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Constrained Particle Swarm Optimization Algorithm Applied to Slope Stability
B. Jellali,Wissem Frikha +1 more
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This article addresses the problem of the stability of a slope within the framework of the yield design theory (YDT), which is a rigorous method that avoids assumptions that could affect the instability of the slope.Abstract:
This article addresses the problem of the stability of a slope within the framework of the yield design theory (YDT), which is a rigorous method that avoids assumptions that could affect th...read more
Citations
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A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability
TL;DR: A novel metaheuristic optimization namely Harris hawks’ optimization (HHO) is introduced for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations.
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Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm.
TL;DR: The results confirmed that the proposed M5Rules–GA model is a robust tool for analyzing slope stability and the other investigated models yielded less robust performance under the evaluation metrics.
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The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes
TL;DR: This study demonstrates the efficiency of the proposed neuro model of PSO-ANN in estimating the factor of safety compared to other conventional techniques.
Journal ArticleDOI
35 Years of (AI) in Geotechnical Engineering: State of the Art
TL;DR: The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researched.
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The performance of six neural-evolutionary classification techniques combined with multi-layer perception in two-layered cohesive slope stability analysis and failure recognition
Chao Yuan,Hossein Moayedi +1 more
TL;DR: Six population-based hybrid algorithms are applied to train the multilayer perceptron (MLP) to improve the classification accuracy, in the stability assessment, and revealed the BBO-MLP with the obtained area under the receiving operating characteristic curve (AUROC) of 0.995 and the classification ratio (CR) of 92.4% is the most accurate model.
References
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Journal ArticleDOI
Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art
TL;DR: A comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms, including approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies.
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Slope stability analysis by finite elements
D. V. Griffiths,P. A. Lane +1 more
TL;DR: In this article, the authors describe several examples of finite element slope stability analysis with comparison against other solution methods, including the influence of a free surface on slope and dam stability, and Graphical output is included to illustrate deformations and mechanisms of failure.
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The analysis of the stability of general slip surfaces
TL;DR: In this paper, a method for statically determinating the shape of a slip surface is presented, and the assumptions necessary to make the problem statically determinate are discussed; the solution of the governing equations ensures that all equilibrium and boundary conditions are satisfied.
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Constrained optimization via genetic algorithms
TL;DR: The proposed search algorithm is realized by GAs which utilize a penalty function in the objective function to account for violation, based on systematic multi-stage assignments of weights in the penalty method as opposed to single- stage assignments in sequential unconstrained minimization.