Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques.

Dieu Tien Bui,Hossein Moayedi,Mu’Azu Mohammed Abdullahi,Ahmad Safuan A Rashid,Hoang Nguyen

doi:10.3390/s19173678

Abstract

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.

Highlights

Belled piles constructed from concrete and have been designed to raise the bearing capacity of embedded piles
Yilmaz et al [16] have studied artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) to predict the permeability of coarse-grained soils
We have described an algorithm of the Radial Basis Function Based Neural Network (RBNN) and the basic concept as follows: We have introduced a nonlinear function h(x,t)

Summary

Introduction

Belled piles constructed from concrete and have been designed to raise the bearing capacity of embedded piles. Different computational models have been used to analyze the pile behavior in various independent loadings, lateral loadings, vertical-uplift, and vertical compressive [1,2,3,4,5], besides, the forecasting of the (1) bearing capacity of pile foundation [6,7]; (2) uplift capacity of suction caisson [8]; (3) pile dynamic capacity [9,10]; (4) pile setup [11]; and (5) pile settlements [12] has defined artificial neural network (ANN) to forecast the pullout capacity of suction foundations through the applying of a Sensors 2019, 19, 3678; doi:10.3390/s19173678 www.mdpi.com/journal/sensors. Thomas et al [19]

Objectives

Methods

Results

Conclusion