Consolidation Coefficient of Soil Prediction by Using Teaching Learning based Optimization with Fuzzy Neural Network

Abstract

A key factor in constructing buildings leaning on soft soil is the consolidating coefficient of the soil referred as Cv. It is a crucial lab-measured engineering parameter utilized during the design and verification of geotechnical structures. Nevertheless, experimental experiments take a lot of time and money. In this study, the is projected using Fuzzy Neural Network (FNN) with optimized feature selection using Teaching Learning-based Optimization, estimating Cv as the most crucial step (TLO), which has enhanced the quality of the prediction model by removing unnecessary characteristics and relying solely on crucial ones. The experimental results demonstrate that the projected FNN, followed by the Multi-layer Training algorithm Neural Network (MLP), Impact of changing Optimization (BBO), a support vector regression (SVR), Back - propagation algorithm Multi-layer Training algorithm Bayesian Network (Bp-MLP Neural Nets), has the highest predictive validity for the prediction of (Root Mean Squared Error (RMSE )= 0.379, Mean Absolute Error (MAE) = 0.26, and coefficient of determination r = 0.835). Hence, it can be said that even if all used models perform well in predicting the soil consolidation coefficient, the FNN-TLO performs the best.