Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: A complex network theory approach

Yang, Jianfeng; Wang, Pengchao; Liu, Xinyong; Bian, Ming-Cheng; Chen, Liangchao; Lv, Si-Yun; Tao, Jin-Fu; Suo, Guan-Yu; Xuan, Shen-Qing; Li, Ru; Zhang, Jianwen; Shu, Chi‐Min; Dou, Zhan

doi:10.1016/j.jlp.2023.105061

Cited by 23 publications

(3 citation statements)

References 41 publications

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“…According to Joghataei et al [55], the characteristics of worksite accidents can be classified according to industrial sector, cause, type, body part(s), location, time, month, and outcome. Yang et al [56] categorized 154 chemical accidents according to special job categories, industrial distribution, company scale, provincial location, time volatility characteristics, and accident causes. By using statistical data from 2010 to 2019, Shuang and Zhang [57] examined data for 7275 accidental deaths to determine accident characteristics based on time, occurrence, location, severity, and geographical area.…”

Section: Cacd Category Determinationmentioning

confidence: 99%

Analysis of Fatal Construction Accidents in Indonesia—A Case Study

Bria,

Chen,

Muhammad

et al. 2024

Buildings

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Booming demand for facilities and infrastructure in Indonesia has led to an increase in construction-related accidents. Court rulings provide valuable data on accident circumstances, which can help improve safety standards. Although information on these accidents is scarce and not systematically consolidated, effective data gathering and analysis can lead to better safety outcomes. This study analyzes 150 court rulings in construction industry accident cases and their related decision frameworks to identify seven risk categories using scenario analysis. The correlative patterns and their associated significance are explored via descriptive statistical analysis, and four categories, namely project, task, accident, and medium, were found to be correlated. The results of the cross-tabulation test between two categories were used to highlight correlated categories. Toll road works accounted for 52% of accident cases, with collisions with construction machinery being the most frequent accident type (36%, 76 cases). Lifting and moving goods had respective phi and Cramer values of 0.534 and 0.001. It is obvious that future safety improvement efforts should focus on roadway projects and utilizing construction vehicles for transportation and lifting work. This can be achieved through increased regulatory compliance, proper utilization of standard operating procedures (SOPs), and ensuring vehicle suitability. The results of this study are highly relevant to workplace health and safety officers and risk management personnel.

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Section: Cacd Category Determinationmentioning

confidence: 99%

Analysis of Fatal Construction Accidents in Indonesia—A Case Study

Bria,

Chen,

Muhammad

et al. 2024

Buildings

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“…The node evaluation index in complex networks is a specific representation of the topological relationship between nodes and edges in the network. Accurate analysis of the evaluation index can provide a better understanding of the characteristics of the network and the interaction between nodes [34]. In this paper, seven characteristic parameters, namely, degree of node, network diameter, average path length, clustering coefficient, intermediary centrality, closeness centrality, and comprehensive importance evaluation were selected as node indexes of complex networks for analysis, which can be seen in Table 1.…”

Section: Complex Network Evaluation Indexmentioning

confidence: 99%

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Wang,

Zhai,

Cao

et al. 2023

Applied Sciences

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The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents.

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“…Complex network theory has been extensively applied in the field of accident cause analysis. By using complex networks, the interrelations among causes are analyzed in diverse fields like coal mine accidents [19], chemical industry accidents [20], Tower-Crane Accidents [21], and construction accidents [22]. Yet, there are few applications of complex network theory in the domain of elevator safety accidents, considering a systemic analysis of all-rounded causes like humans, objects, environment, and management.…”

Section: Introductionmentioning

confidence: 99%

Cause Analysis of Elevator Accidents Based on Complex Network

Wenhui Du, Yiwei Chen, Qian Zhou, Minghui Zhang, Ru Yi

2024

jes

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In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.

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Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: A complex network theory approach

Cited by 23 publications

References 41 publications

Analysis of Fatal Construction Accidents in Indonesia—A Case Study

Analysis of Fatal Construction Accidents in Indonesia—A Case Study

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Cause Analysis of Elevator Accidents Based on Complex Network

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