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Risk assessment of bridge construction investigated using random forest algorithm.

Ying Wu1,2, Yigang Wang3, Hongbing Liu4

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This study introduces a machine learning approach using the Random Forest algorithm to accurately assess bridge construction risks. It identifies key factors influencing safety, improving upon subjective traditional methods for better accident prevention.

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Area of Science:

  • Civil Engineering
  • Risk Management
  • Machine Learning Applications

Background:

  • Bridge construction safety is a critical concern, with current risk evaluations often hindered by subjective assessor experience.
  • Existing methods struggle to identify key factors for accident prevention in bridge construction.
  • The need for objective, accurate, and efficient risk assessment in bridge construction is paramount.

Purpose of the Study:

  • To develop an objective and accurate method for evaluating bridge construction risks.
  • To identify and rank the most influential risk factors in bridge construction.
  • To overcome the limitations of subjective expert-based assessments in bridge safety.

Main Methods:

  • Analysis and classification of 26 artificial and environmental risk factors in bridge construction.
  • Application of the Random Forest (RF) algorithm, a non-parametric machine learning method.
  • Validation of the proposed method using a case study of an urban complex pedestrian bridge.

Main Results:

  • The Random Forest algorithm provided accurate and robust risk assessment results, unaffected by expert subjectivity.
  • The study successfully ranked the importance of various risk indicators.
  • Key influential factors impacting bridge construction risk were identified.

Conclusions:

  • The proposed Random Forest-based method offers a feasible and effective solution for objective bridge construction risk assessment.
  • This approach enhances the ability to identify critical risk factors, contributing to improved safety management.
  • The findings are consistent with real-world risk assessments, demonstrating practical applicability.