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<p>This study presents a novel approach to predicting the factor of safety (FOS) for infinite slopes stabilized with nano-silica (NS), leveraging machine learning (ML) models to address limitations of traditional geotechnical assessments. A unique dataset, consisting of 1053 samples, was compiled to capture critical parameters, including NS content and curing time, thereby enhancing model accuracy across diverse conditions. Six ML models were evaluated, with the gradient boosting (GB) model emerging as the most robust, achieving an <i>R</i><sup>2</sup> value of 0.99, mean absolute error of 0.03 and root mean squared error of 0.04. A reliability analysis quantified a failure probability of 29.66% and a reliability index of 1.64 for NS-treated soils, underscoring the model’s applicability in real-world geotechnical design. Additionally, a parabolic regression equation was derived, offering practitioners a reliable tool for FOS estimation. To facilitate practical implementation, an intuitive graphical user interface (GUI) was developed, allowing for accurate FOS predictions based on user-defined inputs. This study provides a comprehensive, data-driven model for NS-stabilized slopes, advancing the field with a user-friendly predictive tool that supports sustainable engineering practices in challenging geotechnical environments.</p>

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Geotechnical Characterization and Stability Prediction of Nano-Silica-Stabilized Slopes: A Machine Learning Approach to Mitigating Geological Hazards

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月4日

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  • Ishwor Thapa,
  • Sufyan Ghani,
  • Sunita Kumari,
  • A. K. Choudhary,
  • Tryfon Sivenas,
  • Panagiotis G. Asteris

摘要

This study presents a novel approach to predicting the factor of safety (FOS) for infinite slopes stabilized with nano-silica (NS), leveraging machine learning (ML) models to address limitations of traditional geotechnical assessments. A unique dataset, consisting of 1053 samples, was compiled to capture critical parameters, including NS content and curing time, thereby enhancing model accuracy across diverse conditions. Six ML models were evaluated, with the gradient boosting (GB) model emerging as the most robust, achieving an R2 value of 0.99, mean absolute error of 0.03 and root mean squared error of 0.04. A reliability analysis quantified a failure probability of 29.66% and a reliability index of 1.64 for NS-treated soils, underscoring the model’s applicability in real-world geotechnical design. Additionally, a parabolic regression equation was derived, offering practitioners a reliable tool for FOS estimation. To facilitate practical implementation, an intuitive graphical user interface (GUI) was developed, allowing for accurate FOS predictions based on user-defined inputs. This study provides a comprehensive, data-driven model for NS-stabilized slopes, advancing the field with a user-friendly predictive tool that supports sustainable engineering practices in challenging geotechnical environments.