Machine learning predictive model based on national data for fatal accidents of construction workers

Choi, Jongko; Gu, Bonsung; Chin, Sangyoon; Lee, Jong-Seok

doi:10.1016/j.autcon.2019.102974

Cited by 126 publications

(112 citation statements)

References 41 publications

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“…A decision tree is a supervised data mining methodology widely used to uncover hidden patterns in categorical data [ 42 , 43 , 44 , 45 , 46 , 47 ] that can be visually represented by an inverted tree-like structure or diagram. The goal of most decision tree algorithms is to split data by minimizing the impurity of the final categories.…”

Section: Methodsmentioning

confidence: 99%

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Gholizadeh

Onuchukwu

Esmaeili

2021

IJERPH

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This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration’s fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings, new construction, and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type, source of injury, cause of injury, injured part of body, nature of injury, and event type. In other words, the statistical probability of a fatal accident—given an accident occurrence—changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is parts and materials; followed by whether the source of injury is tools, instruments, and equipment. In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is parts and materials and whether the source of injury is tools, instruments, and equipment are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.

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Section: Methodsmentioning

confidence: 99%

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Gholizadeh

Onuchukwu

Esmaeili

2021

IJERPH

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“…For instance, one study used two machine learning models, random forest and stochastic gradient tree boosting, to predict construction injury details, including injury type, energy type, and body part [43]. Another study used logistic regression, decision tree, random forest, and AdaBoost analysis machine learning methods to develop a predictive model to help prevent construction accidents [44]. To predict the safety climate in a construction site, another study developed a model based on an artificial neural network that assists clients and contractors in safely managing their construction sites by evaluating and predicting the safety climate [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach

Makki

Mosly

2021

Applied Sciences

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Construction site accidents can be reduced through proactive steps using prediction models developed based on factors that influence the safety climate. In this study, a prediction model of the safety climate observed by construction site personnel in Saudi Arabia was developed, identifying a set of significant safety climate predictors. The model was built with data collected from 401 active construction site personnel using a bootstrapped multiple ordinal logistic regression model. The model revealed five significant predictors: supervision, guidance, and inspection; social security and health insurance; management’s commitment to safety; management’s safety justice; and coworker influence. The model can correctly predict 67% of the safety evaluations. The identified predictors present proof of the importance of safety support, commitment, and interaction in construction sites and their influence on the perceived evaluations of the safety climate by personnel. Moreover, the prediction model can help construction industry decision makers, safety policy designers, government agencies, and stakeholders to estimate the safety climate and assess the current situation. Furthermore, the model can help form a better understanding and determine areas of improvement, which can translate into higher safety performance levels.

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“…Profession-related diseases are those associated with the profession of the victim, whereas occupational diseases are the ones caused by special conditions a worker is subject to (AZZOLIN et al, 2012). , v.16, n. 2, p. 168 -194, 2021. order to carry out these analyses, some tools should be used, such as the exploratory analysis of data and statistical methods (CHENG et al, 2010) and computational resources, such as data mining (CHOI et al, 2020).…”

Section: Occupational Safety and Health (Osh)mentioning

confidence: 99%

Occupational health and safety and data mining: a bibliometric analysis

Rafael¹,

Peternella²,

Reis³

et al. 2021

GEPROS

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Purpose - This article aims to carry out a bibliometric analysis on data mining and occupational health and safety, covering the period between 2008 and 2020, for seven scientific databases and 68 articles. Theoretical framework - This study was theoretically based on concepts that involve data mining, machine learning and occupational health and safety. Design/methodology/approach - The selected articles were submitted to a statistical analysis, together with the evaluation of one of the bibliometric laws (Bradford's Law), comprising a number of citations, journals, authors, countries of origin, publication categories and an evaluation of production over the years. Findings - As a result, it was found that the most influential journal was Safety Science, and Taiwan was the leading country in terms of articles produced, with an average of 115 citations per article. The best-ranked journals related to Engineering and Health, both corresponding to 30% of the selected articles and journals. Originality/value - This study provides some insights into the growth of the data mining area together with occupational health and safety. Keywords - Bibliometrics analysis. Occupational health and safety. Data mining.

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Machine learning predictive model based on national data for fatal accidents of construction workers

Cited by 126 publications

References 41 publications

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

Predicting the Safety Climate in Construction Sites of Saudi Arabia: A Bootstrapped Multiple Ordinal Logistic Regression Modeling Approach

Occupational health and safety and data mining: a bibliometric analysis

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