Experimental and Bearing Capacity Research on Prestressed Shape Memory Alloy Strips Confined Concrete Column

https://doi.org/10.28991/CEJ-2025-011-07-01
Shape Memory Alloy Strips Actively Confined Uniaxial Compression Bearing Capacity Calculation Model BP Neural Network

Authors

  • Lidan Xu 1) School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China. 2) Inner Mongolia Key Laboratory of Safety and Durability for Civil Engineering, Baotou 014010, China
  • Guangtao Mu 1) School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China. 2) Inner Mongolia Key Laboratory of Safety and Durability for Civil Engineering, Baotou 014010, China https://orcid.org/0009-0002-8590-7981
  • Jitao Zhao School of Civil and Architecture Engineering, Panzhihua University, Panzhihua 617000, China
  • Miaomiao Zhu 1) School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China. 2) Inner Mongolia Key Laboratory of Safety and Durability for Civil Engineering, Baotou 014010, China
  • Ming Chen 1) School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China. 2) Inner Mongolia Key Laboratory of Safety and Durability for Civil Engineering, Baotou 014010, China
  • Yutong Yan School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
  • Mingfang Shi
    shimingfang2019@imust.edu.cn
    1) School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China. 2) Inner Mongolia Key Laboratory of Safety and Durability for Civil Engineering, Baotou 014010, China https://orcid.org/0000-0001-9676-9479
Vol. 11 No. 7 (2025): July
Research Articles

Downloads

The prestressed shape memory alloy (SMA) strips confined columns are a novel reinforcement method, which not only exerts active confinement stress on the core concrete but also avoids the common stress hysteresis problem in reinforcement. This paper performed axial compression tests on eight sets of concrete columns with varying SMA strip width, net spacing, and pre-strain, and the impacts of these variables regarding the failure pattern, bearing capacity, and deformability of the specimens were investigated. A calculation model for the bearing capacity of SMA strips actively confined to concrete columns was established and contrasted with the prediction performance of the BP neural network. The results indicate that compared to the unconfined column, SMA strip-confined columns exhibit obvious ductile failure under compression, with the highest increase of bearing capacity and deformability reaching up to 20.27% and 24.96%, respectively. The confinement effect becomes better and better with the increasing strip width or the decreasing strip net spacing. When the strip pre-strain gradually increases, the bearing capacity of confined columns gradually improves, while the deformability first enhances and then weakens. The experimental data of other scholars is used to verify that the calculation results accord with the experimental results well, and the prediction precision of the proposed calculation model exceeds that of the BP neural network. Meanwhile, it is confirmed that the BP neural network exhibits a high fitting level in bearing capacity prediction (R2training=0.990 and R2test=0.965), offering a novel approach for predicting the bearing capacity of structures.