Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hybrid deep learning model (RNN-CNN-LSTM) optimized by Optuna to accurately predict the stability of Nano-silica (NS) stabilized slopes. The model achieves 99.4% accuracy, offering an efficient tool for geotechnical engineers.
Area Of Science
- Geotechnical Engineering
- Artificial Intelligence
- Material Science
Background
- Assessing infinite slope stability is crucial in geotechnical engineering.
- Traditional methods (LEM, FEM) are computationally intensive and struggle with non-linear soil stabilization effects.
- Nano-silica (NS) stabilization enhances soil properties and mechanical strength, necessitating advanced analysis techniques.
Purpose Of The Study
- To develop and validate a hybrid deep learning model for predicting the stability of Nano-silica (NS) stabilized infinite slopes.
- To optimize the model using the Optuna algorithm for enhanced predictive performance.
- To improve model interpretability using Explainable AI (XAI) and SHAP techniques.
Main Methods
- A hybrid deep learning model integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) was developed.
- The model was optimized using the Optuna algorithm.
- Explainable AI (XAI) and SHAP techniques were applied for feature importance analysis.
Main Results
- The optimized RNN-CNN-LSTM model achieved a 99.4% accuracy on unseen test data.
- Stable validation trends and robust predictive performance were observed.
- Cohesion (c), Nano-Silica Content (NS%), and Slope Angle (β) were identified as the most influential factors for slope stability.
Conclusions
- Hybrid deep learning models, optimized and interpretable, offer a powerful and efficient tool for geotechnical engineers to assess slope stability.
- The proposed framework reduces computational effort and improves predictive accuracy compared to conventional methods.
- The model can be integrated into real-time early warning systems for enhanced landslide risk assessment and infrastructure resilience.
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