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Related Experiment Videos

Multi-strategy quantum-inspired dung beetle optimizer for kernel extreme learning machine in slope safety factor

Zhiyuan Sun1, Mingwei Hai1,2, Miao Wang3

  • 1Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin, 150080, China.

Scientific Reports
|June 11, 2026
PubMed
Summary

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The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...

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This study introduces a novel framework for predicting slope safety factors (FS) using a Quantum-inspired Dung Beetle Optimizer with Kernel Extreme Learning Machine (QHDBO-KELM). The method enhances accuracy and robustness in geotechnical engineering, even with limited data.

Area of Science:

  • Geotechnical Engineering
  • Computational Intelligence
  • Machine Learning

Background:

  • Accurate slope safety factor (FS) prediction is vital for mining and geotechnical risk assessment.
  • Practical applications face challenges due to small, heterogeneous datasets and model hyperparameter sensitivity.

Purpose of the Study:

  • To develop a quantitative FS prediction framework using Kernel Extreme Learning Machine (KELM) optimized by a Quantum-inspired Dung Beetle Optimizer (QHDBO).
  • To tune key hyperparameters (regularization coefficient, kernel parameter) in logarithmic space for improved prediction accuracy.

Main Methods:

  • Utilized five geotechnical and geometric variables (unit weight, cohesion, friction angle, slope angle, slope height) as inputs for FS prediction.
  • Employed a multi-strategy quantum-inspired dung beetle optimizer (QHDBO) to optimize KELM hyperparameters.
Keywords:
Hyperparameter optimizationKernel extreme learning machineRepeated nested cross-validationSafety factorSlope stability

Related Experiment Videos

  • Ensured reliable generalization via strict no-leakage repeated nested cross-validation (5-fold outer CV repeated 30 times).

    • Main Results:

    • The QHDBO-KELM framework demonstrated superior accuracy and robustness compared to baseline KELM and other benchmark models (RF, GBDT, MLP, SVR).
    • Achieved lower prediction errors (RMSE, MAE, MAPE) and higher goodness-of-fit (R²) across diverse data partitions.
    • Statistical significance tests confirmed the proposed model's improvements over 150 outer folds.

    Conclusions:

    • The QHDBO-KELM framework offers an efficient and dependable surrogate tool for slope safety factor prediction.
    • The approach shows promise for extension to other geotechnical stability-related regression tasks.
    • Highlights the effectiveness of quantum-inspired optimization for hyperparameter tuning in geotechnical machine learning models.