基于GEP的螺旋肋FRP筋与混凝土间黏结强度预测

doi:10.19936/j.cnki.2096-8000.20250928.015

摘要/Abstract

摘要： 黏结强度是评价纤维增强树脂复合材料(FRP)与混凝土黏结性能的重要指标。由于影响因素过多,相关规范及研究人员建立的数学模型黏结强度预测精度较低。为解决上述问题,搜集相关参考文献,建立包含81组螺旋肋FRP筋与混凝土间黏结强度的数据库,并基于基因表达式编程(GEP)算法建立数学预测模型。将GEP模型与基于试验数据拟合得到的数学公式进行横向对比,评估指标包括决定系数(R²)、平均绝对误差(MAE)与均方根误差(RMSE)。研究结果表明:基于GEP的黏结强度预测模型的R²,MAE,RMSE分别为0.867,1.510,1.853。相比于现有的精度最高的预测模型,基于GEP的预测模型R²提高了39.39%,MAE减少了31.79%,RMSE减少了27.48%。

关键词: 螺旋肋FRP筋, 基因表达式编程, 黏结强度, 预测模型, 混凝土

Abstract: Bonding strength is an important index to evaluate the bond performance between fiber-reinforced polymer (FRP) and concrete. Due to too many influencing factors, the prediction accuracy of the bonding strength of the relevant specifications and the mathematical model established by the researchers is low. In order to solve the above problems, a database containing 81 groups of bonding strength between helically wound FRP bar and concrete was established by collecting relevant references, and a mathematical prediction model was established based on gene expression programming (GEP) algorithm. The GEP model was compared with the mathematical formula based on the experimental data. The evaluation indexes included determination coefficient (R²), mean absolute error (MAE) and root mean square error (RMSE). The results show that the R², MAE and RMSE of the bonding strength prediction model based on GEP are 0.867, 1.510 and 1.853, respectively. Compared with the existing prediction models with the highest accuracy, the GEP-based prediction model R² is increased by 39.39%, MAE is decreased by 31.79%, and RMSE is decreased by 27.48%.

Key words: helically wound FRP bars, GEP, bonding strength, prediction model, concrete

中图分类号:

TB332

胡聪, 杨浪. 基于GEP的螺旋肋FRP筋与混凝土间黏结强度预测[J]. 复合材料科学与工程, 2025, 0(9): 117-124.

HU Cong, YANG Lang. Prediction of bonding strength between helically wound FRP bars and concrete based on GEP[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(9): 117-124.

参考文献

[1] 宋泽鹏, 陆春华, 宣广宇, 等. 螺纹GFRP筋与混凝土黏结性能试验与理论计算[J]. 建筑材料学报, 2021, 24(4): 887-894.
[2] BASARAN B, KALKAN I. Investigation on variables affecting bond strength between FRP reinforcing bar and concrete by modified hinged beam tests[J]. Composite Structures, 2020, 242: 112185.
[3] CHEN L, LIANG K, SHAN Z. Experimental and theoretical studies on bond behavior between concrete and FRP bars with different surface conditions[J]. Composite Structures, 2023, 309: 116721.
[4] KOMAROV A I, MAKAROVA N V, TSUPRIK V G. Investigation on bond performance between different types of FRP-reinforced rebars and concrete[J]. IOP Conference Series: Materials Science and Engineering, 2020, 889(1): 012027.
[5] SOLYOM S, BALÁZS G L. Bond of FRP bars with different surface characteristics[J]. Construction and Building Materials, 2020, 264: 119839.
[6] LIANG K, CHEN L, SHAN Z, et al. Experimental and theoretical study on bond behavior of helically wound FRP bars with different rib geometry embedded in ultra-high-performance concrete[J]. Engineering Structures, 2023, 281: 115769.
[7] SOLYOM S, BALÁZS G L. Analytical and statistical study of the bond of FRP bars with different surface characteristics[J]. Composite Structure, 2021, 270: 113953.
[8] OKELO R, YUAN R L. Bond strength of fiber reinforced polymer rebars in normal strength concrete[J]. Journal of Composites for Construction, 2005, 9(3): 203-213.
[9] DIAB A M, ELYAMANY H E, HUSSEIN M A, et al. Bond behavior and assessment of design ultimate bond stress of normal and high strength concrete[J]. Alexandria Engineering Journal, 2014, 53(2): 355-371.
[10] SHAN Z W, LIANG K, CHEN L J. Bond behavior of helically wound FRP bars with different surface characteristics in fiber-reinforced concrete[J]. Journal of Building Engineering, 2023, 65: 105504.
[11] 易晓园. 基于PSO-RF的FRP筋与混凝土间黏结强度预测模型[J]. 复合材料科学与工程, 2024(8): 84-90.
[12] 张芮椋, 薛新华. 基于基因表达式编程的NSM FRP-混凝土粘结强度预测模型[J]. 工程科学与技术, 2021, 53(2): 118-124.
[13] AMIN M N, IQBAL M, JAMAL A, et al. GEP tree-based prediction model for interfacial bond strength of externally bonded FRP laminates on grooves with concrete prism[J]. Polymers, 2022, 14(10): 2016.
[14] FERREIRA C. Gene expression programming: a new adaptive algorithm for solving problems[J]. Complex Systems, 2001, 13(2): 87-129.
[15] ZHANG P C, JIA Y Y, GAO J, et al. Short-term rainfall forecasting using multi-layer perceptron[J]. IEEE Transactions on Big Data, 2020, 6(1): 93-106.
[16] BAENA M, TORRES L, TURON A, et al. Experimental study of bond behaviour between concrete and FRP bars using a pull-out test[J]. Composites Part B: Engineering, 2009, 40(8): 784-797.
[17] FAHMY M F M, AHMED S A S, WU Z S. Bar surface treatment effect on the bond-slip behavior and mechanism of basalt FRP bars embedded in concrete[J]. Construction and Building Materials, 2021, 289: 122844.
[18] WANG Q, ZHU H, TONG Y X, et al. Bond-slip behaviour of the CFRP ribbed bars anchored with the innovative additional ribs in concrete[J]. Composite Structures, 2021, 262: 113595.
[19] ZHANG B, ZHU H, WU G, et al. Improvement of bond performance between concrete and CFRP bars with optimized additional aluminum ribs anchorage[J]. Construction and Building Materials, 2020, 241: 118012.
[20] 单波, 佟广权, 刘其元. CFRP筋与海水海砂混凝土黏结性能试验[J]. 建筑科学与工程学报, 2020, 37(5): 113-123.
[21] 中华人民共和国住房和城乡建设部. 混凝土结构设计规范: GB 50010—2010[S]. 北京: 中国建筑工业出版社, 2011.
[22] 吴贤国, 刘鹏程, 陈虹宇, 等. 基于随机森林的高性能混凝土抗压强度预测[J]. 混凝土, 2022(1): 17-20, 24.
[23] American Concrete Institute. Guide for the design and construction of structural concrete reinforced with fiber-reinforced polymer (FRP) bars: ACI 440.1 R-15[S]. Farmington Hills, Michigan: American Concrete Institute, 2015.
[24] Canadian Standards Association. Design and construction of building structures with fiber-reinforced polymers: CAN/CSA S806-12[S]. Toronto: Canadian Standards Association, 2012.
[25] Canadian Standards Association. Canadian highway bridge design code: CAN/CSA S6-06[S]. Toronto: Canadian Standards Association, 2006.
[26] 董恒磊, 李东风, 王代玉. 螺旋缠绕挤压肋FRP筋与混凝土间的粘结性能[J]. 复合材料学报, 2022, 39(11): 5239-5250.