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Evaluating Construction Equipment Accident Risk by Analyzing Utilization and Costs Using Regression Models.

Minwoo Song1, Jaewook Jeong1, Jaehyun Lee2

  • 1Department of Safety Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.

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Summary
This summary is machine-generated.

This study developed a regression model to predict construction equipment accidents using cost and utilization data. The model identifies excavators as posing the highest fatality risk, aiding safety management.

Keywords:
accident riskconstruction equipmentregression analysisutilization rate

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Area of Science:

  • Construction Management
  • Occupational Safety and Health
  • Risk Analysis

Background:

  • Construction equipment is essential but poses significant accident risks.
  • Technological advancements increase equipment demand and introduce new safety challenges.
  • Quantitative analysis of accident likelihood is crucial for effective safety management.

Purpose of the Study:

  • To develop a quantitative accident prediction model for construction equipment.
  • To analyze the influence of utilization rates, subcontractor types, and costs on accident likelihood.
  • To provide a tool for enhanced safety management and investment decisions.

Main Methods:

  • Data collection and classification of construction equipment usage.
  • Calculation of hourly operating costs (HOC) and overall construction costs.
  • Application of data augmentation techniques (multivariate normal and Poisson distributions) for regression analysis.

Main Results:

  • Regression analysis yielded high R-squared values (>0.6) for most equipment types.
  • Dump trucks showed the highest historical fatality frequency.
  • The prediction model indicated excavators as having the highest projected fatality count.

Conclusions:

  • The proposed model effectively categorizes risk groups based on operating and construction costs.
  • The model offers a practical framework for field application in safety management.
  • This research supports the development of construction equipment safety regulations and investment strategies.