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Artificial neural network-based prediction of properties of self-compacting concrete containing limestone powder

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Abstract

Self-Compacting Concrete (SCC) is a fluid concrete that can be placed without any vibration. To the best of our knowledge, no method exists today that can accurately predict the properties of this type of concrete which is highly sensitive to small changes in the mix design, because its characteristics depend on several factors. The present study aims to use the neural network method for developing four artificial neural network models (ANN) for predicting the properties of SCC incorporating limestone powder (LP), while considering three cement strength classes: 32.5, 42.5, and 52.5 MPa. It is worth emphasizing that the compressive strength and some of the most essential characteristics of SCC, namely, the slump flow, V-funnel flow time, and L-box ratio, are the only outputs of the ANN models. On the basis of the structured and statistically examined experimental database, all the above-mentioned models were tested using the Matlab Levenberg–Marquardt backpropagation training algorithm. In addition, it should be noted that the experimental results reported by other researchers were compared to those obtained in this study for the purpose of assessing the predictive ability or generalization of the suggested models. High levels of agreement were found between these findings. The results given by the ANN models indicate that it is quite appropriate to use LP for the production of SCC with normal strength, irrespectively of the water-to-binder ratio and cement strength class, as long as they meet the workability and strength requirements. This would certainly encourage the tendency of promoting the use of self-compacting concrete in building industry. The ANN models elaborated and proposed in the present work can surely be adopted for the prediction of the properties of SCC. In addition, these models seem quite suitable in terms of precision levels, time, and cost savings.

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The authors thank the funding agency “Directorate-General for scientific research and technological development, Ministry of higher education and scientific research, Algiers, Algeria” for their financial support.

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  1. Geomaterials Laboratory, Civil Engineering Department, University of Blida1, Ouled Yaïch, Blida, 09000, Algeria

    Mahmoud Serraye & Said Kenai

  2. Civil Engineering Department, University of Sherbrooke, 2500 Boul. University Boulevard, Sherbrooke, QC, J1K 2R1, Canada

    Bakhta Boukhatem

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  1. Mahmoud Serraye
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Serraye, M., Boukhatem, B. & Kenai, S. Artificial neural network-based prediction of properties of self-compacting concrete containing limestone powder. Asian J Civ Eng 23, 817–839 (2022). https://doi.org/10.1007/s42107-022-00454-8

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