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- 저자 : Vute Sirivivatnanon (검색결과 2 건)
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Elasto-plastic damage modelling of beams and columns with mechanical degradation
R. Emre Erkmen,Nadarajah Gowripalan,Vute Sirivivatnanon 사단법인 한국계산역학회 2017 Computers and Concrete, An International Journal Vol.19 No.3
Within the context of continuum mechanics, inelastic behaviours of constitutive responses are usually modelled by using phenomenological approaches. Elasto-plastic damage modelling is extensively used for concrete material in the case of progressive strength and stiffness deterioration. In this paper, a review of the main features of elasto-plastic damage modelling is presented for uniaxial stress-strain relationship. It has been reported in literature that the influence of Alkali-Silica Reaction (ASR) can lead to severe degradations in the modulus of elasticity and compression strength of the concrete material. In order to incorporate the effects of ASR related degradation, in this paper the constitutive model of concrete is based on the coupled damage-plasticity approach where degradation in concrete properties can be captured by adjusting the yield and damage criteria as well as the hardening moduli related parameters within the model. These parameters are adjusted according to results of concrete behaviour from the literature. The effect of ASR on the dynamic behaviour of a beam and a column are illustrated under moving load and cyclic load cases.
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Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network
Thuc Nhu Nguyen,Yang Yu,Jianchun Li,Nadarajah Gowripalan,Vute Sirivivatnanon 사단법인 한국계산역학회 2019 Computers and Concrete, An International Journal Vol.24 No.6
Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.
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