Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks
This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks (NNs). Applicability of NNs to evaluate the effects of FA and SF for a long period of time is investigated. The investigations...
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| Veröffentlicht in: | Construction & building materials Jg. 21; H. 2; S. 384 - 394 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.02.2007
Elsevier B.V |
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| ISSN: | 0950-0618, 1879-0526 |
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| Abstract | This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks (NNs). Applicability of NNs to evaluate the effects of FA and SF for a long period of time is investigated. The investigations covered concrete mixes at different water cementitious materials ratio, which contained low and high volumes of FA, and with or without the additional small amount of SF. 24 different mixes with 144 different samples were gathered form the literature for this purpose. These samples consist concretes that were cured for 3, 7, 28, 56 and 180 days. A NN model is constructed trained and tested using these data. The data used in the NN model are arranged in a format of eight input parameters that cover the fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA) and age of samples (AS) and an output parameter which is compressive strength of concrete (
f
c). A NN program was devised in MATLAB and the NN model was constructed in this program. The results showed that NNs have strong potential as a feasible tool for evaluation of the effect of cementitious material on the compressive strength of concrete. It was found that FA content contributed little at early ages but much at later ages to the strength of concrete. It can also be concluded that the enhancement effect of low content of SF on compressive strength was not significant. |
|---|---|
| AbstractList | This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks (NNs). Applicability of NNs to evaluate the effects of FA and SF for a long period of time is investigated. The investigations covered concrete mixes at different water cementitious materials ratio, which contained low and high volumes of FA, and with or without the additional small amount of SF. 24 different mixes with 144 different samples were gathered form the literature for this purpose. These samples consist concretes that were cured for 3, 7, 28, 56 and 180 days. A NN model is constructed trained and tested using these data. The data used in the NN model are arranged in a format of eight input parameters that cover the fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA) and age of samples (AS) and an output parameter which is compressive strength of concrete (
f
c). A NN program was devised in MATLAB and the NN model was constructed in this program. The results showed that NNs have strong potential as a feasible tool for evaluation of the effect of cementitious material on the compressive strength of concrete. It was found that FA content contributed little at early ages but much at later ages to the strength of concrete. It can also be concluded that the enhancement effect of low content of SF on compressive strength was not significant. The effects of fly ash (FA) and silica fume (SF) replacement content on the strength of concrete cured for a long period of time was studied using neural networks (NNs). The investigations covered concrete mixes with different water-cement ratios, and which contained low and high volumes of FA, with or without an additional small amount of SF. 24 different mixes with 144 different samples were sourced from the literature for this purpose. These samples consist of concretes that were cured for 3, 7, 28, 56 and 180 days. A NN model was constructed, trained and tested using these data. The data used in the NN model are arranged in a format of eight input parameters that cover the fly ash replacement ratio, silica fume replacement ratio, total cementitious material, fine aggregate, coarse aggregate, water content, high rate water reducing agent and age of samples and an output parameter which is the compressive strength of concrete (fc). A NN program was devised in MATLAB and the NN model was constructed in this program. The results showed that NNs are a feasible tool for evaluating the effect of cementitious material on the compressive strength of concrete. It was found that the FA content contributed little to the strength at early ages but much at later ages. It was also concluded that the enhancement effect of a low content of SF on compressive strength was not significant. 24 refs. |
| Audience | Trade |
| Author | Öztaş, Ahmet Özbay, Erdoğan Yuce, M. Ishak Pala, Murat |
| Author_xml | – sequence: 1 givenname: Murat surname: Pala fullname: Pala, Murat email: pala@gantep.edu.tr organization: Technical Programs Department, Kilis MYO, University of Gaziantep, 79000 Kilis, Turkey – sequence: 2 givenname: Erdoğan surname: Özbay fullname: Özbay, Erdoğan email: ozbay@gantep.edu.tr organization: Technical Programs Department, Kilis MYO, University of Gaziantep, 79000 Kilis, Turkey – sequence: 3 givenname: Ahmet surname: Öztaş fullname: Öztaş, Ahmet organization: Civil Engineering Department, University of Gaziantep, 27310 Gaziantep, Turkey – sequence: 4 givenname: M. Ishak surname: Yuce fullname: Yuce, M. Ishak organization: Civil Engineering Department, University of Gaziantep, 27310 Gaziantep, Turkey |
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| Keywords | Fly ash Compressive strength Scaled conjugate gradient algorithm Long-term cured concrete Neural networks Silica fume |
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| Snippet | This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by... The effects of fly ash (FA) and silica fume (SF) replacement content on the strength of concrete cured for a long period of time was studied using neural... |
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| SubjectTerms | Compressive strength Fly ash Long-term cured concrete Mechanical properties Neural networks Neurons Scaled conjugate gradient algorithm Silica fume |
| Title | Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks |
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