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2023 | OriginalPaper | Buchkapitel

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

verfasst von : Mohammad Mohtasham Moein, Ahmed Soliman

Erschienen in: Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021

Verlag: Springer Nature Singapore

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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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Titel: Predicting the Compressive Strength of Alkali-Activated Concrete Using Various Data Mining Methods
verfasst von: Mohammad Mohtasham Moein
Ahmed Soliman
Verlag: Springer Nature Singapore
Buch: Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021
Print ISBN: 978-981-19-1003-6

Electronic ISBN: 978-981-19-1004-3

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-19-1004-3_26

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