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Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices.

Guimei Wang1,2, Jianliang Zhou1

  • 1School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China.

Computational Intelligence and Neuroscience
|June 27, 2022
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Summary

This study introduces a novel safety evaluation model for smart construction devices using lightweight neural networks. The model accurately predicts construction site accidents, enhancing safety efficiency and practical value.

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Area of Science:

  • Artificial Intelligence
  • Construction Safety Engineering

Background:

  • Smart construction devices require robust safety evaluation methods.
  • Existing models may lack the precision for real-time accident prediction.

Purpose of the Study:

  • To develop and validate a lightweight neural network-based safety evaluation model for smart construction devices.
  • To refine input and output indexes for a comprehensive safety assessment.

Main Methods:

  • Utilized lightweight neural network theory for model development.
  • Designed a detailed index system with refined input factors.
  • Simulated the model using MATLAB, selecting optimal network structures and parameters.
  • Trained the model with 10 expert evaluation samples.

Main Results:

  • The neural network model demonstrated good evaluation performance.
  • Trained models applied to real construction cases showed minimal data discrepancies.
  • The method achieved fast calculation speeds.

Conclusions:

  • The proposed model effectively improves the efficiency and practical value of construction safety evaluation.
  • Lightweight neural networks offer a promising approach for predicting construction site accidents.