Abstract:This study investigates the effect of certain parameters on the magnitude of plastic hinge length of RC shear walls and proposes an analytical expression for estimating the plastic hinge length. A finite element model was analyzed and validated with experimental test results for an existing study and a parametric study was performed for it. The established results were according to several parameters such as axial load ratio, slenderness ratio and wall length. The numerical results showed a different impact of… Show more
“…This is related to the concurrent occurrence of concrete crushing and reinforcement buckling in the compression zone, or concrete crushing in the compression zone before reinforcement yielding in the tension zone. As a result, the gap between the bending moment at the yielding point and the bending moment capacity gets shorter and shorter and the PHL reduction in axially loaded RCSWs occurs (Ren et al, 2022; Hashim et al, 2020). The impact of the ratio of the secondary cracking (rsc) on the PHL prediction cannot be interpreted using Figure 12(b) because the color points are intermingled and do not accurately depict the variation in the SHAP value with the alteration of the secondary cracking (rsc).Figure 12.SHAP method plots.…”
Reinforced concrete structural walls (RCSWs) are integral part of buildings and other structures and are used to carry in-plane and out-plane loads. For assessment purposes and to ensure safety and resilience of the structure, the curvature, capacity, and strain demands of RCSWs need to be estimated. Nonlinear numerical models are increasingly used in earthquake engineering design and assessment, where it is critical to develop high fidelity simulation tools to precisely forecast the global and local behavior of RCSWs. Plastic deformations concentrated in small regions, i.e. plastic hinges, and characterized through the concept of plastic hinge length (PHL), are conventionally used to define the inelastic response of RCSWs. In this study machine learning (ML) algorithms were leveraged in evaluating the PHL of RCSWs. A database containing 721 planar and nonplanar RCSW samples were utilized for training and testing ML models. Four different algorithms were employed, namely: XGBoost, CatBoost, Random Forest (RF), and genetic programming. The RF model outperformed other ML counterparts. The results of the best ML model were also compared against empirical equations available in literature and nonlinear regression analysis. The proposed ML model provided much better predictions than the existing relationships. Predictions of most empirical models were found to be extremely over (or under) conservative. The proposed ML model was also presented via an online tool where users can obtain the PHL of any RCSW simply by inputting wall parameters and clicking a run button.
“…This is related to the concurrent occurrence of concrete crushing and reinforcement buckling in the compression zone, or concrete crushing in the compression zone before reinforcement yielding in the tension zone. As a result, the gap between the bending moment at the yielding point and the bending moment capacity gets shorter and shorter and the PHL reduction in axially loaded RCSWs occurs (Ren et al, 2022; Hashim et al, 2020). The impact of the ratio of the secondary cracking (rsc) on the PHL prediction cannot be interpreted using Figure 12(b) because the color points are intermingled and do not accurately depict the variation in the SHAP value with the alteration of the secondary cracking (rsc).Figure 12.SHAP method plots.…”
Reinforced concrete structural walls (RCSWs) are integral part of buildings and other structures and are used to carry in-plane and out-plane loads. For assessment purposes and to ensure safety and resilience of the structure, the curvature, capacity, and strain demands of RCSWs need to be estimated. Nonlinear numerical models are increasingly used in earthquake engineering design and assessment, where it is critical to develop high fidelity simulation tools to precisely forecast the global and local behavior of RCSWs. Plastic deformations concentrated in small regions, i.e. plastic hinges, and characterized through the concept of plastic hinge length (PHL), are conventionally used to define the inelastic response of RCSWs. In this study machine learning (ML) algorithms were leveraged in evaluating the PHL of RCSWs. A database containing 721 planar and nonplanar RCSW samples were utilized for training and testing ML models. Four different algorithms were employed, namely: XGBoost, CatBoost, Random Forest (RF), and genetic programming. The RF model outperformed other ML counterparts. The results of the best ML model were also compared against empirical equations available in literature and nonlinear regression analysis. The proposed ML model provided much better predictions than the existing relationships. Predictions of most empirical models were found to be extremely over (or under) conservative. The proposed ML model was also presented via an online tool where users can obtain the PHL of any RCSW simply by inputting wall parameters and clicking a run button.
Post-tensioned precast concrete walls are an attractive research trend in structural engineering, which replaces cast-in-place concrete walls in earthquake-prone buildings. Precast concrete walls use mild steel and high-strength post-tensioning steel for flexural resistance. Mild steel reinforcement yields in tension and compression, dissipating inelastic energy. Unbounded tendons are used inside the wall to give self-centering capability, which lowers residual displacements. At the wall-foundation, horizontal slots are installed equally. Meanwhile, the middle-wall concrete is still anchored to the base. A three-dimensional finite element (FE) model is developed in this study to assess the lateral load response of shear walls with horizontal bottom slots. The seismic performance of three distinct walls is evaluated using the Abaqus software FEA. The model is validated by comparing the experimental data accessible in the literature. Furthermore, we investigate the effects of bottom slit length, steel strand position, initial prestressing level, and concrete strength. All of these criteria are critical for constructing structures with the new concept. The results of this study show that the three-dimensional finite element model accurately predicts all of the above-mentioned properties, including the lateral force–displacement response and toe area damage of self-centering shear walls.
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