Application of machine learning techniques for predicting the consequences of construction accidents in China

Zhu, Rongchen; Hu, Xiaofeng; Hou, Jiaqi; Li, Xin

doi:10.1016/j.psep.2020.08.006

Cited by 96 publications

(33 citation statements)

References 35 publications

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“…are labelled non-electrical as shown in Table 1 . The tree was pruned with R tuning parameters to avoid overfitting [ 46 ] and the optimal model with the best accuracy was selected. It can be seen from Figure 1 that the decision tree model has a total of thirteen nodes of which seven nodes are leaf nodes.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…By traversing from a root node to a leaf node and fulfilling the split conditions along the way, one can form decision rules [ 49 ] which reveal existing associations between predictor variables [ 50 ] and how they combine to predict the response variable. Since decision trees are easy to use and interpret [ 46 , 51 ], especially when studying the association between variables or factors [ 43 ], they have been applied in similar studies to analyze occupational accidents [ 31 , 32 , 44 , 49 , 50 , 52 , 53 ]. Common decision tree algorithms include C4.5, classification and regression tree (CART), and Chi-square automatic interaction detection (CHAID).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Gholizadeh

Onuchukwu

Esmaeili

2021

IJERPH

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“…Construction industry This research study applies machine-learning technique to analyze 16 critical factors with the aim of assessing the impact of the identified factors in predicting the severity of construction accidents. Zhu et al (2020) 11…”

Section: Min Et Al (2019)mentioning

confidence: 99%

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Shamsuzzoha

Kankaanpaa

Nguyen

et al. 2021

International Journal of Computer Integrated Manufacturing

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This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.

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“…In recent years there has been a significant effort to leverage data analytics to assist in decisionmaking that directly improves safety performance (Huang et al, 2018;, in what has been called the era of Safety 4.0 (Wang, 2021). To that end, a number of safety analytics (SA) approaches have been reported in the literature (see, for example, Zhu et al (2021); Sarkar et al (2020); Verma et al (2018); Poh et al (2018); Tixier et al (2016Tixier et al ( , 2017; Kakhki et al (2019); Cheng et al (2013)). These studies compare and extend different machine learning methods that attempt to forecast incidents or their severity in different industrial settings.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Testing and Evaluation of Safety Analytics Approaches

Paiva,

Tewari

2022

Preprint

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There has been a significant increase in the development of data-driven safety analytics (SA) approaches in recent years. In light of these advances it has become imperative to evaluate such approaches in a principled way to determine their merits and limitations. To that end, we propose an evaluation methodology underpinned by a simulated environment that allows for a comprehensive assessment of SA approaches. While assessing such approaches with historical field data is undoubtedly important, such an assessment has limited statistical power because it corresponds to only a few realizations. The proposed methodology enables validation over a large number of realizations, thereby circumventing the statistical limitations of evaluation on historical data. Moreover, a simulated environment allows for a comparison under controlled circumstances, resulting in a fair and systematic assessment of the potential long-term benefits of SA approaches. We demonstrate the utility of the proposed methodology via a case study that compares a few candidate SA approaches, which differ in how they assimilate field data to assess safety risk and suggest mitigative actions. We show that the proposed methodology indeed reveals useful insights and quantifies the relative merits and drawbacks of the different SA approaches, which would be otherwise difficult to objectively determine in a real-world scenario.

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Application of machine learning techniques for predicting the consequences of construction accidents in China

Cited by 96 publications

References 35 publications

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

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

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Methodology for Testing and Evaluation of Safety Analytics Approaches

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