• Title/Summary/Keyword: variable angle truss

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Effective torsional strength of axially restricted RC beams

  • Taborda, Catia S.B.;Bernardo, Luis F.A.;Gama, Jorge M.R.
    • Structural Engineering and Mechanics
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    • v.67 no.5
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    • pp.465-479
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    • 2018
  • In a previous study, design charts where proposed to help the torsional design of axially restricted reinforced concrete (RC) beams with squared cross section. In this article, new design charts are proposed to cover RC beams with rectangular cross section. The influence of the height to width ratio of the cross section on the behavior of RC beams under torsion is firstly shown by using theoretical and experimental results. Next, the effective torsional strength of a reference RC beam is computed for several values and combinations of the study variables, namely: height to width ratio of the cross section, concrete compressive strength, torsional reinforcement ratio and level of the axial restraint. To compute the torsional strength, the modified Variable Angle Truss Model for axially restricted RC beams is used. Then, an extensive parametric analysis based on multivariable and nonlinear correlation analysis is performed to obtain nonlinear regression equations which allow to build the new design charts. These charts allow to correct the torsional strength in order to consider the favourable influence of the compressive axial stress that arises from the axial restraint.

Prestressed concrete beams under torsion-extension of the VATM and evaluation of constitutive relationships

  • Bernardo, Luis F.A.;Andrade, Jorge M.A.
    • Structural Engineering and Mechanics
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    • v.61 no.5
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    • pp.577-592
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    • 2017
  • A computing procedure is presented to predict the ultimate behavior of prestressed beams under torsion. This computing procedure is based on an extension of the Variable Angle Truss-Model (VATM) to cover both longitudinal and transversal prestressed beams. Several constitutive relationships are tested to model the behavior of the concrete in compression in the struts and the behavior of the reinforcement in tension (both ordinary and prestress). The theoretical predictions of the maximum torque and corresponding twist are compared with some results from reported tests and with the predictions obtained from some codes of practice. One of the tested combinations of the relationships for the materials was found to give simultaneously the best predictions for the resistance torque and the corresponding twist of prestressed beams under torsion. When compared with the predictions from some codes of practice, the theoretical model which incorporates the referred combination of the relationships provides best values for the torsional strength and leads to more optimized designs.

Probabilistic shear strength models for reinforced concrete beams without shear reinforcement

  • Song, Jun-Ho;Kang, Won-Hee;Kim, Kang-Su;Jung, Sung-Moon
    • Structural Engineering and Mechanics
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    • v.34 no.1
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    • pp.15-38
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    • 2010
  • In order to predict the shear strengths of reinforced concrete beams, many deterministic models have been developed based on rules of mechanics and on experimental test results. While the constant and variable angle truss models are known to provide reliable bases and to give reasonable predictions for the shear strengths of members with shear reinforcement, in the case of members without shear reinforcement, even advanced models with complicated procedures may show lack of accuracy or lead to fairly different predictions from other similar models. For this reason, many research efforts have been made for more accurate predictions, which resulted in important recent publications. This paper develops probabilistic shear strength models for reinforced concrete beams without shear reinforcement based on deterministic shear strength models, understanding of shear transfer mechanisms and influential parameters, and experimental test results reported in the literature. Using a Bayesian parameter estimation method, the biases of base deterministic models are identified as algebraic functions of input parameters and the errors of the developed models remaining after the bias-correction are quantified in a stochastic manner. The proposed probabilistic models predict the shear strengths with improved accuracy and help incorporate the model uncertainties into vulnerability estimations and risk-quantified designs.