锈蚀RC柱抗震性能预测与劣化规律研究综述  

Research review on the seismic performance prediction and degradation law of corroded RC columns

作　　者：陈柳灼 邱羿志 周炎 郑山锁[3] CHEN Liuzhuo;QIU Yizhi;ZHOU Yan;ZHENG Shansuo(Hubei Key Laboratory of Disaster Prevention and Mitigation(China Three Gorges University),Yichang 443002,China;College of Civil Engineering and Architecture,China Three Gorges University,Yichang 443002,China;School of Civil Engineering,Xi’an University of Architecture&Technology,Xi’an 710055,China)

机构地区：[1]防灾减灾湖北省重点实验室(三峡大学),湖北宜昌443002 [2]三峡大学土木与建筑学院,湖北宜昌443002 [3]西安建筑科技大学土木工程学院,陕西西安710055

出　　处：《地震工程与工程振动》2025年第2期33-49,共17页Earthquake Engineering and Engineering Dynamics

基　　金：湖北省自然科学基金青年项目(2024AFB302);国家重点研发计划课题(2019YFC1509302);防灾减灾湖北省重点实验室(三峡大学)开放基金项目(2022KJZ14,2022KJZ16)。

摘　　要：钢筋混凝土(reinforced concrete, RC)柱在服役期内受环境侵蚀的影响,其内部钢筋将发生锈蚀损伤,导致RC柱抗震性能退化,进而使其暴露于严重的地震灾害风险中。文中从试验方法、劣化规律、破坏模式预测和承载力计算四个方面,对锈蚀RC柱抗震性能既有研究进行综述。阐述了现有锈蚀RC柱试件抗震试验中采用的锈蚀与加载方法,统计分析了锈蚀程度和主要设计参数对锈蚀RC柱延性、刚度和耗能能力等抗震性能指标的劣化影响。基于包含290根锈蚀RC柱的抗震试验数据集,对比了剪跨比、延性系数和抗剪需求比3种参数划分方法和极端梯度提升(extreme gradient boosting, XGBoost)机器学习算法识别锈蚀RC柱破坏模式的准确性,并结合沙普利加性解释(shapley additive explanations, SHAP)方法揭示了锈蚀程度与主要设计参数对锈蚀RC柱破坏模式的影响规律。总结了锈蚀RC柱剩余抗弯与抗剪承载力计算方法,并讨论了其预测效果。结果表明:不同锈蚀方法下钢筋的锈蚀形态、锈蚀速率和锈蚀精度均存在差异,双向拟静力加载机制较单向加载更能反映锈蚀RC柱抗震性能劣化规律;随着钢筋锈蚀率的增加,RC柱的延性、刚度和耗能能力均显著降低。结合SHAP法的机器学习模型能够有效兼顾锈蚀RC柱破坏模式预测的准确性与可解释性,此类数据驱动的预测方法为解决锈蚀RC柱性能评估问题提供了新的思路。钢筋锈蚀会降低RC柱抗弯与抗剪承载力,现阶段提出的锈蚀RC柱承载力计算模型精度仍待进一步提高,以为锈蚀构件性能评估提供合理依据。Reinforced concrete(RC)columns are exposed to serious seismic disaster risks due to corrosion damage of the internal rebar during the service period caused by environmental corrosion,causing the seismic performance of RC columns to deteriorate and thus be exposed to severe seismic hazard risks.This paper reviews the existing research on the seismic properties of corroded RC columns from four aspects:test methods,degradation law,failure mode prediction and bearing capacity calculation.The corrosion and loading methods used in seismic tests of corroded RC columns are elaborated.The effects of corrosion degree and main design parameters on the deterioration of seismic performance indexes such as ductility,stiffness and energy dissipation capacity of corroded RC columns are statistically analyzed.Based on the seismic test dataset of 290 corroded RC columns,the accuracy of three parametric delineation methods including shear span ratio,ductility coefficient,and shear demand ratio and the extreme gradient boosting(XGBoost)machine learning algorithm for failure mode recognition of corroded RC columns is compared.The influence of the degree of corroded and main design parameters on the failure modes of corroded RC columns is revealed by shapley additive explanations(SHAP)method.The calculation method of residual flexural and shear strength of corroded RC columns are summarized and the prediction effect are discussed.The results show that there are differences in corrosion shape,corrosion rate and corrosion accuracy under different corrosion methods.The bidirectional quasi-static loading mechanism can reflect the degradation law of seismic performance of corroded RC columns better than unidirectional loading.With the increase of the corrosion rate of the rebar,the ductility,stiffness and energy dissipation capacity of RC columns deteriorate significantly.The machine learning model combined with SHAP method can effectively balance the accuracy and interpretability of the failure mode prediction of corroded RC columns.This

关 键 词：锈蚀RC柱 抗震性能 机器学习 破坏模式 剩余承载力 

分 类 号：TU375.3[建筑科学—结构工程] TU352.11[自动化与计算机技术—控制理论与控制工程] TP181[自动化与计算机技术—控制科学与工程] P315.9[天文地球—地震学]