Journal of Health and Safety at Work

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Introduction: Each country needs to preserve its human capital through preventing accidents for its development. Therefore, this study is carried out to study the relationship between safety investments and safety performance indices considering the interactive effect of the project hazard level in construction industry.
Material and Methods: This study was conducted using multiple case studies in 5 major construction worksites, in Tehran, in 2019. Data was collected using questionnaire, checklists and interview as well as evaluating the safety documents. The data analysis in this study was carried out using SPSS 18.
Results: There was a strong inverse correlation between safety investments (total safety investment, basic safety investment, and voluntary safety investment) and accident frequency rate (AFR) (r=-0.936, P-value<0.05), and there was a direct strong correlation between safety investment and safety performance (P-value<0.05, r=0.939). Also, the effect of various safety investments on safety performance indices under various project conditions (project hazard levels) was not the same; when the project hazard level was high, the effect of safety investments on safety performance was higher.
Conclusion: Increasing safety investment improves safety performance through decreasing the accidents. Also, investment in both safety components (basic safety investment and voluntary safety investment) might improve safety performance. The results of the current study can be used as a basis by the contractors and construction companies to invest in safety and to determine proper budget for managing safety of construction projects.

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Introduction: Acceptance of Health, Safety, and Environment (HSE) rules plays a crucial role in determining the performance of employees in HSE-related areas at the workplace. This study aimed to design a questionnaire to investigate influential factors on acceptance of HSE rules among employees.
Material and Methods: The face validity of the survey was assessed by ten individuals from the target population, while content validity was evaluated by ten HSE experts using both quantitative and qualitative methods. The impact scores were calculated for the quantitative assessment of face validity, and the Content Validity Ratio (CVR) and Content Validity Index (CVI) values were used to assess content validity. Construct validity was determined through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) involving 506 participants. The reliability of the survey was evaluated using Cronbach’s alpha and the Intraclass Correlation Coefficient (ICC).
Results: All items in the survey showed satisfactory levels of impact score (>1.5), CVR (>0.69), and CVI (>0.79). The mean values for the Impact score, CVI, CVR, and S-CVI-UA were 4.26, 0.963, 0.944, and 0.62, respectively. The survey and its dimensions demonstrated strong reliability, as indicated by Cronbach’s alpha and ICC values exceeding 0.70. Additionally, EFA successfully identified the structure of the questionnaire, and CFA confirmed its goodness of fit.
Conclusion: The Persian version of the questionnaire demonstrated satisfactory validity and reliability. This instrument can be effectively used to assess the factors that influence the acceptance of HSE rules among employees in various workplace settings.

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Introduction: Cognitive functions play a vital role in how tasks are performed; for this, temporary cognitive and mental dysfunctions could lead to grave consequences, especially when an accurate and prompt response is required. Attention and reaction time to noise are among the most effective exogenous factors on the brain processing mechanism. This study aimed to measure the sustained attention of workers in the steel industry exposed to different sound pressure levels. 
Material and Methods: The study was conducted in 4 general stages, including 1- Selecting predictive orientation variables (age, work history, different sound pressure levels); 2- Conducting the Cognitive Performance Test (CPT); 3 Conducting N-BACK Cognitive Performance Test and 4- Modeling cognitive performance changes using model precision methods.
Results: Continuous Performance Test (CPT) results indicated that all three groups’ omission error, commission error, and response time were affected by shift time. All three components increased significantly as the shift ended, decreasing individuals’ cognitive function. Also, the higher noise impact in modeling CPT and N-Back tests indicated reduced workers’ concentration.
Conclusion: These study findings suggested that greater noise weight obtained in test modeling in three-time intervals, i.e., in the beginning, middle, and end of the shift, affected the continuous performance components of the CPT and working memory performance of the N-back test, including workers’ response time and reaction time, with workers’ rate of error increasing and their focus decreasing during the shift. 

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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.