Modeling of concrete-filled PVC tube columns confined with CFRP strips under uniaxial eccentric compression: machine learning and finite element approaches

条状物 有限元法 管(容器) 压缩(物理) 计算科学与工程 材料科学 结构工程 复合材料 同心的 古怪的 计算机科学 几何学 工程类 计算科学 数学
作者
Yan Wang,Maha El-Meligy,Haytham F. Isleem,Asmaa Hamed,Diyar N. Qader,Mohamed Sharaf,Pradeep Jangir,Arpita Arpita,Ghanshyam G. Tejani
出处
期刊:Journal of Big Data [Springer Science+Business Media]
卷期号:12 (1) 被引量:19
标识
DOI:10.1186/s40537-024-01058-6
摘要

This paper presents an analytical and finite element modeling (FEM) investigation on the carbon fiber reinforced polymer concrete-filled polyvinyl chloride tube (CCFPT) concrete columns under axial eccentric compression. The study involved collecting experimental data from 32 CFPT columns confined with CFRP from literature and modeling them using FEM in ABAQUS. A parametric study was conducted on 260 CCFRP concrete columns, examining various parameters such as eccentricity, number of CFRP layers, thickness of PVC tube, column slenderness ratio, CFRP spacing, thickness of CFRP strips, confined concrete strength, and concrete core diameter. The effects of these parameters on the ultimate load and strain capacity were analyzed. Analytical models were developed to express the confined concrete strength and strain as functions of the constituent properties and dimensionless confinement parameters. The findings revealed that increasing eccentricity significantly reduced the ultimate load (up to 45%) and strain (up to 67%) capacities. Adding more layers of CFRP increased strength and strain capacities by 25% when going from 2 to 3 layers at a 20 mm eccentricity. Thicker PVC tubes increased load capacity by preventing buckling, but had inconsistent effects on strain. Higher slenderness ratios decreased both capacities, particularly strain. Six machine learning models were employed to predict the load-carrying capacity and confined ultimate strain. Various performance metrics, data visualization techniques, SHAP analysis, sensitivity analysis, and error characteristic curves were used to evaluate the prediction performance and analyze the impact of input parameters. The findings revealed that increasing eccentricity and CFRP layers lead to reduced ultimate load and strain capacities, while higher slenderness ratios result in increased ultimate loads. The study concluded that CCFPT columns with optimized CFRP wrapping can offer superior performance for eccentrically loaded columns.
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