Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks

Ayhan, Bilal Umut; Tokdemir, Onur Behzat

doi:10.1061/(asce)co.1943-7862.0001762

Cited by 96 publications

(56 citation statements)

References 39 publications

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“…Attributes as well as the background of working team will be taken into account for ARM. (Ayhan & Tokdemir, 2020) Creating a risk matrix and collecting the background information of working team and company…”

Section: Discussionmentioning

confidence: 99%

“…The proposed study introduces a new approach explicitly for the safety prevention process. The preventative measures defined in previous studies (Ayhan & Tokdemir, 2019a, 2019b, 2020 enlighten the future work. Figure 9 stands for visualizing the development of procedures for preventative measures in the most recent studies.…”

Section: Contributions To Body Of Knowledgementioning

confidence: 99%

“…Initially, the prevention measures were established just concerning the severity level of construction incidents. The construction cases were classified into three groups, and preventative measures formed according to severity (Ayhan & Tokdemir, 2020). The second study supplemented a risk matrix which enables to rate risk factor of the working team as well as the company.…”

Section: Contributions To Body Of Knowledgementioning

confidence: 99%

“…Besides, the present model is capable of adapting the changes in data since it accomplished to obtain different characteristics regarding the accident type. That means the model can work dynamically with any system and can be part of a universal system that was started to be studied in previous studies (Ayhan & Tokdemir, 2019a, 2019b, 2020.…”

Section: Contributions To Body Of Knowledgementioning

confidence: 99%

“…There are several studies previously conducted in order to investigate the correlation of contributory factors of occupational accidents in construction sites via Association Rule Mining (ARM) (e.g., Liao & Perng, 2008;Cheng et al, 2010;Verma et al, 2014;Shin et al, 2018;Guo et al, 2019;Liao et al, 2019). Some other studies established a predictive model and introduced a simple preventative measure that cannot be adaptable for changes in data (Ayhan & Tokdemir, 2019a, 2019b, 2020. The lacking point of the current studies is that they require an individual examination to capture prospective risks.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Discussionmentioning

confidence: 99%

Section: Contributions To Body Of Knowledgementioning

confidence: 99%

Section: Contributions To Body Of Knowledgementioning

confidence: 99%

Section: Contributions To Body Of Knowledgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

Liu,

Li,

Qiang

et al. 2023

Engineering Reports

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Construction accidents in the construction industry cause a large amount of property damage and human casualties. Therefore, avoiding construction accidents as much as possible is a problem that engineers have been working on for a long time. Currently, few construction managers use artificial intelligence methods for construction safety management. The purpose of this article is to propose a new artificial neural network (ANN) prediction model to provide early warning for future construction and to provide reference for construction organization decision‐makers. In the proposed method, atomic search optimization algorithm is used to optimize the weights and thresholds of back propagation neural network, and the Tent chaotic mapping is used to initialize the population to increase the diversity of the population. The statistical data of production safety accidents of housing and municipal engineering in China from 2015 to 2019 are used as an example, and the prediction results of the proposed model are compared with back‐propagation neural network (BPNN) and wavelet neural network (WNN). The mean absolute error (MAE) of predicting construction accidents is 0.2225, with small fluctuations in the predicted results. The mean absolute percentage error (MAPE) of the predictions is 0.6048%. The research results indicate that IASO‐BPNN has higher prediction accuracy than standard BPNN and WNN, providing judgment methods for ensuring construction progress and decision support for construction organization decision‐makers.

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Introducing Safety on Construction Industry Along with Lean Construction Hypothesis

Anandh

Nachiar

Abeshek

et al. 2022

Lecture Notes in Civil Engineering

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Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks

Cited by 96 publications

References 39 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

Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

Introducing Safety on Construction Industry Along with Lean Construction Hypothesis

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