ArticleScientific reports2021
Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bond Behavior and Critical Anchorage Length Prediction of Novel Negative Poisson's Ratio Bars Embedded in Ultra-High-Performance Concrete.Materials (Basel, Switzerland) · 2025Article
- Machine learning base models to predict the punching shear capacity of posttensioned UHPC flat slabs.Scientific reports · 2024Article
- Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams.Scientific reports · 2023Article
- Node, place, ridership, and time model for rail-transit stations: a case study.Scientific reports · 2022Article
- Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN.Materials (Basel, Switzerland) · 2022Article
- Structural Behavior of FRP-Retrofitted RC Beams under Combined Torsion and Bending.Materials (Basel, Switzerland) · 2022Article
- Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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