Stability analysis of a homogeneous rock slope under steady-state seepage using artificial neural networks

An accurate and effective procedure to determine the stability of rock slopes is paramount during the design and, even more so, during the management and maintenance of transportation infrastructures, when the rock slopes should be continuously monitored and checked against changing environmental conditions. The stability of rock slopes may be assessed using stability charts or limit equilibrium methods that assume an equivalent linear Mohr-Coulomb failure criterion for the rock or numerical models that can deal with complex configurations and rock behavior but demand more computational resources, expertise, and advanced software. In this study, an artificial neural network (ANN) was used to predict the safety factor of a two-dimensional, homogeneous rock slope incorporating several critical factors simultaneously, such as the rock's non-linear failure behavior, rock dilatancy, and different levels of the groundwater table associated with steady-state seepage flow through the rock. This greatly extends previous contributions in the field. A two-hidden layers ANN was trained using thousands of numerical simulations applying the Discontinuity Layout Optimization model, considering the rock mass toughness coefficient, non-dimensional height of the slope, artificial slope angle, dilatancy, and non-dimensional water level position. The ANN predictions closely matched the numerical results, demonstrating its potential as an easier and more accessible method for accurately evaluating the safety factor of rock slopes and as a reliable alternative to traditional numerical methods within specified input ranges. Its implementation is straightforward, using simple equations provided in the article, and it is easily scalable to perform intensive tasks and help complex engineering decision-making.