Data-driven model for predicting intermediate crack induced debonding of FRP-strengthened RC beams in flexure

Li, Guibing; Hu, Tianyu; Shao, Yian; Bai, Dawei

doi:10.1016/j.istruc.2022.05.023

Cited by 4 publications

(1 citation statement)

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“…These cracks typically initiate at the point of higher bending moment (mid-span) and propagate toward the ends of the FRP plate, following the direction of decreasing bending moment. This behavior is commonly observed during IC debonding failure [10,11]. PE debonding is more likely to occur in FRP-strengthened concrete beams with a small shear span and where the mechanical behavior of the beam is primarily governed by shear forces instead of bending moments.…”

Section: Introductionmentioning

confidence: 92%

Interpretable Machine Learning-Based Prediction Model for Concrete Cover Separation of FRP-Strengthened RC Beams

Zheng,

Hu,

2024

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This study focuses on the prediction of concrete cover separation (CCS) in reinforced concrete beams strengthened by fiber-reinforced polymer (FRP) in flexure. First, machine learning models were constructed based on linear regression, support vector regression, BP neural networks, decision trees, random forests, and XGBoost algorithms. Secondly, the most suitable model for predicting CCS was identified based on the evaluation metrics and compared with the codes and the researcher’s model. Finally, a parametric study based on SHapley Additive exPlanations (SHAP) was carried out, and the following conclusions were obtained: XGBoost is best-suited for the prediction of CCS and codes, and researchers’ model accuracy needs to be improved and suffers from over or conservative estimation. The contributions of the concrete to the shear force and the yield strength of the reinforcement are the most important parameters for the CCS, where the shear force at the onset of CCS is approximately proportional to the contribution of the concrete to the shear force and approximately inversely proportional to the yield strength of the reinforcement.

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Section: Introductionmentioning

confidence: 92%