An Association Rule Mining Model for the Assessment of the Correlations Between the Attributes of Severe Accidents

Ayhan, Bilal Umut; Doğan, Neşet Berkay; Tokdemir, Onur Behzat

doi:10.3846/jcem.2020.12316

Cited by 19 publications

(13 citation statements)

References 69 publications

(94 reference statements)

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“…-In Tree Level 2 and Rule Number IV, 30% of workers who were struck by falling objects, bitten/stung/scratched, and caught in or between objects had serious/ fatal injuries because PPE was not provided. In a similar study, "being struck by an object" was the most observed and fatal accident type that generally occurred during operations (Ayhan et. al., 2020).…”

Section: Resultsmentioning

confidence: 91%

Applying decision tree algorithm to explore occupational injuries in the Turkish construction industry

Tetik

Kale

Bayram

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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Occupational injuries and fatalities are one of the most significant issues in the construction industry. Variables, such as workers’ behavior, age, worksite condition, and type of activity, play key roles in the occurrence of construction accidents. In recent years, data mining techniques have been successfully used not only in health, economy, and social sciences but also in construction-related fields. In this study, C5.0 decision tree algorithm was used to analyze the accident data obtained from the Social Security Institution of Turkey. A classification tree model was created to discover the associations between the attributes. The results show the relationship between the injury status of workers and the attributes, and the accuracy rate of the model was 70.26%. Meanwhile, according to findings, unsafe conditions, type of accident, and activity type were the most important attributes in the model. Furthermore, the predictor importance of the attributes was compared, and several outcomes were discovered; for instance, the workers’ educational background has greater predictive power than age. On the other hand, the branches of the decision tree pointed out several attribute sequences due to their high rated serious/fatal injury rates. The results of this study can be used in the prevention and mitigation strategies for construction accidents.

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Section: Resultsmentioning

confidence: 91%

Applying decision tree algorithm to explore occupational injuries in the Turkish construction industry

Tetik

Kale

Bayram

et al. 2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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“…The association rule method (ARM) is very popular in accident analysis. Ayhan et al (2020) used ARM to identify the attributes of severe accidents and concluded that the most common attribute in various accidents is that personnel cannot perceive external risks. Yang (2020) constructed a logistics financial risk ontology model based on ontology and association rules.…”

Section: Literature Reviewmentioning

confidence: 99%

An association rule mining model for evaluating the potential correlation of construction cross operation risk

Chen

Tian

Liu

et al. 2022

ECAM

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PurposeCross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.Design/methodology/approachThe concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.FindingsThe application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.Originality/valueThis is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

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“…, 2019). In the European Union (EU), as well as the USA, construction accounts for a full one-fifth of accidents reported across all industries (Ayhan et al. , 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The myriad uncertainties and risks involved in such endeavors, combined with an intense labor dependence (Ayhan and Tokdemir, 2019), results in relatively high rates of occupational injuries (Bavafa et al, 2018;Jin et al, 2019). In the European Union (EU), as well as the USA, construction accounts for a full one-fifth of accidents reported across all industries (Ayhan et al, 2020). Moreover, over the period 2010 to 2016, the number of occupational health and safety (OHS)-related injuries increased 28%, with some 991 fatalities in 2016 (Al-Bayati et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Towards enhancement in reliability and safety of construction projects: developing a hybrid multi-dimensional fuzzy-based approach

Mohandes

Durdyev

Sadeghi

et al. 2022

ECAM

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PurposeIn the study, a five-dimensional-safety risk assessment model (5D-SRAM) is developed to improve the construction safety risk assessment approaches available in the literature. To that purpose, a hybrid multi-dimensional fuzzy-based model is proposed, which provides a comprehensive ranking system for the safety risks existing in a project by considering the contextualization of the construction-related activities resulting in an accident.Design/methodology/approachThe developed 5D-SRAM is based on an amalgamation of different fuzzy-based techniques. Through the proposed fuzzy analytic hierarchy process (AHP) method, the importance weights of essential risk dimensions playing role in defining the magnitude of the construction-related risks are obtained, while a precise prioritized ranking system for the identified safety risks is acquired using the proposed fuzzy technique of order preference similarity to the ideal solution (FTOPSIS).FindingsThrough the application of the proposed 5D-SRAM to a real-life case study – which is the case of green building construction projects located in Hong Kong – contributions are realized as follows: (1) determination of a more complete range of risk dimensions, (2) calculation of importance weightings for each risk dimension and (3) obtainment of a precise and inclusive ranking system for safety risks. Additionally, the supremacy of the developed 5D-SRAM against the other safety assessment approaches that are commonly adopted in the construction industry is proved.Research limitations/implicationsThe developed 5D-SRAM provides the concerned safety decision-makers with not only all the crucial dimensions that play roles toward the magnitude of safety risks posing threats to the workers involved in construction activities, but also they are given hindsight regarding the importance weights of these dimensions. Additionally, the concerned parties are embellished with the final ranking of safety risks in a more comprehensive way than those of existing assessment methods, leading to sagacious adoption of future prudent strategies for dealing with such risks occurring on construction sites.Originality/valueNumerous studies have documented the safety risks faced by construction workers including proposals for risk assessment models. However, the dimensions considered by such models are limited, generally constrained to risk event probability combined with risk impact severity. Overlooking other dimensions that are essential towards the calculation of safety risks' magnitude culminates in overshadowing the further adoption of fruitful mitigative actions. To overcome this shortcoming, this study proposes a novel 5D-SRAM.

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An Association Rule Mining Model for the Assessment of the Correlations Between the Attributes of Severe Accidents

Cited by 19 publications

References 69 publications

Applying decision tree algorithm to explore occupational injuries in the Turkish construction industry

Applying decision tree algorithm to explore occupational injuries in the Turkish construction industry

An association rule mining model for evaluating the potential correlation of construction cross operation risk

Towards enhancement in reliability and safety of construction projects: developing a hybrid multi-dimensional fuzzy-based approach

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