Decision tree analysis of construction fall accidents involving roofers

Mıstıkoğlu, Gülgün; Gerek, İbrahim Halil; Erdiş, Ercan; Usmen, P.E. Mumtaz; Cakan, Hulya; Kazan, Emrah

doi:10.1016/j.eswa.2014.10.009

Cited by 139 publications

(65 citation statements)

References 36 publications

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“…Besides, it dealt with only struck-by accidents only. Mistikoglu et al (2015) conducted a decision tree analysis, which is a supervised data mining method. The research investigated a fall from height accidents taken from the US Occupational Safety and Health Administration (OSHA).…”

Section: Analytical Models For the Construction Safetymentioning

confidence: 99%

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

Ayhan

Doğan

Tokdemir

2020

Journal of Civil Engineering and Management

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Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.

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Section: Analytical Models For the Construction Safetymentioning

confidence: 99%

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

Ayhan

Doğan

Tokdemir

2020

Journal of Civil Engineering and Management

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“…The merits of decision tree methodology are discussed in the works of Tsui et al (2006), Huo et al (2006), Hand et al (2001), Mistikoglu et al (2015) and Mantas and Abellan (2014). Decision trees are adept at handling both continuous and categorical variables.…”

Section: Decision Tree Methodologymentioning

confidence: 99%

Modeling the Impact of Airport Deicing/Anti-icing Activities on the Dissolved Oxygen Levels in the Receiving Waterways

Ariyajunya

Tarun

Chen

et al. 2018

JWMM

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This paper evaluates the efficacy of the environmental management system (EMS) at Dallas Fort Worth International Airport (DFW) in capturing spent aircraft deicing fluids to prevent their discharge into receiving waterways. DFW claims that its EMS captures the spent aircraft deicing fluids completely, leaving only the spent aircraft anti-icing fluids to contribute to drip and shear during taxiing and takeoff and subsequent runoff to the waterways. Glycols in the aircraft deicing and anti-icing fluids reduce dissolved oxygen (DO) in the airport's receiving waterways upon mixing with it during aircraft deicing operations.To evaluate the airport's EMS claim, two decision tree models were built: one with anti-icing glycol usage as a predictor variable, and one with separated deicing glycol usage and anti-icing glycol usage as predictor variables. The analyses suggest that deicing glycol usage is more significant for predicting DO concentrations in the airport's receiving waters than is anti-icing glycol usage.

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“…In turn, Drozd [6,7] analyzed the costs of accidents and the length of a post-accident absence. A study on the above-mentioned papers and global research carried out by Saiful, Razwanul and Tarek [8], Mistikoglu et al [9], Chua and Goh [10], or Chan, Leung and Liu [11] indicate that there is a strong need to build models of advisory systems supporting H&S management in its various aspects. Such systems should be effective, easy to use, and predict precisely with the historical data.…”

Section: Introductionmentioning

confidence: 99%

Predicting the length of a post-accident absence in construction with boosted decision trees

Krawczyńska-Piechna¹

2020

MATEC Web Conf.

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Work safety control and analysis of accidents during construction performance are one of the most important issues of construction management. The paper focuses on post-accident absence as an element of occupational safety management. Somehow, the length of the post-accident absence can be treated as an indicator of building performance safety. The paper attempts to answer the question of whether it is possible to use boosted classifier ensembles to predict the post-accident absence length using a small set of historical observations, and which classification algorithm is the most promising to solve the prediction problem. It also proves that there is a dependence between the length of the post-accident absence and the cause of the accident or working conditions The choice of boosted algorithms is not accidental. Thanks to the use of aggregation methods it is possible to build classifiers that predict precisely and do not require any initial data treatment, which simplifies the prediction process significantly. The model of the prediction problem has been clarified. To identify the most promising classifier ensemble the prediction accuracy measures of selected classification algorithms were analyzed. The data used to build models was gathered on national (Polish) construction sites.

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Decision tree analysis of construction fall accidents involving roofers

Cited by 139 publications

References 36 publications

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

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

Modeling the Impact of Airport Deicing/Anti-icing Activities on the Dissolved Oxygen Levels in the Receiving Waterways

Predicting the length of a post-accident absence in construction with boosted decision trees

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