Probabilistic Design of Retaining Wall Using Machine Learning Methods

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential analyze the failure probability such a structure. To keep importance geotechnics on par with advancement in technology, implementation artificial intelligence techniques done for reliability analysis Designing structure based leads an economical design. Machine learning models used predicting factor safety wall Emotional Neural Network, Multivariate Adaptive Regression Spline, SOS–LSSVM. The First-Order Second Moment Method calculating index wall. In addition, these assessed results they produce, best model among concluded extensive field study future. overall performance evaluation through various accuracy quantification determined SOS–LSSVM as model. obtained show that calculated by AI methods differs from reference values less than 2%. These methodologies have made problems facile increasing precision result. Artificial has removed cumbersome calculations almost all acquainted fields disciplines. this evolved versions some older algorithms. This work aims clarify probabilistic approach toward designing structures, using simplify practical evaluations.