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Predicting slope safety using an optimized machine learning model.

Mohammad Khajehzadeh1,2, Suraparb Keawsawasvong1

  • 1Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand.

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This summary is machine-generated.

This study introduces a hybrid artificial intelligence model to accurately predict earth slope safety. The novel approach enhances slope stability analysis, improving prediction accuracy by 7% compared to traditional methods.

Keywords:
Artificial electric fieldMachine learningSlope stabilitySupport vector regression

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Area of Science:

  • Geotechnical Engineering
  • Artificial Intelligence
  • Computational Mechanics

Background:

  • Slope collapse poses significant hazards, necessitating accurate prediction tools for safety.
  • Traditional slope stability analysis methods often lack precision.
  • Developing advanced forecasting models is crucial for mitigating risks associated with slope failures.

Purpose of the Study:

  • To develop a hybrid machine learning model for precise estimation of the factor of safety (FOS) in earth slopes.
  • To introduce a novel optimization algorithm, the global-best artificial electric field algorithm (GBAEF), for enhancing machine learning model performance.
  • To validate the efficacy of the hybrid model against traditional techniques using a real-world case study.

Main Methods:

  • Development and verification of the global-best artificial electric field algorithm (GBAEF) using benchmark functions.
  • Implementation of support vector regression (SVR) for predicting the factor of safety (FOS).
  • Optimization of SVR hyper-parameters using the proposed GBAEF to create a hybrid model.

Main Results:

  • The hybrid GBAEF-SVR model demonstrated superior accuracy in FOS prediction, improving results by approximately 7% over conventional methods.
  • The model achieved high performance in both training (R² = 0.9633) and testing (R² = 0.9242), indicating a strong correlation between predicted and observed FOS.
  • The optimization of SVR hyper-parameters significantly enhanced prediction accuracy, highlighting the effectiveness of the GBAEF algorithm.

Conclusions:

  • The hybrid AI model offers a significant advancement in earth slope stability analysis.
  • The GBAEF algorithm effectively optimizes machine learning models for improved predictive accuracy in geotechnical applications.
  • Accurate FOS prediction using advanced AI techniques is vital for effective hazard mitigation and infrastructure safety.