Research Article
Year 2023,
Volume: 9 Issue: 2, 469 - 477, 31.12.2023
Abstract
Kaya sınıflandırması tünel ve yeraltı çalışmalarının yanı sıra madencilik ve hidrokarbon araştırmalarında da önem teşkil eder. Kayaların türünün yanlış tespiti hem para hem de zaman kaybına yol açar. Bu çalışmada 4 farklı kaya sınıfının fiziksel ve mekanik özellikleri kullanılarak kaya sınıflandırması yapılmıştır. Sınıflandırma için Bulanık Sırasız Kural İndüksiyon Algoritması (FURIA), Bulanık Kafes Akıl Yürütme (FLR), Çok Amaçlı Evrimsel Bulanık (MOE Fuzzy) sınıflandırıcıları kullanıldı. Çok Amaçlı Evrimsel Bulanık MOE Bulanık sınıflandırıcısı, ENORA ve NSGA II algoritmalarına dayanmaktadır. Bu nedenle bu algoritmalar ayrı ayrı kullanıldı. Verilere sınıflandırma öncesi Sentetik Azınlık Aşırı Örnekleme Tekniği uygulandı ve bu işlemden önceki ve işlem sonrasında sınıflandırma performansları karşılaştırıldı. Sonuç olarak Sentetik Azınlık Aşırı Örnekleme Tekniği ile sınıflandırma başarısının arttığı görülmüştür. Sınıflandırmada en başarılı algoritma FURIA algoritması oldu. Algoritma sınıflandırmayı %93 doğrulukla ve 0.16 hata değeri ile gerçekleştirdi.
Keywords
Supporting Institution
yok
Project Number
yok
References
- Kurtuluş, C., Sertçelik, F., Sertçelik, I. Correlating physico-mechanical properties of intact rocks with P-wave velocity. Acta Geodaetica et Geophysica, 51, 571-582, 2016.
- Ran, X., Xue, L., Zhang, Y., Liu, Z., Sang, X., He, J. Rock classification from field image patches analyzed using a deep convolutional neural network. Mathematics, 7(8), 755, 2019.
- Su, C., Xu, S. J., Zhu, K. Y., & Zhang, X. C. Rock classification in petrographic thin section images based on concatenated convolutional neural networks. Earth Science Informatics, 13, 1477-1484, 2020.
- dos Anjos, C. E., Avila, M. R., Vasconcelos, A. G., Pereira Neta, A. M., Medeiros, L. C., Evsukoff, A. G., . .. & Landau, L. Deep learning for lithological classification of carbonate rock micro-CT, 2021.
- Imamverdiyev, Y., & Sukhostat, L. Lithological facies classification using deep convolutional neural network. Journal of Petroleum Science and Engineering, 174, 216-228, 2019.
- Chen, J., Yang, T., Zhang, D., Huang, H., & Tian, Y. Deep learning based classification of rock structure of tunnel face. Geoscience Frontiers, 12(1), 395-404, 2021.
- Santos, A. E. M., Lana, M. S., & Pereira, T. M. Evaluation of machine learning methods for rock mass classification. Neural Computing and Applications, 34(6), 4633-4642, 2022.
- Liu, Q., Wang, X., Huang, X., & Yin, X. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunnelling and Underground Space Technology, 106, 103595, 2020.
- Hasegawa, N., Hasegawa, S., Kitaoka, T., & Ohtsu, H. Applicability of neural network in rock classification of mountain tunnel. Materials Transactions, 60(5), 758-764, 2019.
- Bertuzzi, R. Revisiting rock classification to estimate rock mass properties. Journal of Rock Mechanics and Geotechnical Engineering, 11(3), 494-510, 2019.
- Dai, B., Li, D., Zhang, L., Liu, Y., Zhang, Z., & Chen, S. Rock Mass Classification Method Based on Entropy Weight–TOPSIS–Grey Correlation Analysis. Sustainability, 14(17), 10500, 2022.
- Sadeghi, S., Sharifi Teshnizi, E., & Ghoreishi, B. Correlations between various rock mass classification/characterization systems for the Zagros tunnel-W Iran. Journal of Mountain Science, 17(7), 1790-1806, 2020.
- Lu, J., Guo, W., Liu, J., Zhao, R., Ding, Y., Shi, S. An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine. Applied Sciences, 13(4), 2068, 2023.
- Huang, X., Yin, X., Liu, B., Ding, Z., Zhang, C., Jing, B., & Guo, X. A gray wolf optimization-based improved probabilistic neural network algorithm for surrounding rock squeezing classification in tunnel engineering. Frontiers in Earth Science, 10, 857463, 2022.
