电磁干扰
结构工程
接口(物质)
材料科学
声学
工程类
计算机科学
复合材料
电磁干扰
电子工程
物理
毛细管数
毛细管作用
作者
Qian Liu,Bin Xu,Zhipeng Xia,Zhifei Chen,Yudi Yao,Jiang Wang
标识
DOI:10.1177/13694332241242978
摘要
Concrete-filled steel tube (CFST) members have been widely used in skyscrapers and long-span bridges. Non-uniformly distributed hydration heat during curing, inadequate compaction during construction and unavoidable shrinkage and creep of mass concrete in service most likely lead to interface debonding defects between concrete core and steel tube, which has been a common concern. The development of effective non-destructive inspection methods for interface bonding condition of existing CFST members is critical. In this study, an interface debonding defect detection method based on electromechanical impedance (EMI) measurement using surface-mounted piezoelectric-lead-zirconate-titanate (PZT) sensors is proposed at first. Then, experimental study on the feasibility of the proposed approach is carried out with scaled CFST specimens with artificially mimicked interface debonding defects. EMI of surface-mounted PZT sensors at different frequency bands are measured and compared to verify the detectability of the proposed approach for the mimicked interface debonding defects. Thirdly, a multi-physics CFST-PZT coupling finite element model (FEM) with interface debonding defects is established, and EMI of surface-mounted PZT sensors at different locations is simulated. The influence of interface debonding defects on EMI measurements is illustrated. Finally, a blind inspection on the interface bonding condition of selected CFST columns in an existing skyscraper in service using the proposed approach is carried out. Both bonding and debonding defect in the tested CFST columns are detected correctly and validated with drilling hole observation. Experimental, numerical and engineering application results show the proposed approach is effective for interface debonding detection of CFST members and sensitive to minor debonding defect of 0.3 mm in CFST members.
科研通智能强力驱动
Strongly Powered by AbleSci AI