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Estimation of Young's Modulus of Limestones using Multi-Layer Perceptron

Yıl 2023, , 87 - 93, 31.12.2023
https://doi.org/10.47897/bilmes.1334810

Öz

The Young’s modulus (E) is a very important parameter used in many engineering projects and in the petroleum industry. It is especially important for tunneling, mining and rock slope stability analysis. This parameter is determined by difficult experiments. In addition, cores must be taken for the experiment and the cores taken must be of high quality. The aim of the study is to estimate the Young’s modulus, which represents the basic mechanical property of rocks, using relatively easy-to-apply and low-cost methods. For this purpose, the multi-layer perception method was used. Input parameters of these meshes are Dry density, Water saturated density, Bulk density, Porosity, Water absorption, Ultrasound Pulse Velocity (UPV), Poisson ratio (v), Tensile strength (To), The uniaxial compressive strength (UCS) and The point load index (Is)' is Four different network models were created and the successes of these network models were compared using the 5-fold cross-validation method. As a result of the comparison, it was understood that the model 2 network was more successful. The Correlation coefficient values of the model were calculated as 95% in training and 84% in 5-fold cross validation.

Kaynakça

  • [1] Kurtulus, C., Cakir, S., & Yoğurtcuoğlu, A.C. “Ultrasound Study of Limestone Rock Physical and Mechanical Properties”. Soil Mechanics & Foundation Engineering, 52(6), 2016.
  • [2] Çelik SB. “Prediction of uniaxial compressive strength of carbonate rocks from nondestructive tests using multivariate regression and LS-SVM methods”. Arab J Geosci 2019;12:193. https://doi.org/10.1007/s12517-019-4307-2.
  • [3] Ferentinou M, Fakir M. “An ANN Approach for the Prediction of Uniaxial Compressive Strength, of Some Sedimentary and Igneous Rocks in Eastern KwaZulu-Natal”. Symposium of the International Society for Rock Mechanics 2017, 191:1117–25. https://doi.org/10.1016/j.proeng.2017.05.286.
  • [4] Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV. “Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach”. Bull Eng Geol Environ 2015;74:745–57.
  • [5] Z.A. Moradian, M. Behnia, “Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test”.Int. J. Geomech. 9 (1(14)) 1532–3641, 2009. [6] Khan, N. M., Cao, K., Yuan, Q., Bin Mohd Hashim, M. H., Rehman, H., Hussain, S., ... & Khan, S. “Application of machine learning and multivariate statistics to predict uniaxial compressive strength and static Young’s modulus using physical properties under different thermal conditions”. Sustainability, 14(16), 9901,2022.
  • [7] Acar, M. C., & Kaya, B. “Models to estimate the elastic modulus of weak rocks based on least square support vector machine”. Arabian Journal of Geosciences, 13(14), 590, 2020.
  • [8] Madhubabu, N., Singh, P. K., Kainthola, A., Mahanta, B., Tripathy, A., & Singh, T. N. “Prediction of compressive strength and elastic modulus of carbonate rocks”. Measurement, 88, 202-213, 2016.
  • [9] Ghasemi, E., Kalhori, H., Bagherpour, R., & Yagiz, S. “Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks”. Bulletin of Engineering Geology and the Environment, 77, 331-343., 2018.
  • [10] E. Yasar, Y. Erdogan, “Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks”. Int. J. Rock Mech. Min. Sci. 41 871, 2004.
  • [11] Armaghani, D.J., Tonnizam Mohamad, E., Momeni, E., Monjezi, M., Narayanasamy, M.S., 2016. “Prediction of the strength and elasticity modulus of granite through an expert artificial neural network”. Arabian Journal of Geosciences 9 (48), 1e16
  • [12] Hadi, F., & Nygaard, R. Estimating unconfined compressive strength and Young’s modulus of carbonate rocks from petrophysical properties. Petroleum Science and Technology, 41(13), 1367-1389,2023.
  • [13] Shahani, N. M., Zheng, X., Liu, C., Hassan, F. U., & Li, P. “Developing an XGBoost regression model for predicting young’s modulus of intact sedimentary rocks for the stability of surface and subsurface structures”. Frontiers in Earth Science, 9, 761990, 2021.
  • [14] Waqas, U., & Ahmed, M. F. “Prediction modeling for the estimation of dynamic elastic Young’s modulus of thermally treated sedimentary rocks using linear–nonlinear regression analysis, regularization, and ANFIS”. Rock Mechanics and Rock Engineering, 53, 5411-5428, 2020.
  • [15] Roy, D. G., & Singh, T. N. “Predicting deformational properties of Indian coal: Soft computing and regression analysis approach”. Measurement, 149, 106975, 2020.
  • [16] Nasiri, H., Homafar, A., & Chelgani, S. C. “Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence”. Results in Geophysical Sciences, 8, 100034, 2021.
  • [17] Mahmoud, A. A., Elkatatny, S., & Al Shehri, D. “Application of machine learning in evaluation of the static young’s modulus for sandstone formations”. Sustainability, 12(5), 1880, 2020.
  • [18] Kahraman, S. A. İ. R., Altun, H., Tezekici, B. S., & Fener, M. “Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks”. International journal of rock mechanics and mining sciences, 43(1), 157-164, 2006.
  • [19] Kumar, C. V., Vardhan, H., & Murthy, C. S. “Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations”. Modeling Earth Systems and Environment, 8(1), 141-161, 2022.
  • [20] Ebrahimi, E., Monjezi, M., Khalesi, M. R., & Armaghani, D. J. “Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm”. Bulletin of Engineering Geology and the Environment, 75, 27-36, 2016.
  • [21] Lu, S., Koopialipoor, M., Asteris, P. G., Bahri, M., & Armaghani, D. J. “A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs”. Materials, 13(17), 3902., 2020.
  • [22] Erten, M.Y., İnanç, N.. “Machine Learning Based Short Term Load Estimation in Commercial Buildings”. ISVOS. 5:171–181, 2021.

