HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks
Abstract The uniaxial compressive strength (UCS) of the rock is one of the most important design parameters in various engineering applications. Therefore, the UCS requires to be either preciously measured through extensive field and laboratory studies or could be estimated by employing machine lear...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41349-1 |
| _version_ | 1797576799026151424 |
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| author | Mohamed Yusuf Hassan Hasan Arman |
| author_facet | Mohamed Yusuf Hassan Hasan Arman |
| author_sort | Mohamed Yusuf Hassan |
| collection | DOAJ |
| description | Abstract The uniaxial compressive strength (UCS) of the rock is one of the most important design parameters in various engineering applications. Therefore, the UCS requires to be either preciously measured through extensive field and laboratory studies or could be estimated by employing machine learning techniques and several other measured physical and mechanical explanatory rock parameters. This study is proposed to estimate the UCS of the evaporitic rocks by using a simple, measured point load index (PLI) and Schmidt Hammer (SHVRB) test rock blocks of evaporitic rocks. Finite mixture regression model (FMR), hybrid fuzzy inference systems model (HYFIS), multiple regression model (MLR), and locally weighted regression (LWR) are employed to predict the UCS. Different algorithms are implemented, including expectation–maximization (EM) algorithm, Mamdani fuzzy rule structures, Gradient descent-based learning algorithm with multilayer perceptron (MLP), and the least squares. Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and A20-index accuracy measures are used to compare the performances of the competing models. Based on all the above measures, LWR outperformed with the other models whereas the HYFIS model has a slight advantage over the other two models. |
| first_indexed | 2024-03-10T21:58:53Z |
| format | Article |
| id | doaj.art-dd71d50d54b6437b92fdac1611d4b637 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:58:53Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dd71d50d54b6437b92fdac1611d4b6372023-11-19T13:02:00ZengNature PortfolioScientific Reports2045-23222023-08-0113111510.1038/s41598-023-41349-1HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocksMohamed Yusuf Hassan0Hasan Arman1Department of Statistics, College of Business, United Arab Emirates UniversityDepartment of Geosciences, College of Science, United Arab Emirates UniversityAbstract The uniaxial compressive strength (UCS) of the rock is one of the most important design parameters in various engineering applications. Therefore, the UCS requires to be either preciously measured through extensive field and laboratory studies or could be estimated by employing machine learning techniques and several other measured physical and mechanical explanatory rock parameters. This study is proposed to estimate the UCS of the evaporitic rocks by using a simple, measured point load index (PLI) and Schmidt Hammer (SHVRB) test rock blocks of evaporitic rocks. Finite mixture regression model (FMR), hybrid fuzzy inference systems model (HYFIS), multiple regression model (MLR), and locally weighted regression (LWR) are employed to predict the UCS. Different algorithms are implemented, including expectation–maximization (EM) algorithm, Mamdani fuzzy rule structures, Gradient descent-based learning algorithm with multilayer perceptron (MLP), and the least squares. Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and A20-index accuracy measures are used to compare the performances of the competing models. Based on all the above measures, LWR outperformed with the other models whereas the HYFIS model has a slight advantage over the other two models.https://doi.org/10.1038/s41598-023-41349-1 |
| spellingShingle | Mohamed Yusuf Hassan Hasan Arman HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks Scientific Reports |
| title | HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks |
| title_full | HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks |
| title_fullStr | HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks |
| title_full_unstemmed | HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks |
| title_short | HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks |
| title_sort | hyfis vs fmr lwr and least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks |
| url | https://doi.org/10.1038/s41598-023-41349-1 |
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