“…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.
“…The bond slip at the column-bearing joint was simulated by a zero-length element. Based on the test phenomenon and the obtained data, the plastic hinge height of the specimen L p was 20 cm [32,33]. The height of the damage transition area was taken as 2 times L p .…”
To study the strengthening effect of carbon fiber composite materials (CFRP) on recycled concrete columns subjected to different levels of seismic damage, four column specimens were designed for pseudo-static tests. The four specimens were categorized as non-destructive without strengthening (prototype), non-destructive strengthening, medium seismic damage strengthening, and severe seismic damage strengthening based on the replacement rate of recycled aggregates and the level of seismic damage. The characteristics of the deformation damage and seismic performance indicators of each specimen were compared and analyzed. The results were verified on the OpenSees platform. A decrease was observed in the initial stiffness of the seismically damaged recycled concrete column specimens strengthened with CFRP, while the ductility, peak bearing capacity, and energy dissipation capacity of the specimens were improved. In addition, with the reduction in seismic damage, the ductility and energy dissipation capacity of the strengthened seismically damaged recycled concrete column specimens were enhanced to different degrees. In particular, the cumulative energy dissipation of the strengthened specimens exposed to medium seismic damage increased most significantly, by 32.5%. In general, the hysteretic curves of the strengthened specimens were full, and the average ductility coefficients were 4.1–6.8. CFRP strengthening was more effective for restoring and enhancing the performance of the recycled concrete column specimens with medium and lower seismic damage levels (displacement ratio ≤ 3%).
“…Hence, redistribution of the moment, becomes more effective. For this reason, each floor has to be manufactured monolithically for RC structures [3][4][5][6]. Accurately defining the plastic hinge zone is very hard and important, especially in analytical studies.…”
For many years, plastic hinges have been a very interesting and complex topic for researchers and engineers due to plastic deformations. Nonetheless, both standards and empirical relations are insufficient to determine the length of plastic hinges attached to concrete flexural elements. This study aims to analyze studies and formulas used for the calculation of plastic hinges in reinforced concrete (RC) columns and RC sheer walls. In addition, the study also evaluated the effect of plastic hinge behavior on other structural members. The study also evaluated plastic hinge length formulas and plastic hinge formulas of flexure elements either created with numerical modelling or presented with numerical models supported by empirical data. In addition, the data for columns and/or shear walls included in the literature have been evaluated in order to develop a unique formula that anticipates the length of plastic hinges of columns and/or shear walls. In this study, the use of Levenberg-Marquardt Algorithm based on artificial neural network (ANN) models in estimating the plastic hinge length of columns and shear walls was investigated. It was observed that the R 2 values of the model outputs are greater than 0.98. It has been determined that the developed ANN model is a practical and useful method for estimating the plastic hinge length of columns and shear walls, both.
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