Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength
Unconfined compressive strength (UCS) is one of the rocks’ most valuable mechanical properties in constructing an accurate geo-mechanical model. It has traditionally been determined through laboratory core sample testing or by analysis of well-log data. After a great deal of effort and growing inves...
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Tamkang University Press
2025-01-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202508-28-08-0015 |
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| author | She Wang Qi Zhang |
| author_facet | She Wang Qi Zhang |
| author_sort | She Wang |
| collection | DOAJ |
| description | Unconfined compressive strength (UCS) is one of the rocks’ most valuable mechanical properties in constructing an accurate geo-mechanical model. It has traditionally been determined through laboratory core sample testing or by analysis of well-log data. After a great deal of effort and growing investment in time, the proper adoption of machine learning methods, especially the radial basis function (RBF), opens a route to promising alternatives against empirical methods for better real-time prediction of UCS. The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced metaheuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). Based on an extensive dataset already used in previous studies and applying some soft computing techniques, vigorous performance metrics such as RMSE, R², MAE, U95, and MNB were used to test the
developed frameworks. The outcomes indicate a significant outperformance of the hybrid RBFF technique over the solo RBF and RBF-IA frameworks. Specifically, the RBFF model resulted in an R² of 0.998, an RMSE of 1.313, and an MNB of -0.003, reflecting its better performance in UCS prediction. This study indicates the efficiency of
integrating RBF with meta-heuristic optimization to enhance UCS predictions in geotechnical studies. |
| format | Article |
| id | doaj-art-a746c2cfe72c450dac3ebf6625f909b8 |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-a746c2cfe72c450dac3ebf6625f909b82025-01-17T08:39:20ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-01-012881779179410.6180/jase.202508_28(8).0015Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive StrengthShe Wang0Qi Zhang1School of Computer and Electronic Information Engineering, Wuhan City Polytechnic, Wuhan 430000, Hubei, ChinaDepartment of Commerce and Trade, Wuhan Instrument and Electronic Technical School, Wuhan 430205, Hubei, ChinaUnconfined compressive strength (UCS) is one of the rocks’ most valuable mechanical properties in constructing an accurate geo-mechanical model. It has traditionally been determined through laboratory core sample testing or by analysis of well-log data. After a great deal of effort and growing investment in time, the proper adoption of machine learning methods, especially the radial basis function (RBF), opens a route to promising alternatives against empirical methods for better real-time prediction of UCS. The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced metaheuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). Based on an extensive dataset already used in previous studies and applying some soft computing techniques, vigorous performance metrics such as RMSE, R², MAE, U95, and MNB were used to test the developed frameworks. The outcomes indicate a significant outperformance of the hybrid RBFF technique over the solo RBF and RBF-IA frameworks. Specifically, the RBFF model resulted in an R² of 0.998, an RMSE of 1.313, and an MNB of -0.003, reflecting its better performance in UCS prediction. This study indicates the efficiency of integrating RBF with meta-heuristic optimization to enhance UCS predictions in geotechnical studies.http://jase.tku.edu.tw/articles/jase-202508-28-08-0015unconfined compressive strengthradial basis functionimproved arithmetic optimization algorithmflying foxes optimization |
| spellingShingle | She Wang Qi Zhang Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength Journal of Applied Science and Engineering unconfined compressive strength radial basis function improved arithmetic optimization algorithm flying foxes optimization |
| title | Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength |
| title_full | Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength |
| title_fullStr | Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength |
| title_full_unstemmed | Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength |
| title_short | Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength |
| title_sort | utilization of machine learning based model hybridized with meta heuristic frameworks for estimation of unconfined compressive strength |
| topic | unconfined compressive strength radial basis function improved arithmetic optimization algorithm flying foxes optimization |
| url | http://jase.tku.edu.tw/articles/jase-202508-28-08-0015 |
| work_keys_str_mv | AT shewang utilizationofmachinelearningbasedmodelhybridizedwithmetaheuristicframeworksforestimationofunconfinedcompressivestrength AT qizhang utilizationofmachinelearningbasedmodelhybridizedwithmetaheuristicframeworksforestimationofunconfinedcompressivestrength |