Machine Learning-Based Estimation of Concrete Compressive Strength: A Multi-Model and Multi-Dataset Study

Document Type : Research Papers

Authors

Ph.D. Instructor, Faculty of Civil Engineering, Duy Tan University, Da Nang, Vietnam.

Abstract

Concrete is a commonly used construction material due to its favourable engineering properties, such as high compressive strength, good durability, and resistance to corrosion. Accurate predictions of the compressive strength of this material significantly reduce the time and effort required by laboratory tests. The current paper aims to compare the performance of prominent machine learning-based approaches used for predicting the compressive strength of concrete. In addition, 11 historical datasets, collected from the literature, are used. The diversity of the input features, the data dimensionality, and the number of instances can be helpful to evaluate the generalization capability of the employed machine learning models. Repetitive data sampling processes, consisting of 20 independent runs, are carried out to obtain the machine learning models’ performances. Through experiments, it can be shown that the gradient boosting machines attain the best performance. Notably, the extreme gradient boosting machine has achieved the best outcome in five historical datasets.

Keywords

Main Subjects

References

 
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Civil Engineering Infrastructures Journal
Volume 57, Issue 2
December 2024
Pages 247-265

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History

  • Receive Date: 31 January 2023
  • Revise Date: 24 May 2023
  • Accept Date: 12 June 2023

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