Research on Shear Lag Effect of T-shaped Short-leg Shear Wall

Longitudinal displacement of cross section of T-shaped shortlegshear wall was simplified to three parts: shear lag warpingdisplacement, plane section bending displacement and axialdisplacement. Shear lag warping deformation was assumed ascubic parabola distribution along flange, and based on minimumpotential energy principle, differential equations were deduced;with boundary conditions, a calculation theory for shear lageffect was established. With two T-shaped short-leg shear wallmodels, vertical stresses of flanges were obtained by calculationtheory and finite element calculation respectively, and comparisonbetween theoretical analysis results and numerical calculationresults was made. At last, parameter analysis was carriedout, and the influence of shear force, shear span ratio andheight-thickness ratio on shear lag coefficient was obtained.Research shows that numerical calculation results are in goodagreement with theoretical analysis results, and each parameterhas different influence on shear lag coefficient

Machine Learning-Based Models for Shear Strength Prediction of UHPFRC Beams

Estimating shear strength is a crucial aspect of beam design. The goal of this research is to develop a shear strength calculation technique for ultra-high performance fiber reinforced concrete (UHPFRC) beams. To begin, a shear test database of 200 UHPFRC beam specimens is established. Then, random forest (RF) is used to evaluate the importance of influence factors for the shear strength of UHPFRC beams. Subsequently, three machine learning (ML)-based models, including artificial neural network (ANN), support vector regression (SVR), and eXtreme-gradient boosting (XGBoost), are proposed to compute shear strength. Results demonstrate that the area of longitudinal reinforcement has the greatest influence on the shear capacity of UHPFRC beams, and ten parameters with high importance (e.g., the area of longitudinal reinforcement, the stirrup strength, the cross-section area, the shear span ratio, fiber volume fraction, etc.) are selected as input parameters. The models of ANN, SVR, and XGBoost have close accuracy, and their R2 are 0.8825, 0.9016, and 0.8839, respectively, which are much larger than those of existing theoretical models. In addition, the average ratios of prediction values of ANN, SVR, and XGBoost models to experimental results are 1.08, 1.02, and 1.10, respectively; the coefficients of variation are 0.28, 0.21, and 0.28, respectively. The SVR model has the best accuracy and reliability. The accuracy and reliability of ML-based models are much better than those of existing models for calculating the shear strength of UHPFRC beams.Applied Science, Faculty ofNon UBCCivil Engineering, Department ofReviewedFacult

Seismic performance of concrete walls reinforced by high-strength bars: cyclic loading test and numerical simulation

This study examines the influence of cross-section shape on the seismic behaviour of high-strength steel reinforced concrete shear walls (HSS-RC) designed with Grade HRB 600 MPa reinforcement. As part of the study, two flexure-dominant walls with rectangular and T-shaped cross-sections, are tested under reversed cyclic loading. Seismic performance is evaluated by studying the failure characteristics, hysteretic curves, energy dissipation, ductility and reinforcing bar strains in the two walls. As part of the numerical study, two-dimensional (2D) and three-dimensional (3D) finite element modelling (FEM) are used to predict the seismic response of the rectangular and T-shaped walls, respectively. The test results show that compared to the rectangular wall, the flange in the T-shaped HSS-RC wall increased strength, energy dissipation and stiffness, but decreased ductility. The analytical hysteretic curves calculated using 2D and 3D FEM analyses show good agreement with the experimental test results.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Seismic performance of concrete walls reinforced by high-strength bars: cyclic loading test and numerical simulation

This study examines the influence of cross-section shape on the seismic behaviour of high-strength steel reinforced concrete shear walls (HSS-RC) designed with Grade HRB 600 MPa reinforcement. As part of the study, two flexure-dominant walls with rectangular and T-shaped cross-sections, are tested under reversed cyclic loading. Seismic performance is evaluated by studying the failure characteristics, hysteretic curves, energy dissipation, ductility, and reinforcing bar strains in the two walls. As part of the numerical study, two-dimensional (2D) and three-dimensional (3D) finite element modelling (FEM) are used to predict the seismic response of the rectangular and T-shaped walls, respectively. The test results show that compared to the rectangular wall, the flange in the T-shaped HSS-RC wall increased strength, energy dissipation and stiffness, but decreased ductility. The analytical hysteretic curves calculated using 2D and 3D FEM analyses show good agreement with the experimental test results. </jats:p

Seismic Shear Strength Prediction of Reinforced Concrete Shear Walls by Stacking Multiple Machine Learning Models

Reinforced concrete shear walls (RCSWs) are complicated to compute their shear capacity due to their large cross-sectional height-to-thickness ratios and the fact that they are subjected to vertical loads. Numerous factors influence RCSWs’ shear strength capacity, and the analytical models find it challenging to fully account for each factor’s impact on RCSWs’ shear-bearing capacity. Machine learning (ML) technology can deeply capture the mapping relationship between each input feature and the target value, and provide a more flexible and effective prediction method for RCSW shear-bearing capacity. To this end, a shear capacity test database containing 583 RCSW specimens was first established and characterized, and then the database was employed to train single, ensemble, and deep learning models for the shear strength of shear walls and combined with hyper-parameter tuning to enhance each model’s prediction performance, after which the prediction performance of each model was compared. Then, the ML models were contrasted with conventional techniques founded on the mechanical premise. Finally, in order to improve the prediction accuracy and reliability of the ML methods, the individually trained models were integrated into a stacking model using the stacking method, and the stacking model’s prediction performance was assessed. The results of this study show that in the single model, the test set R2 of the decision tree (DT) reaches 0.94, showing good trend-capturing ability. Among the ensemble models, Gradient Boosting (GB) performs the best and is comparable to DT in terms of RMSE and R2 and significantly outperforms other ensemble methods, such as Random Forest (RF) and Bagging. Deep Neural Networks (DNNs) show the strongest predictive ability among all models, with the lowest RMSE (263 kN) and a test R2 of 0.95, which is much better than the majority of ensemble models. The ML models show high accuracy and reliability compared to the traditional RC shear wall shear capacity models. The stacking model has an R2 of 0.98 and a CoV of 0.147 in the test set, and it is much better than other independent ML models (R2 = 0.88~0.95, CoV = 0.179~0.651)

Research on Concrete Columns Reinforced with New Developed High-Strength Steel under Eccentric Loading

The use of new developed high-strength steel in concrete members can reduce steel bar congestion and construction costs. This research aims to study the behavior of concrete columns reinforced with new developed high-strength steel under eccentric loading. Ten reinforced concrete columns were fabricated and tested. The test variables were the transverse reinforcement amount and yield strength, eccentricity, and longitudinal reinforcement yield strength. The failure patterns were compression and tensile failure for columns subjected to small eccentricity and large eccentricity, respectively. The same level of post-peak deformability and ductility could only be obtained with a lower amount of transverse reinforcement when high-strength transverse reinforcements were used in columns subjected to small eccentricity. The high-strength longitudinal reinforcement improved the bearing capacity and post-peak deformability of the concrete columns. Furthermore, three different equivalent rectangular stress block (ERSB) parameters for predicting the bearing capacity of columns with high-strength steel are discussed based on test and simulated results. It is concluded that the China Code GB 50010-2010 overestimates the bearing capacity of columns with high-strength steel, whereas the bearing capacities computed using the America Code ACI 318-14 and Canada Code CSA A23.3-04 agree well with the test results.</jats:p