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Prediction of seismic slope stability through combination of particle swarm optimization and neural network

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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.

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Authors and Affiliations

  1. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Behrouz Gordan & Danial Jahed Armaghani

  2. Construction Research Alliance, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Mohsen Hajihassani

  3. Department of Mining, Tarbiat Modares University, Tehran, 14115-143, Iran

    Masoud Monjezi

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  1. Behrouz Gordan
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  2. Danial Jahed Armaghani
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  3. Mohsen Hajihassani
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  4. Masoud Monjezi
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Correspondence to Behrouz Gordan.

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Gordan, B., Jahed Armaghani, D., Hajihassani, M. et al. Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers 32, 85–97 (2016). https://doi.org/10.1007/s00366-015-0400-7

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