The present work demonstrates the nonlinear finite element analysis (NLFEA) of 13 concentrically and eccentrically loaded short rectangular concrete column specimens reinforced with GFRP and conventional steel bars. GFRP bars are lightweight having the high tensile strength and high corrosion resistance. An NLFEA model for the rectangular concrete specimens was developed using the commercial software ABAQUS Standard and calibrated for different materials and geometric parameters based on the previous experimental test results of the studied specimen. The behavior of reinforced concrete was modelled using the concrete damaged plasticity (CDP) model, the behavior of steel bars was simulated as a bilinear elastoplastic material, and the GFRP bars were considered as a linear elastic material. After the calibration of CDP parameters, the control sample was used for the further numerical parametric analysis to investigate the effect of critical parameters, i.e., the area of concrete (Ac), the compressive strength of concrete (fc′), and the ratio of longitudinal reinforcement (ρl) and transverse reinforcement (ρt) on the load-carrying capacity of columns. The results show that the selected NLFEA model can simulate the behavior of columns accurately and there was good agreement of numerical results obtained from ABAQUS Standard with the experimental results.
In light of recently published work highlighting the incompatibility between the concepts underlying current code specifications and fundamental concrete properties, the work presented herein focuses on assessing the ability of the methods adopted by some of the most widely used codes of practice for the design of reinforced concrete structures to provide predictions concerning load-carrying capacity in agreement with their experimentally established counterparts. A comparative study is carried out between the available experimental data and the predictions obtained from (1) the design codes considered, (2) a published alternative method (the compressive force path method), the development of which is based on assumptions different (if not contradictory) to those adopted by the available design codes, as well as (3) artificial neural networks that have been calibrated based on the available test data (the later data are presented herein in the form of a database). The comparative study reveals that the predictions of the artificial neural networks provide a close fit to the available experimental data. In addition, the predictions of the alternative assessment method are often closer to the available test data compared to their counterparts provided by the design codes considered. This highlights the urgent need to reassess the assumptions upon which the development of the design codes is based and identify the reasons that trigger the observed divergence between their predictions and the experimentally established values. Finally, it is demonstrated that reducing the incompatibility between the concepts underlying the development of the design methods and the fundamental material properties of concrete improves the effectiveness of these methods to a degree that calibration may eventually become unnecessary. Keywords Artificial neural network • Design codes • Ultimate limit state • RC beams • Compressive force path method • Physical models Abbreviations CFP Compressive force path ANN Artificial neural network ULS Ultimate limit state List of symbols a v Shear span b Beam width d Effective depth x Depth of the compressive zone A s Area of tensile reinforcement A s ′ Area of compressive reinforcement A sw Area of transverse reinforcement v ∕d Shear span-to-depth ratio f c Uniaxial compressive strength of concrete f yl Longitudinal reinforcement yield stress f yw Transverse reinforcement yield stress s Spacing between shear links l Ratio of tensile reinforcement (l = A s ∕b × d) t Ratio of compressive reinforcement (t = A s � ∕b × d) w Ratio of transverse reinforcement (w = A sw ∕b × s) V c Shear resistance of the RC beam without the contribution of the shear links V s Shear resistance offered by of shear links V u Shear developing along the span of the RC beam at failure M u Bending developing along the span of the RC beam at failure M f Flexural moment capacity of the cross section of the RC beam
This paper aims at establishing a framework for the development of artificial neural networks (ANNs) capable of realistically predicting the load-carrying capacity of reinforced concrete (RC) members. Multilayer back propagation neural networks are developed through the use of MATLAB and enriched databases which contain information describing the variation of load-carrying capacity in relation to key design parameters associated with the RC specimens (i.e. beams) considered. This work forms the basis for the development of a knowledge-based structural analysis tool capable of predicting RC structural response. A detailed discussion is provided on the different aspects of the proposed framework which include (1) the formation and analysis of the relevant (experimental) data, (2) the architecture of the ANNs, (3) the training/calibration process they undergo and finally, (4) ways of extending their applicability enabling them to predict the behaviour of RC structural forms with design parameters not represented in the available experimental database. Non-linear finite element analysis is used for validating the predictions of the ANN models developed. The comparative study reveals that the ANN models developed through the proposed framework are capable of effectively predicting the load-carrying capacity s of the RC structural forms considered quickly, accurately and without requiring significant computational resources. Keywords Artificial neural network • Database • Sampling method • Ultimate limit state • Reinforced concrete • Training process • Finite element analysis • Failure • Latin hypercube sampling List of symbols v Shear span b Width of the beam specimen cross-section d Effective depth of the beam specimen cross-section A s Area of longitudinal reinforcement acting in tension A sw Area of transverse reinforcement v ∕d Shear span to depth ratio f c Uniaxial compressive strength of concrete f yl Yield stress of longitudinal reinforcement bars f yw Yield stress of transverse reinforcement bars s Spacing between shear links l Ratio of tensile reinforcement (l = A s ∕b ⋅ d) w Ratio of transverse reinforcement (l = A sw ∕b ⋅ s) V u Shear strength Abbreviations CFP Compressive force path ANN Artificial neural network ULS Ultimate limit state LHS Latin hypercube sampling * Afaq Ahmad,
A comparison of the predicted and experimentally-established behaviour of over 150 reinforced concrete beam specimens (selected from 465 test results considered) revealed that around 20% of the specimens exhibited shear failure rather than the expected flexural failure. The work presented in this paper investigated the possibility that the causes of shear failure reflected shortcomings of the code methods adopted for calculating flexural capacity. It was found that the predicted values of flexural capacity tended to underestimate their experimentally-established counterparts by up to 17% on average. It was shown that by accounting for the triaxial stress conditions invariably developing in the compressive zone through a simple modification of code-proposed stress blocks, the correlation between predicted and experimental values was similar to the best possible one resulting from the development and use of an artificial neural network model.
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