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

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...

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Published inScientific reports Vol. 11; no. 1; pp. 15061 - 20
Main Authors Amini Pishro, Ahad, Zhang, Shiquan, Huang, Dengshi, Xiong, Feng, Li, WeiYu, Yang, Qihong
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 23.07.2021
Nature Publishing Group
Nature Portfolio
Subjects
639/166/986
639/705/1041
639/705/1042
Algorithms
Humanities and Social Sciences
multidisciplinary
Neural networks
Regression analysis
Science
Science (multidisciplinary)
Steel
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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 ( d b ) of 12, 14, 16, 18, and 20, concrete compressive strength ( f c ′ ), bond lengths ( L ), and concrete covers ( C ) of d b , 2 d b , 3 d b and 4 d b 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.
AbstractList 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 ( $$d_{b} )$$ d b ) of 12, 14, 16, 18, and 20, concrete compressive strength ( $$f_{c}^{\prime }$$ f c ′ ), bond lengths ( $$L$$ L ), and concrete covers ( $$C$$ C ) of $$d_{b}$$ d b , $$2d_{b}$$ 2 d b , $$3d_{b}$$ 3 d b and $$4d_{b}$$ 4 d b 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.
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 ( d b ) of 12, 14, 16, 18, and 20, concrete compressive strength ( f c ′ ), bond lengths ( L ), and concrete covers ( C ) of d b , 2 d b , 3 d b and 4 d b 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.
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.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.
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 (db) of 12, 14, 16, 18, and 20, concrete compressive strength (fc′), bond lengths (L), and concrete covers (C) of db, 2db, 3db and 4db 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.
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 ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{b} )$$\end{document} d b ) of 12, 14, 16, 18, and 20, concrete compressive strength ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f_{c}^{\prime }$$\end{document} f c ′ ), bond lengths ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L$$\end{document} L ), and concrete covers ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document} C ) of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{b}$$\end{document} d b , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2d_{b}$$\end{document} 2 d b , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3d_{b}$$\end{document} 3 d b and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4d_{b}$$\end{document} 4 d b 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.
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 ( $$d_{b} )$$ d b ) of 12, 14, 16, 18, and 20, concrete compressive strength ( $$f_{c}^{\prime }$$ f c ′ ), bond lengths ( $$L$$ L ), and concrete covers ( $$C$$ C ) of $$d_{b}$$ d b , $$2d_{b}$$ 2 d b , $$3d_{b}$$ 3 d b and $$4d_{b}$$ 4 d b 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.
ArticleNumber 15061
Author Yang, Qihong
Zhang, Shiquan
Huang, Dengshi
Li, WeiYu
Xiong, Feng
Amini Pishro, Ahad
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  surname: Amini Pishro
  fullname: Amini Pishro, Ahad
  organization: Department of Civil Engineering, Sichuan University of Science and Engineering
– sequence: 2
  givenname: Shiquan
  surname: Zhang
  fullname: Zhang, Shiquan
  organization: Sichuan University
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  givenname: Dengshi
  surname: Huang
  fullname: Huang, Dengshi
  organization: Southwest Jiaotong University
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  givenname: Feng
  surname: Xiong
  fullname: Xiong, Feng
  organization: Sichuan University
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  givenname: WeiYu
  surname: Li
  fullname: Li, WeiYu
  organization: Southwest Jiaotong University
– sequence: 6
  givenname: Qihong
  surname: Yang
  fullname: Yang, Qihong
  email: yangqh@stu.scu.edu.cn
  organization: Sichuan University
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SSID ssj0000529419
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Snippet 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...
Abstract We investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC)...
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SubjectTerms 639/166/986
639/705/1041
639/705/1042
Algorithms
Humanities and Social Sciences
multidisciplinary
Neural networks
Regression analysis
Science
Science (multidisciplinary)
Steel
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Title Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars
URI https://link.springer.com/article/10.1038/s41598-021-94480-2
https://www.proquest.com/docview/2554496816
https://www.proquest.com/docview/2555108601
https://pubmed.ncbi.nlm.nih.gov/PMC8302721
https://doaj.org/article/0fb64eae79c34c038edcc640e0acf70c
Volume 11
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