Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs.

Muhammad Ali, Mubashir Aziz, Aaqib Ali, Usman Ali

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Muhammad AliDepartment of Civil Engineering, University of Engineering and Technology, Lahore, Pakistan.

Mubashir AzizDepartment of Civil and Environmental Engineering, College of Design and Built Environment, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia. mubashir.aziz@kfupm.edu.sa.

Aaqib AliDepartment of Civil and Environmental Engineering, College of Design and Built Environment, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia.

Usman AliDepartment of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The unconfined compressive strength of organic-rich clay shale is a fundamental parameter in geotechnical and energy applications, influencing drilling efficiency, wellbore stability, and excavation design. This study presents machine learning-based predictive models for unconfined compressive strength estimation, trained on a comprehensive dataset of 1217 samples that integrate non-destructive indicators such as ultrasonic pulse velocity, shale fabric metrics, wettability potential and destructive field-derived parameters. A dual-model framework was implemented using Support Vector Machine, Decision Tree, K-Nearest Neighbor, and Extreme Gradient Boosting (XGBoost) algorithms. Among these, the composite XGBoost model exhibited the highest accuracy (R

Indexed as

Machine learningRock drillingShaleUltrasonic pulse velocityUnconfined compressive strength

Identifiers

PMID40883383

DOI10.1038/s41598-025-15572-x

PMCPMC12397233

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.