Exploring Causal Factors on the Severity Rate of Occupational Accidents in Construction Worksites

Soltanzadeh, Ahmad; Mohammadfam, Iraj; Moghimbeygi, Abbas; Ghiasvand, Reza

doi:10.1007/s40999-017-0184-9

Cited by 30 publications

(25 citation statements)

References 20 publications

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“…According to the SEM results, indicator variables, such as using PPE, implementation of housekeeping, and TBM have the most effect on S and H risk control latent factor. Furthermore, consistent with the findings, several studies revealed that using PPE and implementing housekeeping as well as TBM are basic ways in reducing unsafe conditions and accident prevention in installation and construction phases (4,12,15).…”

Section: Discussionsupporting

confidence: 75%

“…The findings have proved that the investigated and modeled important variables and factors in construction, installation, and start-up phases of process industries for various reasons had been ignored. Some of these reasons are unstable working conditions, using contract workforces, financial and budget limitations, time pressure for completing projects, insufficient organizing safety issues, financial problems for implementing S and H measures, caused by unsystematic risk management, incomplete and insufficient data and information about dangers and accidents, lack of HAZID and accident investigation system in risk management systems, and not having involved workers in safety problems (4,5,12,21). tant phases before the operation in the process industries; it is notable that occupational accidents, especially in more complicated process workplaces and unstable phases, such as construction, installation, and start up arisen from faults or failures in the interactions between workers, workplaces, material and equipment.…”

Section: Discussionmentioning

confidence: 99%

“…Variables of S and H risk management system (e.g. S and H training, risk assessment indicator variables, and control measures) are good cases in point (4)(5)(6).…”

Section: Introductionmentioning

confidence: 99%

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Structural Equation Modeling Modeling (SEM) of Occupational Accidents Size Based on Risk Management Factors; A Field Study in Process Industries

et al. 2018

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Background: Due to high risks for occupational accidents, process industries are one of the most dangerous industries around the world. Accidents' size in these industries are influenced by combination of different factors. Objectives: The present study aimed at analyzing and modeling occupational accidents' size and investigating the role of different risk management factors on accidents' size in process industries. Methods: This analytical study was carried out on accidents in ten process industries, including petrochemicals, refineries, and chemical industries during eight years (2008 to 2015). Studied data were included variables and factors of risk management systems and information about human injuring accidents. Data analysis and modeling were done based on feature selection by Pearson χ 2 coefficient and structural equation modeling (SEM) approach using statistical software of IBM SPSS AMOS v22.0. Results: Lost working days (LWD) as index of accident size was estimated 197.42 ± 111.06 days. Results of feature selection and SEM approach showed that LWD was affected by different factors such as safety and health (S and H) training, risk management, and risk control, and its indicator variables (P < 0.05). Conclusions: The findings implied that structural equation modeling is a reliable and applicable accidents analysis method. Furthermore, the results should be considered to prevent and reduce occupational accidents' size in process industries.

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

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Structural Equation Modeling Modeling (SEM) of Occupational Accidents Size Based on Risk Management Factors; A Field Study in Process Industries

et al. 2018

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“…CASI is mostly a mathematical aggregation of two or more basic indicators that can reflect production situation of the construction industry. On the whole, CASIs are divided into three categories: accident frequency, accident severity and accident trend (Dong et al, 2011;Sari, Selcuk, Karpuz, & Duzgun, 2009;Soltanzadeh, Mohammadfam, Moghimbeygi, & Ghiasvand, 2017). From the accident frequency level, fatal injury rate per 100,000 full-time equivalent workers are widely used to estimate annual average fatalities based on construction time perspective (Marsh & Fosbroke, 2015;Mendeloff & Burns, 2013); fatal injury rate per 100,000 construction practitioners can be considered to explain the average fatalities of construction practitioners based on construction personnel perspective (Coates, 2011;Eteifa & El-adaway, 2018); fatal injury rate per 100,000,000 yuan of GDP in the construction industry is always selected to reflect the harmonious level between the construction industry and economic development based on construction output value perspective.…”

Section: Literature Reviewmentioning

confidence: 99%

Comprehensive Assessment Model on Accident Situations of the Construction Industry in China: A Macro-Level Perspective

Shao

Tong

et al. 2019

Journal of Civil Engineering and Management

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As one of the most high-risk sections, the construction industry has traditionally been the research hotspot. Yet little attention has been paid to macro-level accident situations of the entire industry. Therefore, this study develops a comprehensive assessment model on accident situations of Chinese building industry, aiming at assisting the government to better understand and improve accident situations of the entire industry. Based on China conditions, six indicators related to accident situations are firstly selected to establish an indicator system; then structure entropy weight method is proposed to determine indicator weighs, with dynamic classification method to explore the characteristics of accident situations. The results demonstrate that the provinces with poor accident situations account for 53% of all provinces, and they are mainly distributed in the central and western regions of China where there exist the underdeveloped economy. Meanwhile, some provinces experience poor accident situations that could be out-of-control, especially for Hebei. Provinces in the southeastern and northeastern regions of China perform relatively well, but they still have much improvement room for accident situations. The findings validate the rationality of the developed model and can provide valuable insights of safety regulation strategies for the government from the macro-level perspective.

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“…The assessments performed in small-scale construction projects (SSCPs) are associated with significant challenges in terms of safety and health performance problems, which is mainly due to the multidimensionality of safety and health (S&H) issues, which often yield unreliable outcomes [1,2]. In the past two decades, more than 26,000 individuals employed in construction companies have been reported to die in the United States, which accounts for an average of five deaths per day [3].…”

Section: Introductionmentioning

confidence: 99%

Accident Modeling in Small-scale Construction Projects Based on Artificial Neural Networks

Savareh

Mahdinia

Ghiyasi

et al. 2019

J. Hum. Environ. Health Promot

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Journal of Human, Environment, and Health PromotionJournal homepage: www.zums.ac.ir/jhehp 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)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) was analyzed and modeled. Data analysis was performed based on feature selection using Pearson's χ 2 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 decisionmaking regarding safety and health construction issues. Furthermore, Pearson's correlation-coefficient and ANN modeling are considered to be reliable tools for accident modeling in SSCPs.

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Exploring Causal Factors on the Severity Rate of Occupational Accidents in Construction Worksites

Cited by 30 publications

References 20 publications

Structural Equation Modeling Modeling (SEM) of Occupational Accidents Size Based on Risk Management Factors; A Field Study in Process Industries

Structural Equation Modeling Modeling (SEM) of Occupational Accidents Size Based on Risk Management Factors; A Field Study in Process Industries

Comprehensive Assessment Model on Accident Situations of the Construction Industry in China: A Macro-Level Perspective

Accident Modeling in Small-scale Construction Projects Based on Artificial Neural Networks

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