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Prediction of the drying shrinkage of alkali-activated materials using artificial neural networks

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Title: Prediction of the drying shrinkage of alkali-activated materials using artificial neural networks
Authors: Kong, Y. K. Browse this author
Kurumisawa, Kiyofumi Browse this author →KAKEN DB
Keywords: Alkali-activated materials
Artificial Neural Networks
Drying shrinkage
Mix design
Issue Date: Dec-2022
Publisher: Elsevier
Journal Title: Case Studies in Construction Materials
Volume: 17
Start Page: e01166
Publisher DOI: 10.1016/j.cscm.2022.e01166
Abstract: Alkali-activated materials (AAMs) are qualitatively and quantitatively evaluated with an emphasis on the ultimate drying shrinkage. We systematically evaluated AAMs based on the mix design and curing conditions, utilizing a total of 452 AAM mixtures extracted from 44 papers. Finally, a predictive model for the ultimate drying shrinkage of AAMs was constructed using an artificial neural network (ANN) with high accuracy, in which the reactivity of binder, geopolymer paste volume, liquid-to-binder ratio, alkali activator modulus, aggregate volumetric ratio, curing temperature, relative humidity and specimen size were set as inputs. This model shows great generality by compiling various AAM mixtures and is easy-handling without preparation of samples for acquiring specific properties. Moreover, the efficiency of three commonly used models for predicting the drying shrinkage-the Bazant-Baweja model, Gardner and Lockman model, and multi-linear regression model-were evaluated and compared to the proposed ANN model, revealing a better prediction performance of ANN model. This study will advance the understanding of the drying shrinkage behaviors of AAMs and provide practical guidelines for designing AAM mixtures with high durability.
Type: article
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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