Artificial Intelligence and Data Science
Online ISSN : 2435-9262
Volume 4, Issue 4
Displaying 1-1 of 1 articles from this issue
  • Kouji IGARASHI, Kazuhisa ABE
    2023 Volume 4 Issue 4 Pages 1-15
    Published: 2023
    Released on J-STAGE: December 28, 2023

    The issue of infrastructure deterioration has been an ongoing concern, and research based on the inspection results of bridges regarding the evaluation of structural damage has been continuously conducted. In this study, we employed a machine learning algorithm designed for classification prediction to forecast the extent of crack damage in concrete bridges. By utilizing this machine learning-based model for predicting crack damage, the aim is to extract the intricate factors influencing damage in concrete bridges, with a long-term goal of establishing efficient maintenance and management methods for these bridges. In this research, we implemented crack damage predictions for the main girders of concrete bridges using various machine learning algorithms. As a result, methods known as gradient boosting, specifically XGBoost and LightGBM, demonstrated commendable prediction accuracy. Additionally, through the predictive model using LightGBM, we conducted the selection of significant explanatory variables and visualized the impact on crack damage extent using SHAP, further discussing these findings.

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