Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/18/9187 |
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| author | Masoud Samaei Timur Massalow Ali Abdolhosseinzadeh Saffet Yagiz Mohanad Muayad Sabri Sabri |
| author_facet | Masoud Samaei Timur Massalow Ali Abdolhosseinzadeh Saffet Yagiz Mohanad Muayad Sabri Sabri |
| author_sort | Masoud Samaei |
| collection | DOAJ |
| description | Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority. |
| first_indexed | 2024-03-10T00:49:24Z |
| format | Article |
| id | doaj.art-d8e7b56460a54fd5aa30d5cd973d3896 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:49:24Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d8e7b56460a54fd5aa30d5cd973d38962023-11-23T14:54:26ZengMDPI AGApplied Sciences2076-34172022-09-011218918710.3390/app12189187Application of Soft Computing Techniques for Predicting Thermal Conductivity of RocksMasoud Samaei0Timur Massalow1Ali Abdolhosseinzadeh2Saffet Yagiz3Mohanad Muayad Sabri Sabri4Department of Civil Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz 5166616471, IranDepartment of Mining Engineering, School of Mining and Geosciences, Nazarbayev University, Nur-Sultan 01000, KazakhstanDepartment of Civil Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz 5166616471, IranDepartment of Mining Engineering, School of Mining and Geosciences, Nazarbayev University, Nur-Sultan 01000, KazakhstanPeter the Great St. Petersburg Polytechnic University, 195251 Petersburg, RussiaDue to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority.https://www.mdpi.com/2076-3417/12/18/9187thermal conductivitygeothermal systemsgene expression programming (GEP)non-linear multivariable regression (NLMR)P-waveporosity |
| spellingShingle | Masoud Samaei Timur Massalow Ali Abdolhosseinzadeh Saffet Yagiz Mohanad Muayad Sabri Sabri Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks Applied Sciences thermal conductivity geothermal systems gene expression programming (GEP) non-linear multivariable regression (NLMR) P-wave porosity |
| title | Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks |
| title_full | Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks |
| title_fullStr | Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks |
| title_full_unstemmed | Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks |
| title_short | Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks |
| title_sort | application of soft computing techniques for predicting thermal conductivity of rocks |
| topic | thermal conductivity geothermal systems gene expression programming (GEP) non-linear multivariable regression (NLMR) P-wave porosity |
| url | https://www.mdpi.com/2076-3417/12/18/9187 |
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