- Amiripallia, S. S., Rao, G. N., Beharaa, J., Sanjay, K. Mineral Rock Classification Using Convolutional Neural Network. Recent Trends in Intensive Computing; IOS Press: Amsterdam, The Netherlands, 2021.
- Afraei, S., Shahriar, K., & Madani, S. H. Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: Literature review and data preprocessing procedure. Tunnelling and Underground Space Technology, 83, 324-353, 2019.
- Marinos, V. A revised, geotechnical classification GSI system for tectonically disturbed heterogeneous rock masses, such as flysch. Bulletin of Engineering Geology and the Environment, 78, 899-912, 2019.
- Alzubaidi, F., Mostaghimi, P., Si, G., Swietojanski, P., & Armstrong, R. T. Automated rock quality designation using convolutional neural networks. Rock mechanics and rock engineering, 55(6), 3719-3734, 2022.
- Alzubaidi, F., Mostaghimi, P., Swietojanski, P., Clark, S. R., & Armstrong, R. T. Automated lithology classification from drill core images using convolutional neural networks. Journal of Petroleum Science and Engineering, 197, 107933, 2021.
- de Freitas, K. L. F., da Silva, P. N., Faria, B. M., Gonçalves, E. C., Rios, E. H., Nobre-Lopes, J., ... & de Vasconcelos Azeredo, R. B. A data mining approach for automatic classification of rock permeability. Journal of Applied Geophysics, 196, 104514, 2022.
- Alférez, G. H., Vázquez, E. L., Ardila, A. M. M., & Clausen, B. L. Automatic classification of plutonic rocks with deep learning. Applied Computing and Geosciences, 10, 100061, 2021.
- Deng, G., & Fu, Y. Fuzzy rule based classification method of surrounding rock stability of coal roadway using artificial intelligence algorithm. Journal of Intelligent & Fuzzy Systems, 40(4), 8163-8171, 2021.
- Taheri, F., Jafari, H., Rezaei, M., & Bagheri, R. The use of continuous fuzzy and traditional classification models for groundwater potentiality mapping in areas underlain by granitic hard-rock aquifers. Environmental Earth Sciences, 79, 1-16, 2020.
- Stehlíková, B., Bogdanovská, G., Flegner, P., Frančáková, R., & Drančák, L. Rock Classification Using a Vibration Signal in the Process of Rotary Drilling, 2023.
- Chawla, N. vd. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357, 2002.
- Hühn, J., Hüllermeier, E. FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery, Volume 19, Issue 3, p. 293–319, 2009.
- V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas . Fuzzy Lattice Reasoning (FLR) Classifier and its Application for Ambient Ozone Estimation. International Journal of Approximate Reasoning, Volume 45, Issue 1, May 2007,152-188, 2003.
- Jimenez, F., Sanchez, G. & Juarez, J.M. Multi-objective Evolutionary Algorithms for Fuzzy Classification in Survival Prediction. Artificial Intelligence in Medicine, 60(3), 197-219, 2014.
Year 2023,
Volume: 9 Issue: 2, 469 - 477, 31.12.2023
Abstract
Project Number
yok
References
- Kurtuluş, C., Sertçelik, F., Sertçelik, I. Correlating physico-mechanical properties of intact rocks with P-wave velocity. Acta Geodaetica et Geophysica, 51, 571-582, 2016.
- Ran, X., Xue, L., Zhang, Y., Liu, Z., Sang, X., He, J. Rock classification from field image patches analyzed using a deep convolutional neural network. Mathematics, 7(8), 755, 2019.
- Su, C., Xu, S. J., Zhu, K. Y., & Zhang, X. C. Rock classification in petrographic thin section images based on concatenated convolutional neural networks. Earth Science Informatics, 13, 1477-1484, 2020.
- dos Anjos, C. E., Avila, M. R., Vasconcelos, A. G., Pereira Neta, A. M., Medeiros, L. C., Evsukoff, A. G., . .. & Landau, L. Deep learning for lithological classification of carbonate rock micro-CT, 2021.
- Imamverdiyev, Y., & Sukhostat, L. Lithological facies classification using deep convolutional neural network. Journal of Petroleum Science and Engineering, 174, 216-228, 2019.
- Chen, J., Yang, T., Zhang, D., Huang, H., & Tian, Y. Deep learning based classification of rock structure of tunnel face. Geoscience Frontiers, 12(1), 395-404, 2021.
- Santos, A. E. M., Lana, M. S., & Pereira, T. M. Evaluation of machine learning methods for rock mass classification. Neural Computing and Applications, 34(6), 4633-4642, 2022.
- Liu, Q., Wang, X., Huang, X., & Yin, X. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunnelling and Underground Space Technology, 106, 103595, 2020.
