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Due to frequent climate-induced collapses in the railway track substructure, engineers must provide engineering solutions using more robust techniques beyond traditional fieldwork and GIS mapping. Machine learning (ML) techniques have recently been increasingly applied in geotechnical engineering. In this paper, we compare the performance of three algorithms: Particle Swarm Optimization (PSO), Bayesian Optimization (BOA), and Grid Search Optimization (GSO) for the autonomous evaluation of karst deformation along the railway track. Our assessment revealed the performance of the models as follows: PSO showed R2 = 0.959, RMSE = 1.265, and MAE = 0.882; BOA established R2 = 0.965, RMSE = 0.958, and MAE = 0.801; and GSO exhibited R2 = 0.950, RMSE = 1.389, and MAE = 0.976. These results indicate that BOA, having the highest R2 and minimal error values of RMSE and MAE, demonstrated the most optimal capacity for predicting karst deformation under the influence of large climate indices along the railway track.

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Predicting Karst Deformation from Climate Indices Using Hybrid Multi-layer Perceptron (MLP) Model

  • 会议论文

  • 发表日期：

    2025年

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  • Xu Linrong,
  • Bamaiyi Usman Aliyu,
  • Wang min,
  • Al-Amin Danladi Bello,
  • Musa Inusa,
  • Yuanxingzi He

摘要

Due to frequent climate-induced collapses in the railway track substructure, engineers must provide engineering solutions using more robust techniques beyond traditional fieldwork and GIS mapping. Machine learning (ML) techniques have recently been increasingly applied in geotechnical engineering. In this paper, we compare the performance of three algorithms: Particle Swarm Optimization (PSO), Bayesian Optimization (BOA), and Grid Search Optimization (GSO) for the autonomous evaluation of karst deformation along the railway track. Our assessment revealed the performance of the models as follows: PSO showed R2 = 0.959, RMSE = 1.265, and MAE = 0.882; BOA established R2 = 0.965, RMSE = 0.958, and MAE = 0.801; and GSO exhibited R2 = 0.950, RMSE = 1.389, and MAE = 0.976. These results indicate that BOA, having the highest R2 and minimal error values of RMSE and MAE, demonstrated the most optimal capacity for predicting karst deformation under the influence of large climate indices along the railway track.