Exploratory machine learning analysis of shape memory alloy as hybrid reinforcement for reinforced concrete shear walls
Document Type
Conference Proceeding
Publication Date
3-2023
Abstract
Within the past few years, the application of shape memory alloy (SMA) in seismic engineering has gained great interest from researchers through various applications such as: reinforcement replacement, dampers, base isolation, and expansion joints. Such interest was due to the shape memory effect and the super elasticity nature of SMA that allows fast repair procedure via mechanical or thermal treatment. Among which, SMA has proven its efficiency as replacement for conventional reinforcement for reinforced concrete shear walls in terms of energy dissipation and recovery behavior. However, its application is still limited to lab scale or structural strengthening due to its high initial cost compared to other materials. In this context, this paper aims to determine the influence of the design parameters on the seismic behaviour (specifically, energy dissipation) of hybrid SMA reinforced concrete shear walls. The proposed investigation is backed on an unsupervised exploratory machine learning analysis for a developed numerical database. The generated database was validated against experimental results from the literature. This work lays the foundation for full scale application of SMA in reinforced concrete shear walls.
Recommended Citation
El-desoqi, Mohammed; Shamass, Rabee; Kraincanic, Ivana; and Salem, Shady, "Exploratory machine learning analysis of shape memory alloy as hybrid reinforcement for reinforced concrete shear walls" (2023). Civil Engineering. 138.
https://buescholar.bue.edu.eg/civil_eng/138