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Buildings 2019, 9(1), 10; https://doi.org/10.3390/buildings9010010

Artificial Neural Network Estimation of the Effect of Varying Curing Conditions and Cement Type on Hardened Concrete Properties

1
Department of Civil Engineering, Faculty of Engineering and Architecture, Kastamonu University, 37150 Kastamonu, Turkey
2
Departmentof Materials Science and Engineering, Institute of Science, Kastamonu University, 37150 Kastamonu, Turkey
*
Author to whom correspondence should be addressed.
Received: 28 November 2018 / Revised: 16 December 2018 / Accepted: 19 December 2018 / Published: 3 January 2019
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Abstract

The use of mineral admixtures and industrial waste as a replacement for Portland cement is recognized widely for its energy efficiency along with reduced CO2 emissions. The use of materials such as fly ash, blast-furnace slag or limestone powder in concrete production makes this process a sustainable one. This study explored a number of hardened concrete properties, such as compressive strength, ultrasonic pulse velocity, dynamic elasticity modulus, water absorption and depth of penetration under varying curing conditions having produced concrete samples using Portland cement (PC), slag cement (SC) and limestone cement (LC). The samples were produced at 0.63 and 0.70 w/c (water/cement) ratios. Hardened concrete samples were then cured under three conditions, namely standard (W), open air (A) and sealed plastic bag (B). Although it was found that the early-age strength of slag cement was lower, it was improved significantly on 90th day. In terms of the effect of curing conditions on compressive strength, cure W offered the highest compressive strength, as expected, while cure A offered slightly lower compressive strength levels. An increase in the w/c ratio was found to have a negative impact on pozzolanic reactions, which resulted in poor hardened concrete properties. Furthermore, carbonation effect was found to have positive effects on some of the concrete properties, and it was observed to have improved the depth of water penetration. Moreover, it was possible to estimate the compressive strength with high precision using artificial neural networks (ANN). The values of the slopes of the regression lines for training, validating and testing datasets were 0.9881, 0.9885 and 0.9776, respectively. This indicates the high accuracy of the developed model as well as a good correlation between the predicted compressive strength values and the experimental (measured) ones. View Full-Text
Keywords: slag cement; portland limestone cement; curing; artificial neural networks; w/c slag cement; portland limestone cement; curing; artificial neural networks; w/c
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kaplan, G.; Yaprak, H.; Memiş, S.; Alnkaa, A. Artificial Neural Network Estimation of the Effect of Varying Curing Conditions and Cement Type on Hardened Concrete Properties. Buildings 2019, 9, 10.

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