scholarly journals STUDY ON SHEAR STRENGTH OF COLUMNS AND BEAMS SURROUNDING A WALL PANEL OF REINFORCED-CONCRETE FRAMED SHARE WALLS

2015 ◽  
Vol 80 (715) ◽  
pp. 1435-1445
Author(s):  
Norikazu ONOZATO ◽  
Yukichi KANEHIRA
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Wall Panel

Shear strength of reinforced concrete beams with T-headed bars for safety-related nuclear structures

2021 ◽  
Vol 230 ◽  
pp. 111705
Author(s):  
Yuxing Yang ◽  
Amit H. Varma ◽  
Michael E. Kreger ◽  
Ying Wang ◽  
Kai Zhang
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
Nuclear Structures

Critical shear crack theory-based punching shear model for FRP-reinforced concrete slabs

2020 ◽  
pp. 136943322097814
Author(s):  
Xing-lang Fan ◽  
Sheng-jie Gu ◽  
Xi Wu ◽  
Jia-fei Jiang
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Shear Crack ◽  
Concrete Strength ◽  
Weight Ratio ◽  
Punching Shear ◽  
Concrete Slabs ◽  
Crack Theory ◽  
Reinforced Concrete Slabs ◽  
Rc Slabs

Owing to their high strength-to-weight ratio, superior corrosion resistance, and convenience in manufacture, fiber-reinforced polymer (FRP) bars can be used as a good alternative to steel bars to solve the durability issue in reinforced concrete (RC) structures, especially for seawater sea-sand concrete. In this paper, a theoretical model for predicting the punching shear strength of FRP-RC slabs is developed. In this model, the punching shear strength is determined by the intersection of capacity and demanding curve of FRP-RC slabs. The capacity curve is employed based on critical shear crack theory, while the demand curve is derived with the help of a simplified tri-linear moment-curvature relationship. After the validity of the proposed model is verified with experimental data collected from the literature, the effects of concrete strength, loading area, FRP reinforcement ratio, and effective depth of concrete slabs are evaluated quantitatively.

scholarly journals Shear Strength of Internal Reinforced Concrete Beam-Column Joints: Intelligent Modeling Approach and Sensitivity Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
De-Cheng Feng ◽  
Bo Fu
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Influence Factors ◽  
Regression Tree ◽  
Reinforced Concrete Beam ◽  
Gradient Boosting ◽  
Intelligent Modeling ◽  
Modeling Approach ◽  
Ensemble Machine Learning ◽  
Strength Models

In this paper, an intelligent modeling approach is presented to predict the shear strength of the internal reinforced concrete (RC) beam-column joints and used to analyze the sensitivity of the influence factors on the shear strength. The proposed approach is established based on the famous boosting-family ensemble machine learning (ML) algorithms, i.e., gradient boosting regression tree (GBRT), which generates a strong predictive model by integrating several weak predictors, which are obtained by the well-known individual ML algorithms, e.g., DT, ANN, and SVM. The strong model is boosted as each weak predictor has its own weight in the final combination according to the performance. Compared with the conventional mechanical-driven shear strength models, e.g., the well-known modified compression field theory (MCFT), the proposed model can avoid the complicated derivation process of shear mechanism and calibration of the involved empirical parameters; thus, it provides a more convenient, fast, and robust alternative way for predicting the shear strength of the internal RC joints. To train and test the GBRT model, a total of 86 internal RC joint specimens are collected from the literatures, and four traditional ML models and the MCFT model are also employed as comparisons. The results indicate that the GBRT model is superior to both the traditional ML models and MCFT model, as its degree-of-fitting is the highest and the predicting dispersion is the lowest. Finally, the model is used to investigate the influences of different parameters on the shear strength of the internal RC joint, and the sensitivity and importance of the corresponding parameters are obtained.

scholarly journals Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids

2021 ◽  
Vol 11 (6) ◽  
pp. 2736
Author(s):  
Min Sook Kim ◽  
Young Hak Lee
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Fiber Reinforced Polymer ◽  
Punching Shear ◽  
Test Results ◽  
Shear Reinforcement ◽  
Flat Plates ◽  
Reinforced Polymer ◽  
Glass Fiber Reinforced ◽  
Vertical Grids

In this study, the structural behavior of reinforced concrete flat plates shear reinforced with vertical grids made of a glass fiber reinforced polymer (GFRP) was experimentally evaluated. To examine the shear strength, experiments were performed on nine concrete slabs with different amounts and spacings of shear reinforcement. The test results indicated that the shear strength increased as the amount of shear reinforcement increased and as the spacing of the shear reinforcement decreased. The GFRP shear reinforcement changed the cracks and failure mode of the specimens from a brittle punching to flexure one. In addition, the experimental results are compared with a shear strength equation provided by different concrete design codes. This comparison demonstrates that all of the equations underestimate the shear strength of reinforced concrete flat plates shear reinforced with GFRP vertical grids. The shear strength of the equation by BS 8110 is able to calculate the punching shear strength reasonably for a concrete flat plate shear reinforced with GFRP vertical grids.

Numerical analysis of the shear strength of circular reinforced concrete columns subjected to cyclic lateral loads using linear genetic programming

2020 ◽  
Vol 37 (7) ◽  
pp. 2517-2537
Author(s):  
Mostafa Rezvani Sharif ◽  
Seyed Mohammad Reza Sadri Tabaei Zavareh
Keyword(s):  
Numerical Modeling ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Numerical Analysis ◽  
Genetic Programming ◽  
Linear Genetic Programming ◽  
Rc Columns ◽  
Content Type ◽  
Alternative Approach ◽  
Engineering Problems

Purpose The shear strength of reinforced concrete (RC) columns under cyclic lateral loading is a crucial concern, particularly, in the seismic design of RC structures. Considering the costly procedure of testing methods for measuring the real value of the shear strength factor and the existence of several parameters impacting the system behavior, numerical modeling techniques have been very much appreciated by engineers and researchers. This study aims to propose a new model for estimation of the shear strength of cyclically loaded circular RC columns through a robust computational intelligence approach, namely, linear genetic programming (LGP). Design/methodology/approach LGP is a data-driven self-adaptive algorithm recently used for classification, pattern recognition and numerical modeling of engineering problems. A reliable database consisting of 64 experimental data is collected for the development of shear strength LGP models here. The obtained models are evaluated from both engineering and accuracy perspectives by means of several indicators and supplementary studies and the optimal model is presented for further purposes. Additionally, the capability of LGP is examined to be used as an alternative approach for the numerical analysis of engineering problems. Findings A new predictive model is proposed for the estimation of the shear strength of cyclically loaded circular RC columns using the LGP approach. To demonstrate the capability of the proposed model, the analysis results are compared to those obtained by some well-known models recommended in the existing literature. The results confirm the potential of the LGP approach for numerical analysis of engineering problems in addition to the fact that the obtained LGP model outperforms existing models in estimation and predictability. Originality/value This paper mainly represents the capability of the LGP approach as a robust alternative approach among existing analytical and numerical methods for modeling and analysis of relevant engineering approximation and estimation problems. The authors are confident that the shear strength model proposed can be used for design and pre-design aims. The authors also declare that they have no conflict of interest.

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