Seismic collapse safety and response modification factor of concrete frame buildings reinforced with superelastic shape memory alloy (SMA) rebar

Siddiquee, Kader Newaj; Billah, A. H. M. Muntasir; Issa, Anas

doi:10.1016/j.jobe.2021.102468

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…Furthermore, several studies have examined the application of SMAs in large-scale concrete structures. Siddiquee et al (2021) assessed the seismic performance of 3-, 6-, and 8-story concrete buildings reinforced with Ni-Ti SMA rebar. In this study, they assessed the Collapse Margin Ratio (CMR), which is calculated as the ratio of ground motion intensity at the median collapse point to the ground motion intensity of the Maximum Considered Earthquake (MCE) at the building's fundamental period.…”

Section: Sma Alloymentioning

confidence: 99%

Mechanical properties and constitutive models of shape memory alloy for structural engineering: A review

Mohammadgholipour

Billah

2023

Journal of Intelligent Material Systems and Structures

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Shape Memory Alloys (SMAs) are an innovative material with the unique features of superelasticity and energy dissipation capabilities under extreme loads. Due to their unique features, they have a great potential to be employed in structural engineering applications under different conditions. However, in order to effectively use SMAs in civil engineering structures and model their behaviors accurately in Finite Element (FE) packages, it is crucial for structural engineers to comprehend the mechanical properties and cyclic behavior of different SMA compositions under varying loading conditions. While previous studies have focused mainly on the cyclic behavior of SMAs under tensile loading, it is important to evaluate their fatigue behavior under cyclic tension-compression loading for seismic applications. This literature review aims to discuss the current gaps in the existing literature on the behavior of SMA rebars under low-cycle fatigue (LCF). The review provides a comprehensive overview of the primary characteristics of SMAs, summarizes the mechanical properties of SMAs presented in the literature and the parameters that affect them, and critically evaluates the effects of cyclic loading and LCF on SMAs. The review also provides a summary of the different constitutive models of SMAs and compares their advantages and limitations, which helps structural engineers to employ an appropriate constitutive model for predicting the accurate behavior of SMAs in FE software.

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Section: Sma Alloymentioning

confidence: 99%

Mechanical properties and constitutive models of shape memory alloy for structural engineering: A review

Mohammadgholipour

Billah

2023

Journal of Intelligent Material Systems and Structures

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“…The Ni-Ti SMA enables displacement recovery through the shape memory effect, which returns to its original shape above a specific temperature threshold, and the superelastic ability, which returns to its initial state without residual deformation even after surpassing the yield point [21][22][23]. A recent study was conducted to enhance the seismic performance of concrete walls by considering the introduction of SMA bars to replace conventional steel bars in structural members [24][25][26][27]. However, the high production costs associated with SMA bars pose an economic challenge compared to conventional steel bars.…”

Section: Introductionmentioning

confidence: 99%

Development and Performance Evaluation of a Mechanical Connection for Steel and Shape Memory Alloy Bars

Song,

Choi,

Lee

2024

Metals

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Shape memory alloys (SMAs) demonstrate a shape memory effect and superelasticity that can provide recovery performance to structural members. In this study, a round SMA bar was designed to replace the conventional deformed steel bar, particularly within the plastic hinge section of structural members. To integrate the SMA bar and the existing steel bar, a mechanical coupler was proposed by utilizing the advantages of both one-touch and threaded couplers. Uniaxial tensile tests were conducted to analyze the performance of the proposed coupler and the mechanical properties of the SMA–steel connected bar. Stress and strain relationships were examined for steel bars mechanically connected with the SMA bar and for SMA bars before and after exhibiting the shape memory effect. To induce the shape memory effect, SMA should be heated above the finished austenite temperature. Due to the difficulty of accurately measuring strain on the heated bar using traditional contact methods, we employed digital image correlation technology for precise strain measurement of the heated SMA bar. The experimental results indicate the effective application of SMA bars within the plastic hinge region of structural members using the proposed mechanical coupler.

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“…Some earlier studies employed numerical simulation models to determine the drift capacity of circular RC columns reinforced with SMA bars (Billah & Alam, 2018, 2016a, 2016b; Shrestha & Hao, 2016; Shrestha et al., 2015, 2017). The drift capacity of concrete columns with SMA bars is assessed using numerical simulations (Abraik, 2020; Billah & Alam, 2012; Billah & Alam, 2015; Elfeki & Youssef, 2017; Lee & Jeon, 2022b; Lee et al., 2022; Siddiquee et al., 2021); however, the use of sophisticated finite element models is not incorporated into the current design codes because computing nonlinear numerical simulations is time‐consuming and requires professional knowledge to configure the model.…”

Section: Introductionmentioning

confidence: 99%

Machine learning–assisted drift capacity prediction models for reinforced concrete columns with shape memory alloy bars

Lee,

Mangalathu,

Jeon

2023

Computer aided Civil Eng

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Despite notable progress made in predicting the drift capacity of reinforced columns with steel bars, these techniques and methods are proven inapplicable for accurately predicting the drift capacity of RC columns reinforced with shape memory alloy (SMA) bars. This study employed machine learning (ML) to predict and design the drift limit state of concrete columns using SMA bars. To this end, a total of 292,000 synthetic data points were generated through numerical simulations given the limited amount of experimental data for SMA bars. The data analysis results suggest that the light gradient boosting (LGB) algorithm achieves the best performance in terms of computational efficiency and prediction accuracy among nine candidate ML algorithms considered in this study. Further refinements for the LGB algorithm is introduced to yield better prediction results: (1) Hyperparameters are tuned using particle swarm optimization with an improved particle updating strategy and (2) the dimensions of the input data are reduced using a modified recursive feature elimination algorithm with memorizing capability. In addition, this study demonstrated the application of the proposed ML‐assisted drift capacity prediction model to the design of SMA‐reinforced concrete columns using modified particle swarm optimization that can help structural designers worldwide.

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Seismic collapse safety and response modification factor of concrete frame buildings reinforced with superelastic shape memory alloy (SMA) rebar

Cited by 12 publications

References 40 publications

Mechanical properties and constitutive models of shape memory alloy for structural engineering: A review

Mechanical properties and constitutive models of shape memory alloy for structural engineering: A review

Development and Performance Evaluation of a Mechanical Connection for Steel and Shape Memory Alloy Bars

Machine learning–assisted drift capacity prediction models for reinforced concrete columns with shape memory alloy bars

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