基于机器学习预测纤维增强聚合物（FRP）筋-混凝土黏结强度的研究

李华; 古金本; 陶毅

doi:10.3724/j.gyjzG25080202

基于机器学习预测纤维增强聚合物（FRP）筋-混凝土黏结强度的研究

doi: 10.3724/j.gyjzG25080202

李华¹,
古金本^1,2,
陶毅^1,2, ,

1. 西安建筑科技大学土木工程学院, 西安 710055;
2. 绿色建筑全国重点实验室, 西安 710055

基金项目:

西安市科学技术协会青年人才托举计划项目（959202413011）；陕西省教育厅协同创新中心项目（21JY025）；省级国有资本经营预算科技创新专项资金项目（2023012）。

详细信息

作者简介:
李华，硕士研究生，主要从事机器学习预测结构性能方面研究，lihua1111@xauat.edu.cn。

通讯作者:
陶毅，博士，教授，主要从事新材料及新材料结构方面研究，y.tao@xauat.edu.cn。

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出版历程
- 收稿日期: 2025-08-02
- 刊出日期: 2025-10-31

A Machine Learning-Based Method for Predicting Bond Strength Between FRP Bars and Concrete

LI Hua¹,
GU Jinben^1,2,
TAO Yi^{1,2
, ,}

1. College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China;
2. State Key Laboratory of Green Building, Xi'an 710055, China

摘要

摘要: 对纤维增强聚合物（FRP）筋与混凝土的黏结强度进行了研究，通过整合16篇文献中的487组拉拔试验数据，构建了包含纤维类型、成型工艺、表面特性等12个输入变量的数据库。运用随机森林（RF）、极端梯度提升（XGBoost）、LightGBM等7种机器学习算法进行预测，分析并对比12个特征参数对黏结强度的影响机制。预测结果显示，混凝土抗压强度、FRP筋抗拉强度及弹性模量是FRP筋与混凝土黏结强度的关键影响因素，其中树模型整体表现优于非树模型，LightGBM算法兼具高精度与高效性。通过SHAP值分析发现，成型工艺（如肋纹）对黏结强度的影响权重超过纤维类型。基于LightGBM算法进一步对各参数的影响程度进行量化排序，确定了材料力学性能及FRP筋表面特性对黏结强度的影响较大。
- FRP筋 /
- 混凝土 /
- 黏结强度 /
- 机器学习 /
- 成型工艺
Abstract: This study investigates the bond strength between fiber-reinforced polymer （FRP） bars and concrete. A database was constructed by integrating 487 pull-out test data points from 16 literature sources， featuring 12 input variables such as fiber type， forming process， and surface characteristics. Seven machine learning algorithms， such as RF， XGBoost， and LightGBM， were employed for prediction. The influence mechanisms of the 12 variables on the bond strength were analyzed and compared. The prediction results indicated the compressive strength of concrete， along with the tensile strength and elastic modulus of the FRP bars， as the key factors affecting the bond strength. Among them， tree models showed superior performance to non-tree models， with the LightGBM algorithm exhibiting high accuracy and efficiency. SHAP value evaluation revealed that forming processes （e.g.， the RB process） had a greater influence on the bond strength than fiber type did. Furthermore， the significance of all involved variables was quantitatively ranked using the LightGBM algorithm， showing that the mechanical properties of the materials and the surface characteristics of the FRP bars had a more significant effect on the bond strength.
- FRP bars /
- concrete /
- bond strength /
- machine learning /
- forming process

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参考文献(26)

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