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Computers and Concrete Volume 30, Number 2, August 2022 , pages 99-111 DOI: https://doi.org/10.12989/cac.2022.30.2.099 |
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Predicting sorptivity and freeze-thaw resistance of self-compacting mortar by using deep learning and k-nearest neighbor |
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Kazim Turk, Ceren Kina and Harun Tanyildizi
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| Abstract | ||
| In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freezethaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model. | ||
| Key Words | ||
| deep learning; durability factor; k-nearest neighbor; prediction; self-compacting mortar; sorptivity coefficient | ||
| Address | ||
| Kazim Turk: Department of Civil Engineering, Engineering Faculty, Inonu University, Malatya, Turkiye Ceren Kina: Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkiye Harun Tanyildizi: Department of Civil Engineering, Technology Faculty, Firat University, Elazig, Turkiye | ||