Slope failures represent intricate natural events that can lead to serious hazards across various regions, impacting both the environment and human activities. Their complexity arises from factors such as geological conditions, weather patterns, and human interventions, necessitating a comprehensive understanding for effective management. To assess the stability of earth slopes, engineers use a variety of computational techniques. These include limit equilibrium methods, which focus on force and moment balance; limit analysis, which determines the ultimate load-bearing capacity; finite element method (FEM) and finite difference method (FDM), both of which discretize the slope into smaller elements for detailed stress analysis; and boundary element method (BEM), which simplifies the problem by focusing on boundary conditions rather than the entire domain.
Artificial neural networks (ANNs) represent sophisticated problem-solving tools that are inspired by the
architecture of the human brain. These networks are comprised of interconnected nodes, or neurons, that process information and learn from data patterns, making them particularly effective for addressing complex challenges in geotechnical engineering. The application of ANNs in slope stability analysis has demonstrated promising outcomes. These models have the capability to adapt to nonlinear relationships
between input and output variables, a feature that traditional analytical methods often struggle to accommodate.
However, a significant challenge associated with the implementation of ANNs is the risk of overfitting. This phenomenon occurs when a model performs exceptionally well on training data but fails to generalize to unseen data. To mitigate this risk, it is crucial to carefully select input features, conduct thorough training with diverse datasets, and employ strategies such as early stopping during the training
process to prevent excessive fitting to the specific training set. To enhance the training of ANNs and minimize the risk of overfitting, techniques such as Bayesian regularization, which imposes a penalty for larger weights, are employed in conjunction with differential evolution algorithms. These approaches not only improve training efficiency but also enhance model generalization, ensuring robust performance across varying data sets.
The dataset employed in this research is characterized by a carefully balanced assortment of slope conditions, consisting of a total of 46 slopes—23 categorized as dry slopes and an equal number classified as wet slopes. Among these formations, 29 slopes have experienced failure, while 17 slopes have remained stable throughout the evaluation period. This diverse range of slope conditions is crucial for
developing robust predictive models, as it allows for better generalization across various environmental circumstances and increases the reliability of the findings.
To effectively train the Artificial Neural Network (ANN), a selection of critical input parameters was
identified, encompassing fundamental geotechnical factors that significantly impact slope stability. These parameters include the height of the slope, the unit weight of the earth materials involved, the cohesion and internal friction angle of the soil, the angle of the slope itself, and several pore pressure parameters. Each of these variables plays a pivotal role in influencing the overall stability of the slopes and was meticulously selected based on their relevance to geotechnical principles and practical applications in slope stability analysis.
The output data generated by the ANN consists of two key components: qualitative classifications and
quantitative predictions. The qualitative classifications categorize the slopes as either stable or having failed, providing essential insights into the overall health of each slope. Concurrently, the quantitative predictions yield specific calculations concerning the factor of safety for each slope, facilitating a detailed and comprehensive evaluation of slope conditions. This dual approach not only enhances the understanding of slope behavior but also aids in risk assessment and management in geotechnical engineering practices.
Two distinct types of Artificial Neural Network (ANN) models were meticulously developed to address the challenges of slope stability analysis. The first model was specifically designed to classify slopes as either stable or failed, employing a range of input features that capture the physical and environmental characteristics influencing slope behavior. The second model was engineered to quantitatively estimate the factor of safety, which serves as a critical measure in assessing the stability of slopes under varying conditions.
This dual approach not only facilitates immediate classifications needed for timely decision-making but
also allows for ongoing and detailed safety assessments, enhancing overall risk management strategies. In the course of this research, a comprehensive analysis was conducted to evaluate the performance of these ANN models. The models leveraging advanced training techniques—specifically those utilizing Bayesian
regularization and differential evolution algorithms—demonstrated significantly superior performance metrics in comparison to those trained using the more traditional Levenberg-Marquardt algorithm.
The remarkable findings underscore the importance of adopting sophisticated training methodologies to
optimize ANN capabilities in this context. The developed ANN models exhibited a robust ability to accurately predict both the stability state of the slopes—effectively distinguishing between stable and failed conditions—as well as estimating the corresponding factor of safety with considerable precision. Such high levels of accuracy in predictions represent a pivotal advancement in harnessing artificial intelligence for effective slope stability management and risk assessment. Ultimately, these models enhance our understanding and ability to manage slope stability, contributing to improved safety protocols and risk mitigation strategies in geotechnical engineering.
Reference
Das, S. K., Biswal, R. K., Sivakugan, N., & Das, B. (2011). Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences, 64, 201-210.