An Effective Artificial Intelligence Approach for Slope Stability Evaluation

Abstract

In this study, an effective intelligent system based on artificial neural networks (ANN) and a new version of the sine cosine algorithm (SCA) is developed to evaluate and predict the FOS of homogenous slopes under static and dynamic loading. In the first step, an effective hybrid optimization algorithm based on the adaptive sine cosine algorithm (ASCA) and pattern search (PS), namely ASCPS, is proposed and verified using a set of benchmark test functions. Then, the new algorithm, along with the Morgenstern and Price method is applied for seismic slope stability evaluation. To provide a neural network training dataset, a set of 189 slopes with different values of slope height, slope angle, friction angle of soil, soil cohesion, and horizontal acceleration coefficient have been analyzed and their corresponding FOS have been recorded. In the next step, the proposed ASCPS algorithm is implemented for training the ANN model using the collected database. The performance and prediction capacity of the developed model are evaluated using root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with the RMSE value of 0.023 and the R value of 0.984 is a reliable, simple, and valid computational model for estimating the FOS and evaluating the slope stability under static and earthquake loads. In addition, the developed ANN model is applied to a case study of slope stability from previous studies, and the results reveal that the proposed model may provide better optimal solutions and outperform existing methods.

Golden Search Optimization Algorithm

“…There are other algorithms that are a combination or modification of algorithms in these four categories. Some of them are: modified particle swarm optimization [36], [37], modified harmony search algorithm [38], modified gravitational search algorithm [39], [40], modified ant colony optimization [41], modified sine cosine algorithm [42], [43], modified wild horse optimization [44], modified slime mould algorithm [45], hybrid genetic algorithm and particle swarm optimization [46], hybrid firefly algorithm [47], hybrid sperm swarm optimization and gravitational search algorithm [48], hybrid tunicate swarm algorithm and pattern search [49], hybrid arithmetic optimization algorithm and sine cosine algorithm [50].…”

Stability Prediction of Residual Soil and Rock Slope Using Artificial Neural Network

“…roughout the training and testing phases, the ANN is trained on what things to search for and what its output ought to look like, utilising binary yes/no query types [24]. e activation function is imperious for an ANN to learn and build the logic of some genuinely complex problem.…”

Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations

“…Two-way shear: The tendency of the column to punch through the footing slab is called punching shear. According to Equation (10), the maximum shearing force in the upward direction (V u ) should be less than the nominal punching shear strength to avoid such a failure.…”

“…This enables the evaluation of non-linear correlations between any of the soil and foundation characteristics, as well as provides faster and more accurate results than earlier techniques. ANNs have recently been used to solve a variety of geotechnical engineering applications such as bearing capacity estimation [6,7], rock burst hazard prediction in underground projects [8], slope stability evaluation [9][10][11], concrete compressive strength prediction [12], and estimation of rock modulus [13]. This suggests that ANNs can be utilized for forecasting as well as prediction of events by simulating exceedingly complex functions [14].…”