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Compressive strength prediction of high-strength concrete using machine learning

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Abstract

For decades, concrete has been one of the most used materials in the construction industry around the world. Concrete consists of various materials of which cement is an essential component, majorly contributing in better bonding between aggregates and contributes in strength. But when it comes to sustainability, this material has contributed about 8% of the total world \({CO}_{2}\) emission while manufactured. Proportions of various chemical and mineral admixtures, plasticizers, and fibers were analyzed to achieve high strength, satisfactory durability, and sustainable concrete. Various machine learning (ML) techniques were used for carrying out these tasks. Because of the absence of any empirical relation between the compressive strength of concrete and the new and upcoming concrete mixtures, machine learning techniques have been put to use for the predictions of various mechanical properties of concrete. Numerous ML algorithms right from basic linear regressions to individual support vector regressions (SVR) to ensemble methods like bagging and boosting have been used for which appropriate train-test split is employed for reducing overfitting. We aim to reduce the dearth of knowledge pertaining to this specific domain by our comprehensive research. A number of different ML models are used and critically compared based on their predictions. These comparison results conclude random forest, extra tree, and decision tree regression methods to be the most accurate in predicting the compressive strength of concrete. The highest accuracy of 99% was achieved by this research using the extra tree regressor. It was also able to record an R2 score of 0.937 indicating the ability of the model to capture more complex patterns from the data and to explain the higher variability in the data. The mean absolute error of 2.93 was observed which shows the predicted results’ close proximity to the actual values.

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Acknowledgements

The authors are grateful to Department of Computer Engineering, Nirma University, and Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University, for the permission to publish this research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat, India

    Manan Davawala & Tanmay Joshi

  2. Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Manan Shah

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  1. Manan Davawala
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All the authors make a substantial contribution to this manuscript. MD, TS, and MS participated in drafting the manuscript. MD, TS, and MS wrote the main manuscript. All the authors discussed the results and implication on the manuscript at all stages.

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Correspondence to Manan Shah.

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Davawala, M., Joshi, T. & Shah, M. Compressive strength prediction of high-strength concrete using machine learning. emergent mater. 6, 321–335 (2023). https://doi.org/10.1007/s42247-022-00409-4

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