Experimental Study and Theoretical Analysis on Seismic Performance of RC Shear Wall with STRC Columns and Embedded Steel Plate

Cao, Wan Lin; Dong, Hong; Zhang, Jian Wei

doi:10.4028/www.scientific.net/amr.446-449.1006

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Guo et al (2011) studied three different types of SP shear walls, and results show these shear walls have excellent ductility and energy dissipation. Cao et al (2010Cao et al ( , 2011Cao et al ( , 2012 conducted cyclic loading tests on composite shear walls with CFST columns and results show that these all exhibited good elastic-plastic deformation qualities; that CFST columns can greatly enhance the seismic behavior of the shear walls; and that the wall thickness of the SP has a signifi cant bearing on the hysteresis behavior of composite shear walls. However, investigation into past research shows a dearth of information on structural analysis of multi-energy dissipation composite shear walls, with equally little research on the post-earthquake restorability of these walls.…”

Section: Introductionmentioning

confidence: 96%

Experimental and analytical study on seismic behavior of steel-concrete multienergy dissipation composite shear walls

Hao

Cao

et al. 2015

Earthq. Eng. Eng. Vib.

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In this paper, a steel-concrete multi-energy dissipation composite shear wall, comprised of steel-reinforced concrete (SRC) columns, steel plate (SP) deep beams, a concrete wall and energy dissipation strips, is proposed. In order to study the multi-energy dissipation behavior and restorability after an earthquake, two stages of low cyclic loading tests were carried out on ten test specimens. In the fi rst stage, test on fi ve specimens with different number of SP deep beams was carried out, and the test lasted until the displacement drift reached 2%. In the second stage, thin SPs were welded to both sides of the fi ve specimens tested in the fi rst stage, and the same test was carried out on the repaired specimens (designated as new specimens). The load-bearing capacity, stiffness, ductility, hysteretic behavior and failure characteristics were analyzed for both stages and the results are discussed herein. Extrapolating from these results, strength calculation models and formulas are proposed herein and simulations using ABAQUS carried out; they show good agreement with the test results. The study demonstrates that SRC columns, SP deep beams, concrete wall and energy dissipation strips cooperate well and play an important role in energy dissipation. In addition, this study shows that the shear wall has good recoverability after an earthquake, and that the welding of thin SP's to repair a deformed wall is a practicable technique.

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Section: Introductionmentioning

confidence: 96%

Experimental and analytical study on seismic behavior of steel-concrete multienergy dissipation composite shear walls

Hao

Cao

et al. 2015

Earthq. Eng. Eng. Vib.

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“…It was observed that the shear capacity is obviously improved using smaller diameter studs or decreasing the distance between studs when the total area of studs is kept unchanged. Several researchers (Cao et al, 2012; Gan et al, 2008; Ji et al, 2017; Wang et al, 2018; Wu, 2014; Xiao et al, 2012; Zhu, 2015) have conducted experimental study on lateral behaviour of SPRC shear walls. The effects of parameters, including shear span ratio, reinforcement ratio, thickness of the concrete wall and steel plate and so on, on the cyclic behaviour of SPRC shear walls were investigated.…”

Section: Introductionmentioning

confidence: 99%

Shear strength of steel plate reinforced concrete shear wall

Zhao

Jia

Huang

2020

Advances in Structural Engineering

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This article investigates the shear strength of steel plate reinforced concrete shear wall under cyclic loads. A nonlinear three-dimensional finite element model in ABAQUS was developed and validated against published experimental results. Then, a parametric study was conducted to evaluate the effects of the parameters on the lateral capacity of composite shear wall, including shear span ratio, concrete strength, axial load ratio, steel plate ratio and transverse reinforcement ratio of the web. Furthermore, a modified formula of shear strength of composite shear wall was proposed. Regression analyses were used to obtain the contribution coefficients of different parts from 720 finite element models. Finally, the shear strengths of specimens from published tests were compared with design strengths calculated using the proposed formula, American Institute of Steel Construction Provisions and Chinese Code. It was found that the Chinese Code well predicts the shear strength of composite shear wall of a steel plate ratio of less than 5%, while unsafely predicting that of a higher steel plate ratio. The American Institute of Steel Construction Provisions predictions are quite conservative because the contribution of the reinforced concrete is neglected. The modified formula safely predicts the shear strength of composite shear wall.

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Shear bearing capacity prediction of STRC shear walls using data‐augmented fusion model

Yang,

Zhang,

et al. 2024

Structural Concrete

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Machine learning (ML) accurately predicts the shear bearing capacity of steel tube‐reinforced concrete (STRC) shear walls, aiding optimization design. However, creating a database for STRC shear walls experimentally is time‐consuming and costly. The aim of this study is to propose a method for high‐accuracy prediction of STRC shear wall shear bearing capacity using a small‐sample dataset. This study employs generative adversarial network (GAN) data augmentation techniques to address the issues of insufficient ML model training and low prediction accuracy in small‐sample databases. Based on the stacking framework, a fusion model (Stacking‐XRL) combining extreme gradient boosting (XGBoost), random forest (RF), and least absolute shrinkage and selection operator (LASSO) is established to predict the shear bearing capacity of STRC shear walls. Results show that after augmenting the training set with GAN, the prediction performance of K‐nearest neighbors (KNN), backpropagation neural network (BPNN), RF, light gradient boosting machine (LightGBM), XGBoost, and stacking‐XRL models significantly improve, with average increases of 10% in R2 and average decreases of 30% and 25% in MAE and RMSE, respectively. The proposed stacking‐XRL fusion model outperforms tested models, existing formulas, and Abaqus numerical simulations for the shear bearing capacity of STRC shear walls. Model interpretation reveals that the shear span ratios as the most important factors in predicting shear bearing capacity, followed by axial force ratio and whole section configuration steel tubular index.

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Experimental Study and Theoretical Analysis on Seismic Performance of RC Shear Wall with STRC Columns and Embedded Steel Plate

Cited by 4 publications

References 5 publications

Experimental and analytical study on seismic behavior of steel-concrete multienergy dissipation composite shear walls

Experimental and analytical study on seismic behavior of steel-concrete multienergy dissipation composite shear walls

Shear strength of steel plate reinforced concrete shear wall

Shear bearing capacity prediction of STRC shear walls using data‐augmented fusion model

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