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Machine learning for prediction of the uniaxial compressive strength within carbonate rocks

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Abstract

The Uniaxial Compressive Strength (UCS) is an essential parameter in various fields (e.g., civil engineering, geotechnical engineering, mechanical engineering, and material sciences). Indeed, the determination of UCS in carbonate rocks allows evaluation of its economic value. The relationship between UCS and numerous physical and mechanical parameters has been extensively investigated. However, these models lack accuracy, where as regional and small samples negatively impact these models' reliability. The novelty of this work is the use of state-of-the-art machine learning techniques to predict the Uniaxial Compressive Strength (UCS) of carbonate rocks using data collected from scientific studies conducted in 16 countries. The data reflect the rock properties including Ultrasonic Pulse Velocity, density and effective porosity. Machine learning models including Random Forest, Multi Layer Perceptron, Support Vector Regressor and Extreme Gradient Boosting (XGBoost) are trained and evaluated in terms of prediction performance. Furthermore, hyperparameter optimization is conducted to ensure maximum prediction performance. The results showed that XGBoost performed the best, with the lowest Mean Absolute Error (ranging from 17.22 to 18.79), the lowest Root Mean Square Error (ranging from 438.95 to 590.46), and coefficients of determination (R2) ranging from 0.91 to 0.94. The aim of this study was to improve the accuracy and reliability of models for predicting the UCS of carbonate rocks.

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Data availability

The dataset and associated source codes analyzed during the current study are available in the GitHub repository (https://github.com/RatebJabbar/uniaxial-compressive-strength-within-carbonate-rocks).

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Funding

This work received financial support from the “Ministère de l’Enseignement Supérieur et de la Recherche Scientifique en Tunisie”. Experimental assays were performed in the ‘Département des Sciences de la Terre’ of the ‘Faculté des Sciences de Sfax, Université de Sfax-Tunisie’.

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Authors and Affiliations

  1. Research Laboratory GEOMODELE (LR16ES17), Department of Earth Sciences, Faculty of Sciences, University of Sfax, BP 3000, Sfax, Tunisia

    Mohamed Abdelhedi & Chedly Abbes

  2. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Rateb Jabbar, Ahmed Ben Said & Noora Fetais

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  1. Mohamed Abdelhedi
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  2. Rateb Jabbar
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  3. Ahmed Ben Said
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  4. Noora Fetais
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  5. Chedly Abbes
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rateb Jabbar, Ahmed Ben Said, Noora Fetais and Chedly Abbes. The first draft of the manuscript was written by Mohamed Abdelhedi. All authors read and approved the final manuscript.

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Correspondence to Mohamed Abdelhedi.

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Abdelhedi, M., Jabbar, R., Said, A.B. et al. Machine learning for prediction of the uniaxial compressive strength within carbonate rocks. Earth Sci Inform 16, 1473–1487 (2023). https://doi.org/10.1007/s12145-023-00979-9

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