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Predicting the Compressive Strength of Alkali-Activated Concrete Using Various Data Mining Methods

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Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 (CSCE 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 248))

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Abstract

Civil engineers have always considered the use of new data technologies in the construction industry. Several methods were applied to the analysis and sort the available massive data targeting accurate prediction for the performance. With the advent of alternative binding systems to ordinary cement, developing a predictive model that can offset the limitation in available data is a must. Hence, the paper focuses on predicting the compressive strength for alkali-activated slag-fly Ash concrete. Various machine learning techniques, including support vector machine and the artificial neural network, will be applied and compared. Also, the accuracy of each method will be analyzed based on the coefficient of determination (R2), root means square error (RMSE), maximum absolute error (MAE). It is anticipated that fully utilizing such new predictive techniques will provide engineers with a useful prediction tool.

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Authors and Affiliations

  1. Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada

    Mohammad Mohtasham Moein & Ahmed Soliman

Authors
  1. Mohammad Mohtasham Moein
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  2. Ahmed Soliman
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Editor information

Editors and Affiliations

  1. 200 UNIVERSITY AVE W, University of Waterloo, Waterloo, ON, Canada

    Scott Walbridge

  2. Environmental Engineering, Building, Concordia University, Civil, Montreal, QC, Canada

    Mazdak Nik-Bakht

  3. University of Regina, REGINA, SK, Canada

    Kelvin Tsun Wai Ng

  4. Matrix Solutions Calgary, CALGARY, AB, Canada

    Manas Shome

  5. OKANAGAN CAMPUS, University of British Columbia, KELOWNA, BC, Canada

    M. Shahria Alam

  6. THE UNIVERSITY OF WESTERN ONTARIO, ONTARIO, ON, Canada

    Ashraf el Damatty

  7. OKANAGAN CAMPUS, University of British Columbia, KELOWNA, BC, Canada

    Gordon Lovegrove

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© 2023 Canadian Society for Civil Engineering

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Moein, M.M., Soliman, A. (2023). Predicting the Compressive Strength of Alkali-Activated Concrete Using Various Data Mining Methods. In: Walbridge, S., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 . CSCE 2021. Lecture Notes in Civil Engineering, vol 248. Springer, Singapore. https://doi.org/10.1007/978-981-19-1004-3_26

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  • DOI: https://doi.org/10.1007/978-981-19-1004-3_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1003-6

  • Online ISBN: 978-981-19-1004-3

  • eBook Packages: EngineeringEngineering (R0)

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