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作 者:张伟平[1,2] 邱俊澧 姜超 ZHANG Weiping;QIU Junli;JINAG Chao(Key Laboratory of Performance Evolution and Control for Engineering Structures of the Ministry of Education,Tongji University,Shanghai 200092,China;College of Civil Engineering,Tongji University,Shanghai 200092,China)
机构地区:[1]同济大学工程结构性能演化与控制教育部重点实验室,上海200092 [2]同济大学土木工程学院,上海200092
出 处:《建筑材料学报》2024年第11期1022-1032,共11页Journal of Building Materials
基 金:上海市2022年度“科技创新行动计划”社会发展科技攻关项目(22dz1203603)。
摘 要:以混凝土结构中钢筋锈蚀特征的漏磁探测技术为主线,对相关研究进行综合回顾与分析.结果表明,基于漏磁探测技术能够准确识别混凝土结构中钢筋的锈蚀区域并定量评估钢筋锈蚀率.为进一步提升利用漏磁技术探测既有钢筋混凝土结构锈蚀特征的适用性和准确性,未来还需要充分考虑磁化强度随机分布引起的锈蚀率评估结果的概率分布特性,明确应力、疲劳、箍筋/相邻纵筋、锈蚀产物和混凝土的影响,推动基于漏磁成像的计算机视觉自动识别和融合多指标、多技术方法的应用.The main focus is the state-of-the-art magnetic flux leakage(MFL)detection technique for rebar corrosion characteristics in reinforced concrete(RC)structures,providing a comprehensive analysis of relevant research.The results indicate that the MFL detection technique can accurately identify corrosion areas of rebar embedded in RC structures and quantitative assessment the corrosion degree of the rebar.To further improve the applicability and accuracy of using the MFL detection technique for corrosion characteristics in existing RC structures,the probabilistic distribution characteristics of corrosion degree assessments resulting from the random distribution of magnetization should be fully considered,the effects of stress,fatigue,stirrups/adjacent longitudinal rebars,corrosion products,and concrete should be clarified,and the application of computer vision for automatic identification based on MFL imaging and the integration of multiple indicators and methodologies needed be promoted.
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