Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions

被引：13

作者：

Kumar, Aman ^{[1
,2
]}

Arora, Harish Chandra ^{[1
,2
]}

Kumar, Prashant ^{[1
,2
]}

Kapoor, Nishant Raj ^{[1
]}

Nehdi, Moncef L. ^{[3
]}

机构：

[1] Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, Uttar Pradesh, India

[2] CSIR Cent Bldg Res Inst Roorkee, Struct Engn Dept, Roorkee 247667, Uttarakhand, India

[3] McMaster Univ, Dept Civil Engn, Hamilton, ON L8S 4L8, Canada

来源：

DEVELOPMENTS IN THE BUILT ENVIRONMENT | 2024年 / 17卷

关键词：

FRP; Concrete; Bond strength; Durability; Machine learning; XGBoost; Analytical model; Graphical interface; PLATES; JOINTS; MODELS;

D O I：

10.1016/j.dibe.2023.100311

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Predicting FRP-to-concrete bond strength (FRP-CBS) under diverse exposure conditions is an intricate task influenced by multiple variables. Yet, existing pertinent models have several limitations. Accordingly, this study proposes a novel data driven machine learning (ML) methodology to predict the FRP-CBS considering various exposure conditions. A comprehensive database on single and double lap-shear strength tests on concrete specimens was meticulously compiled. Twenty-seven analytical models were used to appraise the developed ML models. Feature importance analysis was conducted to ascertain the influence of input parameters on bond strength. The proposed data-driven ML models attained exceptional accuracy and superior performance compared to existing analytical models. To enhance the accuracy of bond strength estimation and simplify the process for practicing engineers and FRP applicators, a user-friendly graphical interface was developed. It could eliminate the need for complex design procedures, making it easier to accurately estimate the FRP-CBS, thus improving overall efficiency in engineering practice.

引用

页数：16

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