A TLBO-optimized artificial neural network for modeling axial capacity of pile foundations

Abstract

Due to a considerable level of uncertainty describing the pile–soil behavior, many pile capacity prediction methods have focused on correlation with in situ tests. In recent years, artificial neural networks (ANNs) have been applied successfully in many problems in geotechnical engineering, especially, axial pile capacity estimation for driven and drilled shaft piles. Training neural networks is a crucial task that needs effective optimization algorithms. The most popular algorithm is a back-propagation method (BP), which is based on a gradient descent that can trap in local minima. The paper proposes a new artificial neural network (ANN) in which the learning is performed using a recent teaching–learning-based optimization algorithm (TLBO), improving axial capacity predictions. The model is trained and validated on 479 data sets for a wide range of uncemented soils and pile configurations, obtained from the literature. Results show that the considered TLBO-ANN model outperforms other state-of-the-art models in the prediction accuracy and the generalization capability. For instance, we obtained a coefficient of determination $$R^2=0.941$$ R 2 = 0.941 and a variance accounted for $${\\text{VAF}} = 94.09\\%$$ VAF = 94.09 % for TLBO-ANN while $$R^2=0.871$$ R 2 = 0.871 and $${\\text{VAF}} = 87.31\\%$$ VAF = 87.31 % for the classical BP-ANN. In addition, error investigation with log-normal approaches demonstrates that the probability that predictions fall within a $$\\pm \\,25\\%$$ ± 25 % accuracy level for TLBO-ANN model is 0.93 and that for BP-ANN model is 0.75. The proposed TLBO-ANN model predicts pile capacity with more accuracy, less scatter, and higher reliability.