- Hasegawa, N., Hasegawa, S., Kitaoka, T., & Ohtsu, H. Applicability of neural network in rock classification of mountain tunnel. Materials Transactions, 60(5), 758-764, 2019.
- Bertuzzi, R. Revisiting rock classification to estimate rock mass properties. Journal of Rock Mechanics and Geotechnical Engineering, 11(3), 494-510, 2019.
- Dai, B., Li, D., Zhang, L., Liu, Y., Zhang, Z., & Chen, S. Rock Mass Classification Method Based on Entropy Weight–TOPSIS–Grey Correlation Analysis. Sustainability, 14(17), 10500, 2022.
- Sadeghi, S., Sharifi Teshnizi, E., & Ghoreishi, B. Correlations between various rock mass classification/characterization systems for the Zagros tunnel-W Iran. Journal of Mountain Science, 17(7), 1790-1806, 2020.
- Lu, J., Guo, W., Liu, J., Zhao, R., Ding, Y., Shi, S. An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine. Applied Sciences, 13(4), 2068, 2023.
- Huang, X., Yin, X., Liu, B., Ding, Z., Zhang, C., Jing, B., & Guo, X. A gray wolf optimization-based improved probabilistic neural network algorithm for surrounding rock squeezing classification in tunnel engineering. Frontiers in Earth Science, 10, 857463, 2022.
- Amiripallia, S. S., Rao, G. N., Beharaa, J., Sanjay, K. Mineral Rock Classification Using Convolutional Neural Network. Recent Trends in Intensive Computing; IOS Press: Amsterdam, The Netherlands, 2021.
- Afraei, S., Shahriar, K., & Madani, S. H. Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: Literature review and data preprocessing procedure. Tunnelling and Underground Space Technology, 83, 324-353, 2019.
- Marinos, V. A revised, geotechnical classification GSI system for tectonically disturbed heterogeneous rock masses, such as flysch. Bulletin of Engineering Geology and the Environment, 78, 899-912, 2019.
- Alzubaidi, F., Mostaghimi, P., Si, G., Swietojanski, P., & Armstrong, R. T. Automated rock quality designation using convolutional neural networks. Rock mechanics and rock engineering, 55(6), 3719-3734, 2022.
- Alzubaidi, F., Mostaghimi, P., Swietojanski, P., Clark, S. R., & Armstrong, R. T. Automated lithology classification from drill core images using convolutional neural networks. Journal of Petroleum Science and Engineering, 197, 107933, 2021.
- de Freitas, K. L. F., da Silva, P. N., Faria, B. M., Gonçalves, E. C., Rios, E. H., Nobre-Lopes, J., ... & de Vasconcelos Azeredo, R. B. A data mining approach for automatic classification of rock permeability. Journal of Applied Geophysics, 196, 104514, 2022.
- Alférez, G. H., Vázquez, E. L., Ardila, A. M. M., & Clausen, B. L. Automatic classification of plutonic rocks with deep learning. Applied Computing and Geosciences, 10, 100061, 2021.
- Deng, G., & Fu, Y. Fuzzy rule based classification method of surrounding rock stability of coal roadway using artificial intelligence algorithm. Journal of Intelligent & Fuzzy Systems, 40(4), 8163-8171, 2021.
- Taheri, F., Jafari, H., Rezaei, M., & Bagheri, R. The use of continuous fuzzy and traditional classification models for groundwater potentiality mapping in areas underlain by granitic hard-rock aquifers. Environmental Earth Sciences, 79, 1-16, 2020.
- Stehlíková, B., Bogdanovská, G., Flegner, P., Frančáková, R., & Drančák, L. Rock Classification Using a Vibration Signal in the Process of Rotary Drilling, 2023.
- Chawla, N. vd. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357, 2002.
- Hühn, J., Hüllermeier, E. FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery, Volume 19, Issue 3, p. 293–319, 2009.
- V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas . Fuzzy Lattice Reasoning (FLR) Classifier and its Application for Ambient Ozone Estimation. International Journal of Approximate Reasoning, Volume 45, Issue 1, May 2007,152-188, 2003.
- Jimenez, F., Sanchez, G. & Juarez, J.M. Multi-objective Evolutionary Algorithms for Fuzzy Classification in Survival Prediction. Artificial Intelligence in Medicine, 60(3), 197-219, 2014.
There are 28 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Civil Geotechnical Engineering, Civil Engineering (Other) |
| Journal Section | Issue |
| Authors | |
| Project Number | yok |
| Publication Date | December 31, 2023 |
| Published in Issue | Year 2023 Volume: 9 Issue: 2 |
