Examining the Behaviour of a Reinforced Concrete System Under Horizontal Loads and Determination of Preliminary Design Reinforcement Ratios

Koç, Varol; Emiroğlu, Yusuf

doi:10.1007/s40999-017-0208-5

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…The total stiffness and the frequency of the structure, obtained from Eqs. (26) and (27) for different instants of time within the assumed convergence period, can be found in Fig. 4a and b, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…It is worth mentioning that this method can be applied to any structure that can be converted into a unidimensional model that is sufficiently long to suffer bending. Moreover, the formulations used to obtain the generalized properties of the system have nonlinear characteristics regarding the geometry and material, which is essential to the examination of reinforced concrete systems, as affirmed by Koç and Emiroğlu [26].…”

Section: Mathematical Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Stress assessment in reinforcement for columns with concrete creep and shrinkage through Brazilian technical normative

Wahrhaftig

Magalhães

Nascimento

2020

J Braz. Soc. Mech. Sci. Eng.

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In this paper, the induced stress in longitudinal reinforcement bars with cross sections subjected to concrete creep and shrinkage was evaluated. The study was performed for a reinforced concrete column, with loading varying from zero-in which the structure is exclusively subjected to self-weight-to forces close to the loss of stability by bifurcation of equilibrium. A real precast concrete pole, extremely slender, with geometry varying lengthwise, was the structure chosen for the analysis. The aspects related to the material nonlinearity, as well as creep, shrinkage and the development of characteristic concrete strength, were taken into account following Brazilian normative recommendations in NBR 6118:2014 (Design of structural concrete-Procedure) by the Brazilian Association of Technical Standards (ABNT). The critical buckling load was defined according to the structural frequency by using particularly nonlinear formulations. The results were obtained for different instants without exceeding the expected time for convergence of results. The maximum relative value found for the reinforcement stress was 1.24% of the steel yield strength, eliminating the possibility of yielding material.

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Section: Resultsmentioning

confidence: 99%

Section: Mathematical Modelingmentioning

confidence: 99%

Stress assessment in reinforcement for columns with concrete creep and shrinkage through Brazilian technical normative

Wahrhaftig

Magalhães

Nascimento

2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Initially, constitutive models with continuum representations were utilized for engineering modelling of masonry yielding acceptable estimations. However, the inhomogeneous composition is better simulated using discontinuous micromodelling approaches, and thus such techniques were frequently used in the past two decades [18][19][20]. Even so, collating the multitude of compositional and structural variables inside the finite/discrete element method (F/DEM) to solve equations of motions for material deformation, contact point interactions, failure load, and crack propagation remains a daunting task.…”

Section: Introductionmentioning

confidence: 99%

Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels

Glushakova

Liu

Zhang

et al. 2020

Advances in Civil Engineering

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The intricate interplay between the microscopic constituents and their macroscopic properties for masonry structures complicates their failure analysis modelling. A composite strategy incorporating neural network (NN) and cellular automata (CA) is developed to predict the failure load for masonry panels with and without openings subjected to lateral loadings. The discretized panels are modelled by the CA methodology using nine neighbour cells, which derive their state values from geometric parameters and opening location placement for the panels. An identification coefficient dictated by these geometric parameters and experimental data is fed together as the input training data for the NN. The NN uses a backpropagation algorithm and two hidden layers with sigmoid activation functions to predict failure loads. This method achieves greater accuracy in prediction when compared with the yield line and finite elemental analysis (FEA) methods. The results attained elucidate the feasibility of the current methodology to complement conventional approaches such as FEA to provide additional insight into the failure mechanism of masonry panels under varied loading conditions.

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Examining the Behaviour of a Reinforced Concrete System Under Horizontal Loads and Determination of Preliminary Design Reinforcement Ratios

Cited by 2 publications

References 27 publications

Stress assessment in reinforcement for columns with concrete creep and shrinkage through Brazilian technical normative

Stress assessment in reinforcement for columns with concrete creep and shrinkage through Brazilian technical normative

Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels

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