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

Hamed Allahyari, Amin Heidarpour, Ahmad Shayan, Vinh Phu Nguyen

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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 (tc) for fine single-size aggregate. Based on the results, aggregate size and tc have a coupled effect on the ASR-induced expansion.

Original languageEnglish
Article number103460
Number of pages18
JournalCement and Concrete Composites
Volume106
DOIs
Publication statusPublished - 1 Feb 2020

Keywords

  • Accelerated test
  • Alkali-silica reaction
  • Concrete
  • Expansion
  • Neural network

Cite this

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abstract = "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 (tc) for fine single-size aggregate. Based on the results, aggregate size and tc have a coupled effect on the ASR-induced expansion.",
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A robust time-dependent model of alkali-silica reaction at different temperatures. / Allahyari, Hamed; Heidarpour, Amin; Shayan, Ahmad; Nguyen, Vinh Phu.

In: Cement and Concrete Composites, Vol. 106, 103460, 01.02.2020.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Heidarpour, Amin

AU - Shayan, Ahmad

AU - Nguyen, Vinh Phu

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N2 - 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 (tc) for fine single-size aggregate. Based on the results, aggregate size and tc have a coupled effect on the ASR-induced expansion.

AB - 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 (tc) for fine single-size aggregate. Based on the results, aggregate size and tc have a coupled effect on the ASR-induced expansion.

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