Shear walls are effective lateral load resisting elements in high-rise buildings. This paper presents an experimental study of the seismic performance of a composite shear wall system that consists of high-strength concrete walls with the embedded steel plate. Two sets of wall specimens with different aspect ratios (height/width, 1.5 and 2.7) were constructed and tested under quasi-static reversed cyclic loading, including five reinforced concrete shear walls (RCSW) and six reinforced concrete-steel plate shear walls (RCSPSW). The progression of damage, failure modes, and load-displacement responses of test specimens were studied and compared based on experimental observations. The test results indicated that high-strength (HS) RCSPSW system showed superior lateral load strength and acceptable deformation capability. The axial compressive load was found to have an indispensable effect on the ductility of both RCSW and RCSPSW, and an upper limit of axial compression ratio (0.5) is recommended for the application of HS RCSPSW in engineering practices. In addition, the design strength models were suggested for predicting the shear and flexure peak strength values of RCSPSW systems, and their applicability and reliability were verified by comparing with test results.
Transverse reinforcement of reinforced concrete (RC) columns contributes greatly to the ductility deformation capacity of RC structures. The existing models to predict the amount of transverse reinforcement required are all empirical models with low accuracy and large dispersion and have not considered the real ductility demand of individual components. This paper proposes a ductility design method of RC structure based on component drift ratio demand obtained from nonlinear structural dynamic analysis. To establish the best transverse reinforcement ratio prediction model for RC columns, based on an experimental database consisting of 498 columns, 12 machine learning (ML) models are trained. To solve the over-fitting problem caused by the current situation of “few samples and big errors” of the experimental database, feature engineering aiming at dimension reduction is systematically carried out through an iterative process. Through comprehensive performance evaluation on the testing set, an XGBoost model is selected. To interpret the “black box” ML model, the SHAP method and partial dependence plots are used to analyse the correlation between the input parameters and the transverse reinforcement ratio. The interpretation results are consistent with mechanical laws and engineering experience, which prove the reliability of the selected ML model. Compared with two existing empirical models, the proposed XGBoost model shows higher accuracy and smaller deviation. After safety probability analysis, the trained XGBoost model is transformed into C code and integrated into seismic design software for productive practice. An open-source data-driven model to predict the transverse reinforcement ratio required for RC columns is provided worldwide, with the flexibility to account for additional experimental results.
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