Computers and Concrete

  • 제24권6호
  • /
  • Pages.541-553
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  • 2019
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network

  • Nguyen, Thuc Nhu (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Yu, Yang (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Li, Jianchun (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Gowripalan, Nadarajah (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Sirivivatnanon, Vute (Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney)
  • 투고 : 2019.01.28
  • 심사 : 2019.11.18
  • 발행 : 2019.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제 주관 기관 : Australian Research Council

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