Estimation of Young's Modulus of Limestones using Multi-Layer Perceptron

Yıl 2023, , 87 - 93, 31.12.2023
https://doi.org/10.47897/bilmes.1334810

Öz

The Young’s modulus (E) is a very important parameter used in many engineering projects and in the petroleum industry. It is especially important for tunneling, mining and rock slope stability analysis. This parameter is determined by difficult experiments. In addition, cores must be taken for the experiment and the cores taken must be of high quality. The aim of the study is to estimate the Young’s modulus, which represents the basic mechanical property of rocks, using relatively easy-to-apply and low-cost methods. For this purpose, the multi-layer perception method was used. Input parameters of these meshes are Dry density, Water saturated density, Bulk density, Porosity, Water absorption, Ultrasound Pulse Velocity (UPV), Poisson ratio (v), Tensile strength (To), The uniaxial compressive strength (UCS) and The point load index (Is)' is Four different network models were created and the successes of these network models were compared using the 5-fold cross-validation method. As a result of the comparison, it was understood that the model 2 network was more successful. The Correlation coefficient values of the model were calculated as 95% in training and 84% in 5-fold cross validation.

Kaynakça

  • [1] Kurtulus, C., Cakir, S., & Yoğurtcuoğlu, A.C. “Ultrasound Study of Limestone Rock Physical and Mechanical Properties”. Soil Mechanics & Foundation Engineering, 52(6), 2016.
  • [2] Çelik SB. “Prediction of uniaxial compressive strength of carbonate rocks from nondestructive tests using multivariate regression and LS-SVM methods”. Arab J Geosci 2019;12:193. https://doi.org/10.1007/s12517-019-4307-2.
  • [3] Ferentinou M, Fakir M. “An ANN Approach for the Prediction of Uniaxial Compressive Strength, of Some Sedimentary and Igneous Rocks in Eastern KwaZulu-Natal”. Symposium of the International Society for Rock Mechanics 2017, 191:1117–25. https://doi.org/10.1016/j.proeng.2017.05.286.
  • [4] Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV. “Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach”. Bull Eng Geol Environ 2015;74:745–57.
  • [5] Z.A. Moradian, M. Behnia, “Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test”.Int. J. Geomech. 9 (1(14)) 1532–3641, 2009. [6] Khan, N. M., Cao, K., Yuan, Q., Bin Mohd Hashim, M. H., Rehman, H., Hussain, S., ... & Khan, S. “Application of machine learning and multivariate statistics to predict uniaxial compressive strength and static Young’s modulus using physical properties under different thermal conditions”. Sustainability, 14(16), 9901,2022.
  • [7] Acar, M. C., & Kaya, B. “Models to estimate the elastic modulus of weak rocks based on least square support vector machine”. Arabian Journal of Geosciences, 13(14), 590, 2020.
  • [8] Madhubabu, N., Singh, P. K., Kainthola, A., Mahanta, B., Tripathy, A., & Singh, T. N. “Prediction of compressive strength and elastic modulus of carbonate rocks”. Measurement, 88, 202-213, 2016.
  • [9] Ghasemi, E., Kalhori, H., Bagherpour, R., & Yagiz, S. “Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks”. Bulletin of Engineering Geology and the Environment, 77, 331-343., 2018.
  • [10] E. Yasar, Y. Erdogan, “Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks”. Int. J. Rock Mech. Min. Sci. 41 871, 2004.
  • [11] Armaghani, D.J., Tonnizam Mohamad, E., Momeni, E., Monjezi, M., Narayanasamy, M.S., 2016. “Prediction of the strength and elasticity modulus of granite through an expert artificial neural network”. Arabian Journal of Geosciences 9 (48), 1e16
  • [12] Hadi, F., & Nygaard, R. Estimating unconfined compressive strength and Young’s modulus of carbonate rocks from petrophysical properties. Petroleum Science and Technology, 41(13), 1367-1389,2023.
