Techno Press
You logged in as Techno Press

Computers and Concrete
  Volume 24, Number 6, December 2019 , pages 541-553
DOI: https://doi.org/10.12989/cac.2019.24.6.541
 


Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network
Thuc Nhu Nguyen, Yang Yu, Jianchun Li, Nadarajah Gowripalan and Vute Sirivivatnanon

 
Abstract
    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.
 
Key Words
    Alkali Silica Reaction (ASR); elastic modulus; Artificial Neural Network (ANN); Bayesian regularization
 
Address
Thuc Nhu Nguyen, Yang Yu, Jianchun Li, Nadarajah Gowripalan and Vute Sirivivatnanon: Centre for Built Infrastructure Research, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2024 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com