A robust time-dependent model of alkali-silica reaction at different temperatures

被引：16

作者：

Allahyari, Hamed ^{[1
]}

Heidarpour, Amin ^{[1
]}

Shayan, Ahmad ^{[2
]}

Vinh Phu Nguyen ^{[1
]}

机构：

[1] Monash Univ, Dept Civil Engn, Rm 125,23 Coll Walk, Melbourne, Vic 3800, Australia

[2] Concrete Analyt PTY LTD, Melbourne, Vic, Australia

来源：

CEMENT & CONCRETE COMPOSITES | 2020年 / 106卷

关键词：

Alkali-silica reaction; Concrete; Expansion; Accelerated test; Neural network; AGGREGATE SIZE; COMPRESSIVE STRENGTH; NEURAL-NETWORK; ASR EXPANSION; CONCRETE; SYSTEM; PREDICTION; STRAIN; DAMAGE;

D O I：

10.1016/j.cemconcomp.2019.103460

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Alkali-Silica-Reaction (ASR) is one of the most deteriorating phenomena in concrete structures. This study uses a machine learning approach (i.e. Artificial Neural Network) to provide further insight into ASR. The approach combines chemo-mechanical and kinetics-based approaches to develop a time- and temperature-dependent model of ASR, which is eventually used in generating user-friendly charts to conveniently assess existing concrete structures. To reach a higher degree of confidence in the precision of the model, an experimental dataset was developed from the laboratory and was combined with a dataset from the literature. A comparison between the developed model and a chemo-mechanical one (Gao's model) showed higher accuracy for the developed model. This higher accuracy was more obvious regarding the specimen with fine single-size aggregate grading. This study also reveals a varying thickness of connected porosity (t(c)) for fine single-size aggregate. Based on the results, aggregate size and t(c) have a coupled effect on the ASR-induced expansion.

引用

页数：18

共 73 条

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