Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Duan, Pinsheng; Zhou, Jianliang; Goh, Yihan

doi:10.1016/j.aei.2023.101990

Cited by 15 publications

(2 citation statements)

References 62 publications

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“…Due to the high complexity of causal relationships among risk events, it is difficult for traditional methods to effectively analyze them. Meanwhile, complex network (CN) has shown powerful applicability because of the quantitative analysis metrics under the CN theory that are able to assess risk events, such as degree distributions, clustering coefficients, betweenness, etc., and it has been widely utilized in the field of coal mines [25], urban railway [26], and construction [27]. Ma et al [28] stated that complex network could present and analyze complex interactions between influencing factors and identify key factors more effectively than traditional methods such as fault tree analysis, event tree analysis, and Bayesian network.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Lan,

2024

Applied Sciences

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Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

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Section: Literature Reviewmentioning

confidence: 99%

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Lan,

2024

Applied Sciences

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“…However, unlike pedestrian tracking on roads, the motion of workers always features long stays or frequent crossings [28], which may lead to the continuous growth of the covariance matrix in KF. In practical scenarios, a large covariance matrix can lead to the acceptance of false IDs.…”

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confidence: 99%

Bidirectional Tracking Method for Construction Workers in Dealing with Identity Errors

Liu,

Wang,

Zhou

2024

Mathematics

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Online multi-object tracking (MOT) techniques are instrumental in monitoring workers’ positions and identities in construction settings. Traditional approaches, which employ deep neural networks (DNNs) for detection followed by body similarity matching, often overlook the significance of clear head features and stable head motions. This study presents a novel bidirectional tracking method that integrates intra-frame processing, which combines head and body analysis to minimize false positives and inter-frame matching to control ID assignment. By leveraging head information for enhanced body tracking, the method generates smoother trajectories with reduced ID errors. The proposed method achieved a state-of-the-art (SOTA) performance, with a multiple-object tracking accuracy (MOTA) of 95.191%, higher-order tracking accuracy (HOTA) of 78.884% and an identity switch (IDSW) count of 0, making it a strong baseline for future research.

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Spatio-temporal Data Analytics for e-Waste Management System Using Hybrid Deep Belief Networks

Kumar,

Sulochana,

Jessintha

et al. 2024

Big Data Management

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Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Cited by 15 publications

References 62 publications

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Bidirectional Tracking Method for Construction Workers in Dealing with Identity Errors

Spatio-temporal Data Analytics for e-Waste Management System Using Hybrid Deep Belief Networks

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