Machine learning unveils the complex nonlinearity of concrete materials’ uniaxial compressive strength

Abstract

Uniaxial compressive strength (CS), a mechanical property that depends on the type of fine and coarse aggregates, the water-cement (w/c) ratio, age, the volume of admixtures, etc., is difficult to estimate for concrete materials. Hence, an investigation on the application of machine learning (ML) techniques to predict the UCS of concrete precisely and affordably is described here. Different machine learning algorithms, including ensemble models like Classification and regression trees (CART), XGBoost (XGB), Bagging (Bagg), AdaBoost (AdaBo) and Random Forest Regression (RF), and non-ensemble models like Ridge regression (RR), Partial least square (PLS), K-Nearest Neighbors algorithm (KNN) were used. A total of 1206 experimental results datasets were collected. The key input parameters for the soft computing method, which produces the CS of concrete as the output, are composed of nine different elements. The quantity of cement, fine and coarse aggregate, amount of water, plasticizer, slump, and days of curing are some of the major input components. By comparing the ML-derived results with the experimental findings using various performance indices, the study demonstrates that the XGBoost model effectively approximates the CS of concrete materials with reliability and robustness. Furthermore, the research includes sensitivity analysis based on the developed optimal XGBoost model, revealing the influence of different concrete mix parameters on CS and highlighting the strongly nonlinear nature of concrete materials.

Keywords:

• Machine learning
• sensitivity analysis
• compressive strength
• XGBoost
• non-ensemble and ensemble algorithms

Acknowledgements

The authors would like to express their sincere gratitude to Tundi Power Company Pvt. Ltd. and the related personals in the Raughat-Mangale and Upper Raughat Hydro-Electric Project for their support during collection of the datasets. The authors express their gratitude to Dr. Maria Karoglou, a Research Assistant at the School of Chemical Engineering, National Technical University of Athens, for her careful review of the manuscript and valuable insights pertaining to the characteristics and complicated nature of concrete materials.

Authors’ contributions

Siddhi Pandey: conceptualization, methodology, software, investigation, formal analysis, writing – original draft, data curation, writing; Satish Paudel: conceptualization, methodology, software, investigation, formal analysis, writing – original draft, data curation, writing; Kabin Devkota: original draft; resources, software, validation; visualization; Kushum Kshetri: original draft, writing – review & editing; Panagiotis G. Asteris: methodology, supervision, writing – review & editing.

Disclosure statement

All authors disclosed no relevant relationships.

Data availability statement

Data will be made available on request.