A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks
The precise estimation of Unconfined Compressive Strength (UCS) in rock samples is paramount for the effective planning and execution of mining and civil engineering projects. However, the inherent variability and discontinuity within rock masses present challenges in obtaining accurate physico-mech...
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2024-06-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| author | Annabelle Graham Emma Scott |
| author_facet | Annabelle Graham Emma Scott |
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| description | The precise estimation of Unconfined Compressive Strength (UCS) in rock samples is paramount for the effective planning and execution of mining and civil engineering projects. However, the inherent variability and discontinuity within rock masses present challenges in obtaining accurate physico-mechanical data. In this study, the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) investigate as a cost-efficient and reliable approach to predict UCS from Bulk Density (BD), Bulk Tensile Strength (BTS), dry density (DD) test, p-wave velocity test (Vp), Schmidt hammer rebound number (SRn), and point load index test (Is(50)). ANFIS excels in effortlessly amalgamating expert knowledge using linguistic rules and empirical data, thus enhancing model interpretability and transparency, qualities profoundly esteemed in the geosciences. Furthermore, it allows for parameter fine-tuning optimizing model performance, as evidenced by its enhanced performance with the Mountain Gazelle Optimizer (MGO) and Adaptive Opposition Slime Mould Algorithm (AOSMA) in this study. Models were trained with 70% of the dataset, and the performance of the models was tested with 30% of the dataset. Performance indices such as R2, RMSE, NMSE, MAE, and n_10 index were used to assess the predictive capability of models indicating that ANAS with maximum ÿ R2all=0.987 and minimum RMSEall=3.103, has the most optimal prediction performance for practical applications. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-06-01 |
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| spelling | doaj-art-128880c52f634e4db5b54aa82ce79edb2025-02-12T08:47:56ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-06-010030211710.22034/aeis.2024.453697.1186199131A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of RocksAnnabelle Graham0Emma Scott1Faculty of Science, Engineering, and Technology, Swinburne University of Technology, Melbourne, Victoria, 3122, AustraliaRMIT University, Melbourne, Victoria, 3000, AustraliaThe precise estimation of Unconfined Compressive Strength (UCS) in rock samples is paramount for the effective planning and execution of mining and civil engineering projects. However, the inherent variability and discontinuity within rock masses present challenges in obtaining accurate physico-mechanical data. In this study, the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) investigate as a cost-efficient and reliable approach to predict UCS from Bulk Density (BD), Bulk Tensile Strength (BTS), dry density (DD) test, p-wave velocity test (Vp), Schmidt hammer rebound number (SRn), and point load index test (Is(50)). ANFIS excels in effortlessly amalgamating expert knowledge using linguistic rules and empirical data, thus enhancing model interpretability and transparency, qualities profoundly esteemed in the geosciences. Furthermore, it allows for parameter fine-tuning optimizing model performance, as evidenced by its enhanced performance with the Mountain Gazelle Optimizer (MGO) and Adaptive Opposition Slime Mould Algorithm (AOSMA) in this study. Models were trained with 70% of the dataset, and the performance of the models was tested with 30% of the dataset. Performance indices such as R2, RMSE, NMSE, MAE, and n_10 index were used to assess the predictive capability of models indicating that ANAS with maximum ÿ R2all=0.987 and minimum RMSEall=3.103, has the most optimal prediction performance for practical applications.https://aeis.bilijipub.com/article_199131_fc91785a099bb168de774ecd0cab55d9.pdfunconfined compressive strength of rock sampleadaptive neuro-fuzzy inference systemsmountain gazelle optimizeradaptive opposition slime mould algorithm |
| spellingShingle | Annabelle Graham Emma Scott A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks Advances in Engineering and Intelligence Systems unconfined compressive strength of rock sample adaptive neuro-fuzzy inference systems mountain gazelle optimizer adaptive opposition slime mould algorithm |
| title | A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks |
| title_full | A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks |
| title_fullStr | A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks |
| title_full_unstemmed | A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks |
| title_short | A Comparative Study of Hybrid Adaptive Neuro-Fuzzy Inference Systems to Predict the Unconfined Compressive Strength of Rocks |
| title_sort | comparative study of hybrid adaptive neuro fuzzy inference systems to predict the unconfined compressive strength of rocks |
| topic | unconfined compressive strength of rock sample adaptive neuro-fuzzy inference systems mountain gazelle optimizer adaptive opposition slime mould algorithm |
| url | https://aeis.bilijipub.com/article_199131_fc91785a099bb168de774ecd0cab55d9.pdf |
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