Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm

• Extreme learning algorithm is used to train rock slope stability evaluation system.
• Terminal steepest descent algorithm (TSDA) based on finite-time theory is adopted.
• The developed package can either estimate slope stability or perform back analyses.
• The errors converge to zero which makes analyses more accurate and time-effective.
• Multiple parameters can be optimized simultaneously during the back analysis.

The analysis of rock slope stability is a classical problem for geotechnical engineers. However, for practicing engineers, proper software is not usually user friendly, and additional resources capable of providing information useful for decision-making are required. This study developed a convenient tool that can provide a prompt assessment of rock slope stability. A nonlinear input–output mapping of the rock slope system was constructed using a neural network trained by an extreme learning algorithm. The training data was obtained by using finite element upper and lower bound limit analysis methods. The newly developed techniques in this study can either estimate the factor of safety for a rock slope or obtain the implicit parameters through back analyses. Back analysis parameter identification was performed using a terminal steepest descent algorithm based on the finite-time stability theory. This algorithm not only guarantees finite-time error convergence but also achieves exact zero convergence, unlike the conventional steepest descent algorithm in which the training error never reaches zero.