Construction accidents: identification of the main associations between causes, mechanisms and stages of the construction process

Carrillo-Castrillo, Jesús Antonio; Trillo-Cabello, Antonio F.; Rubio-Romero, Juan Carlos

doi:10.1080/10803548.2016.1245507

Cited by 39 publications

(20 citation statements)

References 29 publications

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“…Positive correlations were only obtained between the parameters for two relations, i.e. between (3) and (6) and between (4) and (5). The obtained positive correlation means that both parameters increase simultaneously, i.e.…”

Section: Correlation Analysismentioning

confidence: 91%

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Analysis of occupational accidents in the construction industry with regards to selected time parameters

Szóstak

2019

Open Engineering

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The construction industry is characterized by a large variety and variability of implementation conditions. Construction works are carried out throughout the whole calendar year under varying atmospheric conditions, both during the day and also during the evening and night. Construction workers also often work overtime that exceeds the eight-hour working day. Work in such conditions creates a particularly high threat to the health and life of working people. In the issues of work safety the most important is worker, who in the accident process has a triple role: the decision maker, the perpetrator of an accident and the victim. The purpose of this article is to examine whether there is a dependence between the number of occupational accidents in the construction industry and time parameters such as: the time of an accident, the day of the week and the month in which an accident occurred. The article also attempts to determine the dependence between the age of the injured person, work experience and the number of hours worked by the injured person from the time of starting work to the moment of the accident.

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Section: Correlation Analysismentioning

confidence: 91%

“…• analysis of the mechanisms of the occurrence of occupational accidents [1,2], • analysis of the causes of accidents at work [3][4][5], • identi cation of direct and indirect factors which affect the accident rate [6][7][8] • analysis of the accident rate with regards to factors that generate costs [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of occupational accidents in the construction industry with regards to selected time parameters

Szóstak

2019

Open Engineering

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“…The determining factors of this situation can be found in the peculiarities of the construction sector [4]. In fact, the combination of the following aspects, such as frequent employee turnover, subcontracting and stringent contract deadlines, outdoor work in all weather conditions, lack of highly trained workers, use of obsolete working equipment, frequent change of work sites, as well as the presence of different companies on the same site contemporaneously contributes to the high rate of accidents and injuries [5]. Consequently, analysis of accident causality and the definition of the related measures for safety management represent a complex task [6].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Human Safety Assessment (PHSA) for the Improvement of the Behavioral Aspects of Safety Climate in the Construction Industry

Fargnoli

Lombardi

2019

Buildings

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Occupational safety in the construction industry still represents a relevant problem at a global level. In fact, the complexity of working activities in this sector requires a comprehensive approach that goes beyond normative compliance to guarantee safer working conditions. In particular, empirical research on the factors influencing the unsafe behavior of workers needs to be augmented. Thus, the relationship between human factors and safety management issues following a bottom-up approach was investigated. In particular, an easy-to-use procedure that can be used to better address workers’ safety needs augmenting the company’s safety climate and supporting safety management issues was developed. Such an approach, based on the assessment of human reliability factors, was verified in a real case study concerning the users of concrete mixer trucks. The results showed that the majority of human failures were action and retrieval errors, underlining the importance of theoretical and practical training programs as a means to improve safety behavior. In such a context, information and communication activities also resulted beneficially to augment the company’s safety climate. The proposed approach, despite its qualitative nature, allows a clearer understanding of workers’ perceptions of hazards and their risk-taking behavior, providing practical cues to monitor and improve the behavioral aspects of safety climate. Hence, these first results can contribute to augmenting safety knowledge in the construction industry, providing a basis for further investigations on the causalities related to human performances, which are considered a key element in the prevention of accidents.

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“…Previous investigations have indicated that construction accidents could be influenced by several factors [10,22]. In the present study, the statistical analysis and stochastic modeling revealed that construction accidents could be properly estimated with relatively high degrees of reliability and perception in terms of the contributing factors.…”

Section: Feature Selectionmentioning

confidence: 51%

“…Researchers have proposed various models to determine and analyze the factors involved in accidents and possibly predict construction accidents [10][11][12][13]. For instance, Mitropoulos et al developed a model for the analysis of accidents, predicting the correlations between accidents, and identifying the infleuntial factors in construction accidents [14].…”

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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Construction accidents: identification of the main associations between causes, mechanisms and stages of the construction process

Abstract: Significant differences have been found in accident causation depending on the mechanism of the accident and the construction stage ongoing. These results should be used to prioritize preventive actions to combat the most likely causes for each accident mechanism and construction stage.

Cited by 39 publications

References 29 publications

Analysis of occupational accidents in the construction industry with regards to selected time parameters

Analysis of occupational accidents in the construction industry with regards to selected time parameters

Preliminary Human Safety Assessment (PHSA) for the Improvement of the Behavioral Aspects of Safety Climate in the Construction Industry

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

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