Volume 15, Issue 3 (10-2025)
J Health Saf Work 2025, 15(3): 485-500 |
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Esmaeili H, Afsharkazemi M A, Radfar R, Pilevari N. An Evolutionary Rule Based Framework for Real-Time Risk Governance of Noise and Fumigant Hazards in Marine-Manufacturing Systems. J Health Saf Work 2025; 15 (3) :485-500
URL: http://jhsw.tums.ac.ir/article-1-7203-en.html
URL: http://jhsw.tums.ac.ir/article-1-7203-en.html
1- Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran ,moh.afsharkazemi@iauctb.ac.ir
2- Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran ,
Abstract: (1504 Views)
Introduction: Fumigant gases in maritime and container chains, along with occupational noise in marine and manufacturing industries, are among the most significant chronic risk factors. They are usually assessed separately, despite their simultaneous impact on workers’ health. The importance of this study lies in presenting an integrated approach for real-time monitoring of combined risk and aligning it with occupational exposure limits (OELs). The aim is to develop and validate an interpretable, regulation-oriented framework for predicting combined risk.
Material and Methods: This research integrated and normalized data from the Global Burden of Disease (GBD) 2021 study including age-standardized disability rates (ASDR) and average annual percentage change (AAPC) for 204 countries with occupational exposure limit tables for fumigants. A Sugeno-type fuzzy inference system with three inputs and four rules was designed. Weights and membership function boundaries were optimized using the Prairie Dog Optimization algorithm, and a threshold-based scenario generation module was applied to produce high-risk synthetic data. Model performance was evaluated through an OEL compliance test.
Results: Findings revealed that the proposed optimization reduced the loss function by 42% compared to random search. The mean absolute error (0.028 ± 0.006) and root mean square error (0.041) were obtained. Threshold-based scenario generation improved data coverage in high-risk regions from 0.62 to 0.90 and increased the accuracy of critical condition detection from 0.71 to 0.89. The OEL compliance index reached 0.93, confirming input weighting as the most influential factor.
Conclusion: The proposed framework simultaneously ensures numerical accuracy, interpretability, and regulatory compliance with occupational exposure limits. It can be deployed within real-time monitoring dashboards for ports and factories. Future research should integrate IoT sensors and multi-objective optimization to enable dynamic updates in response to evolving regulations and operational conditions.
Material and Methods: This research integrated and normalized data from the Global Burden of Disease (GBD) 2021 study including age-standardized disability rates (ASDR) and average annual percentage change (AAPC) for 204 countries with occupational exposure limit tables for fumigants. A Sugeno-type fuzzy inference system with three inputs and four rules was designed. Weights and membership function boundaries were optimized using the Prairie Dog Optimization algorithm, and a threshold-based scenario generation module was applied to produce high-risk synthetic data. Model performance was evaluated through an OEL compliance test.
Results: Findings revealed that the proposed optimization reduced the loss function by 42% compared to random search. The mean absolute error (0.028 ± 0.006) and root mean square error (0.041) were obtained. Threshold-based scenario generation improved data coverage in high-risk regions from 0.62 to 0.90 and increased the accuracy of critical condition detection from 0.71 to 0.89. The OEL compliance index reached 0.93, confirming input weighting as the most influential factor.
Conclusion: The proposed framework simultaneously ensures numerical accuracy, interpretability, and regulatory compliance with occupational exposure limits. It can be deployed within real-time monitoring dashboards for ports and factories. Future research should integrate IoT sensors and multi-objective optimization to enable dynamic updates in response to evolving regulations and operational conditions.
Keywords: Threshold-based scenario generation, Occupational noise-induced illness, Occupational exposure limits, Human factors in accidents
Type of Study: Research |
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