Prediction of seismic slope stability through combination of particle swarm optimization and neural network. (January 2016)

• Record Type: Journal Article
• Authors: Gordan, Behrouz; Jahed Armaghani, Danial; Hajihassani, Mohsen; Monjezi, Masoud
• Is Part Of: Engineering with computers. Volume 32:Number 1(2016)
• Page Start: 85
• Page End: 97
• Publication Date: 2016-01
• Subjects: Factor of safety -- Limit equilibrium method -- Artificial neural network -- Particle swarm optimization; Engineering design -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Conception technique -- Informatique -- Périodiques; Conception assistée par ordinateur -- Périodiques; Electronic journals; 620.00285
• DOI: 10.1007/s00366-015-0400-7
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R 2 ) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN.R 2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.