Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach
Abstract Rock aggregates have been extensively exploited in the construction sector, and the associated engineering features play a critical role in their application. The main aim of this research is to assess the impact of petrographic characteristics on the engineering properties of carbonate roc...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83476-3 |
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| author | Javid Hussain Tehseen Zafar Xiaodong Fu Nafees Ali Jian Chen Fabrizio Frontalini Jabir Hussain Xiao Lina George Kontakiotis Olga Koumoutsakou |
| author_facet | Javid Hussain Tehseen Zafar Xiaodong Fu Nafees Ali Jian Chen Fabrizio Frontalini Jabir Hussain Xiao Lina George Kontakiotis Olga Koumoutsakou |
| author_sort | Javid Hussain |
| collection | DOAJ |
| description | Abstract Rock aggregates have been extensively exploited in the construction sector, and the associated engineering features play a critical role in their application. The main aim of this research is to assess the impact of petrographic characteristics on the engineering properties of carbonate rocks. A total of 45 carbonate rock samples from different geological formations within the Salt Range (Western Himalayan Ranges, Pakistan) were subjected to comprehensive petrographic analyses and standard aggregate quality control tests. The engineering characteristics encompassed Los Angeles abrasion value, aggregate crushing value, aggregate impact value, specific gravity, water absorption, and unconfined compressive strength, whereas petrographic examination of thin sections quantified the mineralogical composition. Statistical methods and machine learning models have been applied to elucidate the relationships between the petrographic and engineering features of the aggregates and establish potential predictive capability. The analysis identified clay, calcite, feldspar, and dolomite as the primary determinants for the engineering behavior of carbonate aggregates. Although multiple regression analyses produced R² values exceeding 0.84, the multiple regression equations did not provide substantial insights into the impact of all petrographic parameters on engineering properties. To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). These results highlight the ability of machine learning techniques to provide a more effective and reliable prediction of aggregate engineering properties based on petrographic data. This approach offers significant advantages in the preliminary assessment of aggregate suitability, contributing to more efficient resource allocation in construction projects. |
| format | Article |
| id | doaj-art-8472ee933680453c88982bbc69e53460 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8472ee933680453c88982bbc69e534602025-01-05T12:27:40ZengNature PortfolioScientific Reports2045-23222024-12-0114112510.1038/s41598-024-83476-3Petrological controls on the engineering properties of carbonate aggregates through a machine learning approachJavid Hussain0Tehseen Zafar1Xiaodong Fu2Nafees Ali3Jian Chen4Fabrizio Frontalini5Jabir Hussain6Xiao Lina7George Kontakiotis8Olga Koumoutsakou9State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock, and Soil Mechanics, Chinese Academy of SciencesGeosciences Department, College of Science, United Arab Emirates UniversityState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock, and Soil Mechanics, Chinese Academy of SciencesState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock, and Soil Mechanics, Chinese Academy of SciencesState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock, and Soil Mechanics, Chinese Academy of SciencesDipartimento di Scienze Pure e Applicate (DiSPeA), Università degli Studi di Urbino “Carlo Bo”Research School of Earth Sciences, Australian National UniversityFaculty of Engineering, China University of Geosciences (Wuhan)Department of Historical Geology-Paleontology, Faculty of Geology and Geoenvironment, School of Earth Sciences, National and Kapodistrian University of AthensDepartment of Historical Geology-Paleontology, Faculty of Geology and Geoenvironment, School of Earth Sciences, National and Kapodistrian University of AthensAbstract Rock aggregates have been extensively exploited in the construction sector, and the associated engineering features play a critical role in their application. The main aim of this research is to assess the impact of petrographic characteristics on the engineering properties of carbonate rocks. A total of 45 carbonate rock samples from different geological formations within the Salt Range (Western Himalayan Ranges, Pakistan) were subjected to comprehensive petrographic analyses and standard aggregate quality control tests. The engineering characteristics encompassed Los Angeles abrasion value, aggregate crushing value, aggregate impact value, specific gravity, water absorption, and unconfined compressive strength, whereas petrographic examination of thin sections quantified the mineralogical composition. Statistical methods and machine learning models have been applied to elucidate the relationships between the petrographic and engineering features of the aggregates and establish potential predictive capability. The analysis identified clay, calcite, feldspar, and dolomite as the primary determinants for the engineering behavior of carbonate aggregates. Although multiple regression analyses produced R² values exceeding 0.84, the multiple regression equations did not provide substantial insights into the impact of all petrographic parameters on engineering properties. To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). These results highlight the ability of machine learning techniques to provide a more effective and reliable prediction of aggregate engineering properties based on petrographic data. This approach offers significant advantages in the preliminary assessment of aggregate suitability, contributing to more efficient resource allocation in construction projects.https://doi.org/10.1038/s41598-024-83476-3Construction projectsGradient BoostingStatistical analysesPredictive accuracySalt Range |
| spellingShingle | Javid Hussain Tehseen Zafar Xiaodong Fu Nafees Ali Jian Chen Fabrizio Frontalini Jabir Hussain Xiao Lina George Kontakiotis Olga Koumoutsakou Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach Scientific Reports Construction projects Gradient Boosting Statistical analyses Predictive accuracy Salt Range |
| title | Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach |
| title_full | Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach |
| title_fullStr | Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach |
| title_full_unstemmed | Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach |
| title_short | Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach |
| title_sort | petrological controls on the engineering properties of carbonate aggregates through a machine learning approach |
| topic | Construction projects Gradient Boosting Statistical analyses Predictive accuracy Salt Range |
| url | https://doi.org/10.1038/s41598-024-83476-3 |
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