2024 : 4 : 13
Mohammadreza Motahari

Mohammadreza Motahari

Academic rank: Associate Professor
ORCID: https://orcid.org/0000-0002-2103-0204
Education: PhD.
ScopusId: 55621034700
Faculty: Engineering
Address: Arak University


Two novel combined systems for predicting the peak shear strength using RBFNN and meta‑heuristic computing paradigms
Peak shear strength, RBFNN, Optimization algorithms, Machine learning
Journal Engineering with Computers
Researchers Juncheng Gao ، Menad Nait Amar ، Mohammadreza Motahari ، Mahdi Hasanipanah ، Danial Jahed Armaghani


Effective prediction of the peak shear strength (PSS) is of crucial importance in evaluating the stability of a rock slope with interlayered rocks and has both theoretical and practical significance. This paper offers two novel prediction tools for the PSS prediction based on radial basis function neural network (RBFNN) and meta-heuristic computing paradigms. For this work, the gray wolf optimization (GWO) and ant colony optimization (ACO) algorithms were used to select the optimal parameters of RBFNN. Then, these two new models were compared with the gene expression programming (GEP) model. A total of 158 experimental data were used to train and test the proposed models using three input parameters, i.e., normal stress, compressive strength ratio of joint walls, and joint roughness coefficient. Finally, the computational result revealed that the RBFNN-GWO model, with the coefficient of determination (R2) of 0.997, produced a better convergence speed and higher accuracy compared with RBFNN-ACO and GEP models, with the R2 of 0.995 and 0.996, respectively. The RBFNNGWO model was found an efficient predictive tool that can help rock engineers in the slopes design processes.