基于PSO-RF的FRP筋与混凝土间黏结强度预测模型

doi:10.19936/j.cnki.2096-8000.20240828.012

摘要/Abstract

摘要： 与钢筋相比,FRP筋具有抗疲劳、低磁性和耐腐蚀等优势,在不利工程环境中的应用越来越普遍。然而,现有FRP筋与混凝土间黏结性能预测模型的适用范围较小,预测精度较低。为此,本文收集了170组铰接梁试验数据,并在此基础上利用粒子群算法优化随机森林算法(PSO-RF)建立了FRP筋与混凝土间的黏结强度模型。将PSO-RF模型与RF模型和五个现有模型进行对比,各模型的预测结果通过决定系数R²和平均绝对值误差MAE等统计指标进行评价。PSO-RF模型的R²和MAE值分别为0.939 6和1.014 3,可以为FRP筋在混凝土中的应用提供有价值的参考。与现有模型相比,PSO-RF模型的R²和MAE值分别提高了141.9%和81.3%,预测性能提升明显。模型的参数重要性分析结果表明,黏结长度和混凝土抗压强度对模型的预测结果影响较大,重要性系数分别为22.35%和18.3%。

关键词: 黏结强度, FRP筋, 混凝土, 粒子群优化, 随机森林, 复合材料

Abstract: Compared with steel bars, FRP bars have excellent properties such as corrosion resistance, low magnetic properties and fatigue resistance, making their applications in unfavorable engineering environments more and more common. However, the current predictive models for the bond behavior between FRP bars and concrete exhibit limited applicability and low predictive accuracy. Therefore, this paper collected 170 sets of hinged beam test data. Utilizing particle swarm optimization (PSO) to enhance the random forest (RF)algorithm, a model for predicting the bond strength between FRP bars and concretewas developed. The PSO-RF model was compared with the RF model and five existing models, and the predictive performance of these models was evaluated using statistical indicators such as the coefficient of determination R² and the mean absolute error MAE. The PSO-RF model demonstrated an R² value of 0.939 6 and an MAE value of 1.014 3, which can provide a valuable reference for the applications of FRP bars in concrete. Compared with the existing models, the R² and MAE values of the PSO-RF model were improved by 141.9% and 81.3%, respectively. The results of the analysis on parameter importance within the model indicated that bond length and compressive strength of concrete are two significant factors influencing the model’s predictive outcomes, with importance coefficients of 22.35% and 18.3%, respectively.

Key words: bond strength, FRP bars, concrete, particle swarm optimization, random forest, composites

中图分类号:

TB332

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, 0(8): 84-90.

YI Xiaoyuan. Prediction model of bond strength between FRP bars and concrete based on PSO-RF neural network[J]. COMPOSITES SCIENCE AND ENGINEERING, 2024, 0(8): 84-90.

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