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人工神经网络与多元线性回归在超高性能混凝土（UHPC）与钢筋局部粘结应力方程中的应用。

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

作者信息

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

机构信息

Department of Civil Engineering, Sichuan University of Science and Engineering, Zigong, China.

Sichuan University, Chengdu, China.

出版信息

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

DOI:10.1038/s41598-021-94480-2
PMID:34302020
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8302721/
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.

摘要

我们研究了使用人工神经网络（ANN）来预测超高性能混凝土（UHPC）与钢筋之间的局部粘结应力（LBS），以评估我们通过多元线性回归（MLR）提出的LBS方程的准确性。基于RILEM标准并使用拔出试验的试件的实验和数值LBS结果，由使用TensorFlow平台的ANN算法进行评估。对于每个试件，钢筋直径（分别为12、14、16、18和20）、混凝土抗压强度、粘结长度以及混凝土保护层厚度分别为[公式：见原文]、[公式：见原文]、[公式：见原文]和[公式：见原文]，这些被用作我们ANN的输入参数。为了获得准确的LBS方程，我们首先修改了现有公式，然后使用MLR建立了一个新的LBS方程。最后，我们应用ANN来验证我们新提出的方程。将ABAQUS的数值拔出试验值和我们实验室的实验结果与提出的LBS方程和ANN算法结果进行了比较。结果证实我们的LBS方程在逻辑上是准确的，并且实验、数值、理论和预测的LBS值之间有很强的一致性。此外，ANN算法证明了我们提出的LBS方程的精度。

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