假弹性
形状记忆合金
材料科学
结构工程
刚度
钢筋
钢筋混凝土
剪力墙
计算机科学
复合材料
工程类
马氏体
微观结构
作者
Saim Raza,Behrouz Shafei,Mehdi S. Saiidi,Masoud Motavalli,Moslem Shahverdi
标识
DOI:10.1016/j.conbuildmat.2022.126628
摘要
The degradation of reinforced concrete (RC) structural components owing to aging mechanisms and extreme loading events can cause significant performance and safety concerns. Among the available alternatives for restoring such components, shape memory alloys (SMAs) exhibit unique properties, including the recovery of inelastic strain upon unloading (superelasticity) and/or heating (shape memory effect, SME). Particularly, the superelasticity and SME of SMAs can be applied to reduce permanent deformations and incorporate self-centering behavior into RC structures. Furthermore, the addition of SMAs can enhance the strength and stiffness of RC structures, enabling them to resist high load intensities with less damage. Despite the variety of investigations conducted on the structural applications of SMAs, the existing literature lacks a holistic review of the current progress, main findings, potential limitations, and future prospects of SMAs for the strengthening of existing RC structures and the design of new ones. Furthermore, comprehensive guidance is missing for selecting the SMA types and characteristics most suitable for a particular strengthening/self-centering application. To address the identified fundamental and practical gaps, we performed a detailed review of the applications of SMAs in RC beams, columns, beam-column joints, and shear walls. The identified applications were explored from the perspective of the self-centering, crack recovery, strength enhancement, confinement, and shear strengthening of existing and new RC structures. A critical review of the advantages and disadvantages of strengthening with SMAs is then provided, especially in comparison to conventional strengthening materials and methods. This review concludes with the identification of challenges associated with using SMAs and future opportunities that can arise owing to the proper use of SMAs.
科研通智能强力驱动
Strongly Powered by AbleSci AI