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Teaching-learning-based optimisation algorithm and its application in capturing critical slip surface in slope stability analysis
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DOI：10.1007/s00500-019-04075-3.png)
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Abstract
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The identification of ideal values for algorithm-specific parameters required for the functioning of metaheuristic approaches at their optimum performance is a difficult task. This paper presents the application of a recently proposed teaching-learning-based optimisation (TLBO) algorithm to determine the lowest factor of safety (FS) along a critical slip surface for soil slope. TLBO is a nature-inspired search algorithm based on the teaching-learning phenomenon of a classroom. Four benchmark slopes are reanalysed to test the performance of the TLBO approach. The results indicate that the present technique can detect the critical failure surface and can be easily implemented by practitioners without fine-tuning the parameters that affect the convergence of results. Statistical analyses indicate a drastic decrease in uncertainty and the number of function evaluations in the estimation of the FS over previous approaches.
Keywords:
Teaching-learning-based optimisation
Landslide
Slope stability analysis
Critical failure surface
Swarm intelligence
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