基于改进PF算法的锈蚀RC结构抗弯承载力预测方法

戴理朝; 梁紫璋; 胡卓; 王磊

doi:10.6052/j.issn.1000-4750.2022.01.0038

基于改进PF算法的锈蚀RC结构抗弯承载力预测方法

doi: 10.6052/j.issn.1000-4750.2022.01.0038

长沙理工大学土木工程学院，湖南，长沙 410114

基金项目: 国家自然科学基金项目(52008035)；湖南省重点领域研发计划项目(2019SK2171)；湖南省自然科学基金项目(2020RC4024，2020JJ1006，2021JJ40574)；湖南省交通科技项目(201619)

详细信息

作者简介:
戴理朝(1989−)，男，湖南人，副教授，博士，主要从事桥梁耐久性与可靠度研究(E-mail: lizhaod@csust.edu.cn)

梁紫璋(1997−)，男，湖北人，硕士生，主要从事桥梁耐久性研究(E-mail: 287398174@qq.com)

胡　卓(1994−)，男，湖南人，博士生，主要从事桥梁耐久性与可靠度研究(E-mail: zhuohuhz@outlook.com)

通讯作者:
王　磊(1979−)，男，吉林人，教授，博士，主要从事桥梁耐久性与可靠度研究(E-mail: leiwang@csust.edu.cn)

中图分类号: TU375
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出版历程
- 收稿日期: 2022-01-07
- 修回日期: 2022-04-22
- 网络出版日期: 2022-05-20
- 刊出日期: 2023-09-06

FLEXURAL CAPACITY PREDICTION OF CORRODED RC STRUCTURES BASED ON IMPROVED PARTICLE FILTER ALGORITHM

School of Civil Engineering, Changsha University of Science & Technology, Changsha, Hu’nan 410114, China

摘要

摘要: 为提高锈蚀钢筋混凝土(RC)结构抗弯承载力评估精度，该文综合考虑锈蚀RC结构几何尺寸、钢筋截面积及力学性能、混凝土强度、粘结性能等因素，提出了基于改进粒子滤波(PF)算法的抗弯承载力模型参数更新及预测方法。通过生成大量的粒子以表征承载力退化过程中模型参数的不确定性，从选择不同建议密度函数的角度改进PF算法以解决传统PF算法中粒子退化的问题，分别采用PF、扩展粒子滤波(EPF)、无迹粒子滤波(UPF)算法对模型参数进行估计与更新，实现了锈蚀RC结构抗弯承载力的有效预测。结果表明：随着钢筋锈蚀率的增加，RC结构的抗弯承载力逐渐降低。基于改进PF算法的锈蚀RC结构抗弯承载力预测方法因考虑了模型参数更新使得预测结果更接近试验数据。基于EKF和UKF的改进PF算法可有效抑制粒子退化，其预测精度较PF算法更高；锈蚀RC结构抗弯承载力预测精度随着训练数据及粒子数的增加而提高。
- RC结构 /
- 锈蚀 /
- 抗弯承载力 /
- 扩展粒子滤波算法 /
- 无迹粒子滤波算法
Abstract: To improve the evaluation accuracy of flexural capacity of corroded reinforced concrete (RC) structures, a method for the model parameter updating and prediction of flexural capacity is proposed based on the improved particle filter (PF) algorithm. The proposed model comprehensively considers the geometric size of corroded RC structures, steel cross-sectional area and mechanical properties, concrete strength, bond performance and other factors. A large number of particles are generated to represent the uncertainty of model parameters during the degradation process of flexural capacity. The PF algorithm is improved from the perspective of selecting different proposal density functions to solve the problem of particle degradation in traditional PF algorithm. The PF, the extended particle filter (EPF) and the unscented particle filter (UPF) algorithm are employed to estimate and update the model parameters, which can effectively predict the flexural capacity of corroded RC structures. The results show that the flexural capacity of RC beams decreases gradually with the increase of steel corrosion loss. The prediction method of the flexural capacity of corroded RC structures based on the improved PF algorithm considers the updating of model parameters, which makes the prediction results closer to the reality. The improved PF algorithm based on EKF and UKF can effectively constrain the degradation of the particle, and the prediction accuracy is better than that of the PF algorithm. The prediction accuracy of the flexural capacity of corroded RC structures increases with the increase of training data and particle number.
- RC structures /
- corrosion /
- flexural capacity /
- extended particle filter algorithm /
- unscented particle filter algorithm

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图 1 协同工作系数的拟合曲线

Figure 1. Fitted curves of co-working coefficients

下载: 全尺寸图片幻灯片

图 2 基于改进粒子滤波的抗弯承载力计算流程

Figure 2. Calculation flow of flexural capacity based on improved particle filter

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图 3 抗弯承载力模型各参数更新过程

Figure 3. Updating process of parameters in flexural capacity model

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图 4 以10%锈蚀率为预测起点的抗弯承载力预测结果

Figure 4. Prediction results of flexural capacity with the corrosion loss of 10% as the starting point of prediction

下载: 全尺寸图片幻灯片

图 5 不同预测起点下锈蚀RC结构抗弯承载力预测结果

Figure 5. Prediction results of flexural capacity of corroded RC structures under different prediction starting points

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图 6 RMSE随粒子数目变化图

Figure 6. Variation of RMSE with particle number

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图 7 RMSE随仿真次数变化对比图

Figure 7. RMSE variation with simulation times

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表 1 不同模型的拟合效果比较

Table 1. Comparison of fitting effects with different models

模型	一次函数拟合	二次函数拟合	单指数拟合	双指数拟合
RMSE	0.03223	0.02969	0.03361	0.02902
R²	0.81280	0.84240	0.79580	0.84690

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表 2 状态模型参数初值

Table 2. Initial values of state model parameters

置信区间	参数a	参数b	参数c
中值	−1.468×10⁻⁴	−3.931×10⁻³	0.988
下限值	−1.961×10⁻⁴	−5.465×10⁻³	0.972
上限值	−0.970×10⁻⁴	−2.398×10⁻³	0.997

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表 3 锈蚀RC结构抗弯承载力预测结果

Table 3. Prediction results of flexural capacity of corroded RC structures

预测起始点/(%)	PF算法			EPF算法			UPF算法
预测起始点/(%)	预测值	误差/(%)	PDF分布区间	预测值	误差/(%)	PDF分布区间	预测值	误差/(%)	PDF分布区间
5	27.39	9.56	[23.17, 31.04]	26.83	7.32	[23.34, 29.96]	26.06	4.24	[23.08, 27.97]
10	26.95	7.80	[23.72, 30.48]	26.34	5.36	[23.93, 29.37]	25.52	2.08	[23.64, 27.28]
15	26.57	6.28	[24.68, 29.53]	25.86	3.44	[25.02, 28.31]	25.23	0.92	[24.21, 26.72]

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