On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams

Thuy-Anh Nguyen,Hai-Bang Ly,Van Quan Tran,Hai-Van Thi Mai,Zhen Zhang

doi:10.1155/2021/5548988

Abstract

This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and gradient descent (ANN-GD). A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms’ evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the ANN-CG model has the best prediction performance with R = 0.992, RMSE = 14.02, MAE = 14.24, and MAPE = 6.84. The results of this study show that the ANN-CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers.

Highlights

Deep beams are defined as load-bearing structural elements in the form of simple beams, in which a considerable amount of load is transferred to the supports by a combined compression force of load and jet
The number of input and output representing deep beams’ shear strength is equal to 9 and 1, respectively. erefore, 10 neurons in the hidden layer artificial neural network (ANN) is proposed. e sigmoid activation function for the hidden layer is selected, while the activation function for the output layer is a linear function. e cost function has been chosen as the mean square error one
A database of 106 results from shear tests of reinforced concrete (RC) deep beams is built from the available literature. e ANN model is built with 9 input parameters divided into two groups, namely, the geometric size parameter group and the parameter group representing the material properties

Summary

Introduction

Deep beams are defined as load-bearing structural elements in the form of simple beams, in which a considerable amount of load is transferred to the supports by a combined compression force of load and jet. Deep beams are characterized by a larger beam depth compared to conventional beams, classified by the ratio of the length of the cut span to the beam depth (a/h) or on the ratio between calculated span length and beam height (l/h). According to IS Code 456-2000, the deep beam is defined by a ratio of effective span-to-overall depth (l/h), which does not exceed 2.0 for the simple beam and 2.5 for the continuous beam [1]. The ACI 318–14 [2] classifies a beam as a deep beam if it satisfies the following: (a) the spacing does not exceed four times of overall structural depth, or (b) the cutting span does not exceed twice the overall part depth.

Methods

Results

Conclusion