  • [13] Shahani, N. M., Zheng, X., Liu, C., Hassan, F. U., & Li, P. “Developing an XGBoost regression model for predicting young’s modulus of intact sedimentary rocks for the stability of surface and subsurface structures”. Frontiers in Earth Science, 9, 761990, 2021.
  • [14] Waqas, U., & Ahmed, M. F. “Prediction modeling for the estimation of dynamic elastic Young’s modulus of thermally treated sedimentary rocks using linear–nonlinear regression analysis, regularization, and ANFIS”. Rock Mechanics and Rock Engineering, 53, 5411-5428, 2020.
  • [15] Roy, D. G., & Singh, T. N. “Predicting deformational properties of Indian coal: Soft computing and regression analysis approach”. Measurement, 149, 106975, 2020.
  • [16] Nasiri, H., Homafar, A., & Chelgani, S. C. “Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence”. Results in Geophysical Sciences, 8, 100034, 2021.
  • [17] Mahmoud, A. A., Elkatatny, S., & Al Shehri, D. “Application of machine learning in evaluation of the static young’s modulus for sandstone formations”. Sustainability, 12(5), 1880, 2020.
  • [18] Kahraman, S. A. İ. R., Altun, H., Tezekici, B. S., & Fener, M. “Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks”. International journal of rock mechanics and mining sciences, 43(1), 157-164, 2006.
  • [19] Kumar, C. V., Vardhan, H., & Murthy, C. S. “Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations”. Modeling Earth Systems and Environment, 8(1), 141-161, 2022.
  • [20] Ebrahimi, E., Monjezi, M., Khalesi, M. R., & Armaghani, D. J. “Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm”. Bulletin of Engineering Geology and the Environment, 75, 27-36, 2016.
  • [21] Lu, S., Koopialipoor, M., Asteris, P. G., Bahri, M., & Armaghani, D. J. “A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs”. Materials, 13(17), 3902., 2020.
  • [22] Erten, M.Y., İnanç, N.. “Machine Learning Based Short Term Load Estimation in Commercial Buildings”. ISVOS. 5:171–181, 2021.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme
Bölüm Makaleler
Yazarlar

Ebru Efeoğlu 0000-0001-5444-6647

Yayımlanma Tarihi 31 Aralık 2023
Kabul Tarihi 10 Aralık 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Efeoğlu, E. (2023). Estimation of Young’s Modulus of Limestones using Multi-Layer Perceptron. International Scientific and Vocational Studies Journal, 7(2), 87-93. https://doi.org/10.47897/bilmes.1334810
AMA Efeoğlu E. Estimation of Young’s Modulus of Limestones using Multi-Layer Perceptron. ISVOS. Aralık 2023;7(2):87-93. doi:10.47897/bilmes.1334810
Chicago Efeoğlu, Ebru. “Estimation of Young’s Modulus of Limestones Using Multi-Layer Perceptron”. International Scientific and Vocational Studies Journal 7, sy. 2 (Aralık 2023): 87-93. https://doi.org/10.47897/bilmes.1334810.
EndNote Efeoğlu E (01 Aralık 2023) Estimation of Young’s Modulus of Limestones using Multi-Layer Perceptron. International Scientific and Vocational Studies Journal 7 2 87–93.
IEEE E. Efeoğlu, “Estimation of Young’s Modulus of Limestones using Multi-Layer Perceptron”, ISVOS, c. 7, sy. 2, ss. 87–93, 2023, doi: 10.47897/bilmes.1334810.
ISNAD Efeoğlu, Ebru. “Estimation of Young’s Modulus of Limestones Using Multi-Layer Perceptron”. International Scientific and Vocational Studies Journal 7/2 (Aralık 2023), 87-93. https://doi.org/10.47897/bilmes.1334810.
JAMA Efeoğlu E. Estimation of Young’s Modulus of Limestones using Multi-Layer Perceptron. ISVOS. 2023;7:87–93.
MLA Efeoğlu, Ebru. “Estimation of Young’s Modulus of Limestones Using Multi-Layer Perceptron”. International Scientific and Vocational Studies Journal, c. 7, sy. 2, 2023, ss. 87-93, doi:10.47897/bilmes.1334810.
Vancouver Efeoğlu E. Estimation of Young’s Modulus of Limestones using Multi-Layer Perceptron. ISVOS. 2023;7(2):87-93.


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