Factors affecting occupational accidents in the construction industry (2009-2013)

Mohammadfam, Iraj; Soltanzadeh, Ahmad; Moghimbeigi, Abbas; Akbarzadeh, Mohammad Ali

doi:10.18869/acadpub.johe.3.2.88

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…The results from the analysis emphasize the role of the nature of injury in such predictions and classifications, and are in agreement with the previous literature on the significance of the nature of injury in determining the severity of occupational injuries [15,16,[59][60][61][62][63][64]. In general, injuries are divided into three main groups in workers' compensation claims: occupational disease, multiple injuries, and specific injuries.…”

Section: Model Intepretation and Application In Safetysupporting

confidence: 86%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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Section: Model Intepretation and Application In Safetysupporting

confidence: 86%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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“…Additionally, these might be due to the weakness in the diagnosis or some failures in the risk management system in the workplace. In addition to physical harm and labor failure, the accidents could cause capital, equipment, and economic losses [1][2][3]. Several factors contribute to the incidence of occupational accidents, some of which are poor safety and work, size and magnitude of industry, lack of coordination, time pressure, financial and budgetary restraint, lack of data and standard information, poor organizational and nonorganizational communication, workers' poor participation in safety issues, workers' expertise, inadequate training, fatigue and exhaustion, improper equipment selection, improper use or inspection, poor safety management or awareness, and lack of protective equipment [4,5].…”

Section: Introductionmentioning

confidence: 99%

Association of Non-Organizational Factors and Occupational Accidents: A Field Study based on Structural Equation Modeling

Farahbod

Ghiyasi

Soltanzadeh

2021

JOHE

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Article InfoBackground: Non-organizational or non-occupational factors are among the most important risk factors that significantly influence the emergence of occupational accidents. This study aimed to investigate the association between non-organizational factors and occupational accidents. Materials and Methods: In this descriptive study the structural equation modeling was applied on the data that was collected using a self-developed questionnaire. The random selected sample (n=360) included damaged people referred to the emergency department in Tehran province for treatment during a five-year period . The data analysis was carried out using IBM SPSS AMOS v. 23.0. The goodness of fit indices, including χ2/df, RMSEA, GFI, CFI, NFI, and TLI, were evaluated. Results: The mean age and work experience of the experts' panel was 37.52±2.73 and 9.90±3.18 years, respectively. The Cronbach's alpha coefficient of the non-organizational factors of occupational accidents was calculated as 0.86. Generally, 35.3% of accidents were due to slip and fall, as well as falling the heavy object with 24.1%. The nonorganizational index was estimated at 2.95. The factor analysis findings showed a statistically significant association between the non-organizational factors and the occupational accidents (p<0.05). Conclusion:The results showed a reverse and significant association between the index of non-organizational factors with the title and type of accidents, cause of accidents, type of outcome or damage caused by accidents, and time of accident occurrence. Additionally, this index indicated a direct significant association with the gender, age, work experience, education, marital status, and the type of shift work schedule of the affected people.

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“…The univariate analysis results showed that 25 respondents felt that supervision in "X" Project were still lacking (62.5%) and 21 respondents behaved unsafely (50.2%). Study conducted in construction between 2009 and 2013 reveal that training factor, housekeeping, and supervision are still being implemented poorly in construction site (Mohammadfam et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Supervision of Unsafe Act on Formwork Workers at Project “X” Bekasi City

Lestari¹,

Lestari²

2018

Kemas

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Former observation results at “X” Project find several unsafe behaviors such as not using personal protective equipment (PPE), smoking and jesting while working. The aim of the study is to determine the association between supervision and unsafe behavior in formwork workers of “X” project Bekasi City. This study used cross sectional design. Independent variable was supervision, whereas dependent variable was unsafe behavior. Study population involved all of the formwork workers (40 people). Data was collected through observation using guided checklist on JSA and interview using valid and reliable questionnaire. Data analysis was conducted by chi-square. The study results showed that 25 respondents (62.5%) say that supervision are lacking while 21 respondents (50.2%) perform unsafe behavior. P-value of 0.011 means that there is an association between supervision and unsafe behavior, while the prevalence ratio value of 2.550 with 95%CI of 1,056–6,155 imply that poor supervision has 2.550 higher risk on the occurrence of unsafe behavior in workers compared to better supervision. In conclusion, there is an association between supervision and unsafe behavior.

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Factors affecting occupational accidents in the construction industry (2009-2013)

Cited by 10 publications

References 18 publications

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Association of Non-Organizational Factors and Occupational Accidents: A Field Study based on Structural Equation Modeling

Supervision of Unsafe Act on Formwork Workers at Project “X” Bekasi City

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