Slope stability prediction of road embankment using artificial neural network combined with genetic algorithm

• Main Authors: Rufaizal Che Mamat, Azuin Ramli, Muhamad Razuhanafi Mat Yazid, Anuar Kasa, Siti Fatin Mohd Razali, Bastam, Mukhlis Nahriri
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2022
• Online Access: http://journalarticle.ukm.my/18746/1/16.pdf; http://journalarticle.ukm.my/18746/; https://www.ukm.my/jkukm/volume-3401-2022/
• Institution: Universiti Kebangsaan Malaysia

The prediction of slope stability was performed using artificial neural networks (ANNs) in this work. The factor of safety determined by numerical analysis was used to develop ANN’s data sets. The inputs to the network are slope height, applied surcharge and slope angle. Correlation coefficients between numerical data and ANNs outputs showed the feasibility of ANNs for successfully modelling and predicting safety issues. The ANNs training phase is improved using a genetic algorithm (GA), and the results are compared to those obtained without GA trained ANNs. A sensitivity analysis is conducted to ascertain the relative contribution of different factors on slope stability. The slope angle and applied surcharge have a significant effect on slope stability.