422/
_version_ 1823542192680992768
score 13.239859

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