Application of machine learning technology for occupational accident severity prediction in the case of construction collapse accidents-Reference-Cited by-同舟云学术

Application of machine learning technology for occupational accident severity prediction in the case of construction collapse accidents

Published:2023-07 Issue: Volume:163 Page:106138
ISSN:0925-7535
Container-title:Safety Science
language:en
Short-container-title:Safety Science

Author:

Luo Xixi,Li Xinchun,Goh Yang Miang,Song Xuefeng,Liu Quanlong

Funder

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality,Building and Construction

Reference53 articles.

1. Design for occupational safety and health: a theoretical framework for organisational capability;Adaku;Saf. Sci.,2021

2. The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: an evaluation for active commuting behavior;Ali;J. Transp. Health,2022

3. Quantifying workers' gait patterns to identify safety hazards in construction using a wearable insole pressure system;Antwi-Afari;Saf. Sci.,2020

4. Injury severity on traffic crashes: a text mining with an interpretable machine-learning approach;Arteaga;Saf. Sci.,2020

5. A bayesian solution to multicollinearity through unobserved common factors;Assaf;Tour. Manag.,2021

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hybrid approach integrating case mining (CM) and the Copula Bayesian Network (CBN) for accident causation probabilistic reasoning of building construction collapses;Reliability Engineering & System Safety;2024-12

2. A deep-learning approach to leveraging natural hazard indicators for improved safety on construction sites;Safety Science;2024-09

3. Prediction of number of insured having work accident in Turkish construction industry;Proceedings of the Institution of Civil Engineers - Management, Procurement and Law;2024-08-05

4. Prediction of accident risk in construction projects using data on safety and quality deviations from a Norwegian company;IOP Conference Series: Earth and Environmental Science;2024-08-01

5. Application of Deep Learning for Automatic Identification of Hazardous Materials and Urban Safety Supervision;Journal of Organizational and End User Computing;2024-07-30