Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars

Ahad Amini Pishro; Shiquan Zhang; Dengshi Huang; Feng Xiong; WeiYu Li; Qihong Yang

doi:10.1038/s41598-021-94480-2

Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars

Sci Rep. 2021 Jul 23;11(1):15061. doi: 10.1038/s41598-021-94480-2.

Authors

Ahad Amini Pishro¹, Shiquan Zhang², Dengshi Huang³, Feng Xiong², WeiYu Li³, Qihong Yang⁴

Affiliations

¹ Department of Civil Engineering, Sichuan University of Science and Engineering, Zigong, China.
² Sichuan University, Chengdu, China.
³ Southwest Jiaotong University, Chengdu, China.
⁴ Sichuan University, Chengdu, China. yangqh@stu.scu.edu.cn.

Abstract

We investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC) and steel bars, in order to evaluate the accuracy of our LBS equation, proposed by Multiple Linear Regression (MLR). The experimental and numerical LBS results of specimens, based on RILEM standards and using pullout tests, were assessed by the ANN algorithm using the TensorFlow platform. For each specimen, steel bar diameters ([Formula: see text] of 12, 14, 16, 18, and 20, concrete compressive strength ([Formula: see text]), bond lengths ([Formula: see text]), and concrete covers ([Formula: see text]) of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] were used as input parameters for our ANN. To obtain an accurate LBS equation, we first modified the existing formula, then used MLR to establish a new LBS equation. Finally, we applied ANN to verify our new proposed equation. The numerical pullout test values from ABAQUS and experimental results from our laboratory were compared with the proposed LBS equation and ANN algorithm results. The results confirmed that our LBS equation is logically accurate and that there is a strong agreement between the experimental, numerical, theoretical, and the predicted LBS values. Moreover, the ANN algorithm proved the precision of our proposed LBS equation.