Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data
Journal of Engineering, Design and Technology
ISSN: 1726-0531
Article publication date: 7 February 2022
Issue publication date: 4 March 2024
Abstract
Purpose
The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.
Design/methodology/approach
In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.
Findings
The five statistical indicators like coefficient of determination (R2), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.
Originality/value
The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.
Keywords
Acknowledgements
First and the foremost, the authors are thankful to Department of Civil Engineering, Manipal Institute of Technology, MAHE, Manipal, Karnataka, India. Similarly, authors are also thankful to the Senior Engineer Mr. Sourabh Lonkar from Hindustan Construction Company, Mumbai, India, for providing experience and knowledge and the authors are also thankful to Department of Civil Engineering, National Institute Technology Karnataka, Surthakal, Karnataka, India, for guidance.
Deceleration of competing Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Citation
Kamath, M.V., Prashanth, S., Kumar, M. and Tantri, A. (2024), "Machine-Learning-Algorithm to predict the High-Performance concrete compressive strength using multiple data", Journal of Engineering, Design and Technology, Vol. 22 No. 2, pp. 532-560. https://doi.org/10.1108/JEDT-11-2021-0637
Publisher
:Emerald Publishing Limited
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