Strength Prediction for Discontinuity Regions by Softened Strut-and-Tie Model

Hwang, Shyh‐Jiann; Lee, Hung-Jen

doi:10.1061/(asce)0733-9445(2002)128:12(1519)

Cited by 244 publications

(189 citation statements)

References 6 publications

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“…Using the concept of Lu and Wu [1], the constitutive equation is developed. Then, the constitutive equation is combined with the equilibrium equations of Hwang, and Lee [2]. Present model are used to predict the shear strength of reinforced concrete corbel.…”

Section: Discussionmentioning

confidence: 99%

“…The diagonal compression stress is defined in terms of the yield strength of reinforced steel and the compression strength of concrete. Then, the constitutive equation is combined with the equilibrium equations of Hwang and Lee [2].…”

Section: The Present Modelmentioning

confidence: 99%

“…It is based on the strut-and-tie concept with satisfying equilibrium, compatibility and the constitutive law of concrete. Hwang and Lee [2] modified the softened strut-and-tie model to develop a simple predicting procedure. But, the procedure is still complicate.…”

Section: Introductionmentioning

confidence: 99%

“…Using the concept of Lu and Wu [1], the constitutive equation is developed. Then, the constitutive equation is applied with the equilibrium equations of Hwang and Lee [2]. The results of the present model are compared with the results of ACI code and the experimental data from published papers [6,[8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A simplified shear strength evaluation model for reinforced concrete corbels

Lu¹,

Kuo²,

Lin³

et al. 2007

WIT Transactions on Modelling and Simulation, Vol 46

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A shear strength model for reinforced concrete corbels is developed by modifying the softened strut-and-tie model. Using the concept of Lu and Wu, the constitutive equation is developed. Then, the constitutive equation is combined with the equilibrium equations of Hwang and Lee. The model is used to predict the shear strength of reinforced concrete corbels. The results of the model are compared with the experimental data and the results of ACI code.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Present Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A simplified shear strength evaluation model for reinforced concrete corbels

Lu¹,

Kuo²,

Lin³

et al. 2007

WIT Transactions on Modelling and Simulation, Vol 46

View full text Add to dashboard Cite

show abstract

“…This model adopts a prismatic one with a uniform width. Since the situation of deep beams is similar to that of double corbels in terms of loading conformance and failure modes [33,34], and in order to reflect the behavior of deep beams failing in shear with sufficient flexural capacity, the width of diagonal strut suggested by Howang et al [34] is adopted here:…”

Section: Contribution Of Concrete Compressive Strengthmentioning

confidence: 99%

Alternative Strut and Tie Model for Reinforced Concrete Deep Beams

Al-Bayati

2018

NJES

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This paper presents a simple strut and tie model to calculate the shear strength of reinforced concrete deep beams. The proposed model assumes that the shear strength is the algebraic sum of three strength components: concrete diagonal strut, vertical stirrups, and horizontal web reinforcements. The contribution of each strength components was calibrated with the test results of 305 deep beams compiled from previous studies with wide range of geometrical and material properties. The predictions of the proposed model were compared with those of the current codes of practice (ACI-318-14 and ASHTOO 2014) and those of existing model in the literature. Comparisons revealed that the proposed model provided better predictions than other models. The mean of predicted strength to test of the proposed model, the ACI-318-14 model, the ASHTOO 2014 model were 0.98, 0.79, and 0.75, respectively. The corresponding standard deviations were 0.17, 0.28, and 0.49, respectively.

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Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques

Jeon

Shafieezadeh

DesRoches

2014

Earthq Engng Struct Dyn

106

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This paper proposes a new set of probabilistic joint shear strength models using the conventional multiple linear regression method, and advanced machine-learning methods of multivariate adaptive regression splines (MARS) and symbolic regression (SR). In order to achieve high-fidelity regression models with reduced model errors and bias, this study constructs extensive experimental databases for reinforced and unreinforced concrete joints by collecting existing beam-column joint subassemblage tests from multiple sources. Various influential parameters that affect joint shear strength such as material properties, design parameters, and joint configuration are investigated through tests of statistical significance. After performing a set of regression analyses, the comparison of simulation results indicates that MARS approach is the best estimation method. Moreover, the accuracy of analytical predictions of the derived MARS model is compared with that of existing joint shear strength relationships. The comparison results show that the proposed model is more accurate compared to existing relationships. This joint shear strength prediction model can be readily implemented into joint response models for evaluation of earthquake performance and inelastic responses of building frames.Global GCV for piecewise-linear = 0.017(0.018); total number of basis function = 21 (20); total effective number of parameters = 51(48.5). Note that the value in parenthesis is for piecewise-cubic.

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Strength Prediction for Discontinuity Regions by Softened Strut-and-Tie Model

Cited by 244 publications

References 6 publications

A simplified shear strength evaluation model for reinforced concrete corbels

A simplified shear strength evaluation model for reinforced concrete corbels

Alternative Strut and Tie Model for Reinforced Concrete Deep Beams

Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques

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