Behrouz Gordan

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

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 (R2) 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. R2 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.

A Monte Carlo technique in safety assessment of slope under seismic condition

In geotechnical engineering, stabilization of slopes is one of the significant issues that needs to be considered especially in seismic situation. Evaluation and precise prediction of factor of safety (FOS) of slopes can be useful for designing/analyzing very important structures such as dams and highways. Hence, in the present study, an attempt has been done to evaluate/predict FOS of many homogenous slopes in different conditions using Monte Carlo (MC) simulation technique. For achieving this aim, the most important parameters on the FOS were investigated, and finally, slope height (H), slope angle (α), cohesion (C), angle of internal friction (

A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls

\varnothing

Excellent performance of earth dams under resonance motion using isolator damping layer

∅) and peak ground acceleration (PGA) were selected as model inputs to estimate FOS values. In the first step of analysis, a multiple linear regression (MLR) equation was developed and then it was used for evaluation and prediction by MC technique. Generally, MC model simulated FOS of less than 1.18, lower and higher than measured and predicted FOS values, respectively. However, the results of MC simulation for the FOS values of more than 1.33, is higher than those measured and predicted FOS values. As a result, the mean of FOS values simulated by MC was very close to the mean of actual FOS values. Moreover, results of sensitivity analysis demonstrated that the (

Strength Performance Based on Flexibility from Laterite Soil Using Tire Powder and Micro Silica

\varnothing

Effect of Material Property in Foundation during Earthquake on the Embankment

∅), among other parameters, is the most effective one on FOS. The obtained results indicated that MC is a reliable approach for evaluating and estimating FOS of slopes with high degree of performance.