Estimating Penetration Rate of Excavation Machine Using Geotechnical Parameters and Neural Networks in Tabriz Metro | |
| روش های تحلیلی و عددی در مهندسی معدن | |
| مقاله 1، دوره 13، شماره 37، دی 1402، صفحه 1-9 اصل مقاله (1.15 M) | |
| نوع مقاله: مقاله پژوهشی | |
| شناسه دیجیتال (DOI): 10.22034/anm.2023.20414.1604 | |
| نویسندگان | |
| Samaneh Khodaee Ashestani؛ Hamid Chakeri؛ Mohammad Darbor* ؛ Erfan Khoshzaher؛ Seyyed Shahab Eddin Bazargan | |
| Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran | |
| چکیده | |
| In this study, the penetration rate of the excavation machine in Tabriz Metro Line 2 using geotechnical parameters and neural networks is estimated. For this purpose, through comprehensive analysis, including borehole drilling, field and laboratory tests, and consideration of similar projects, the geotechnical parameters for soil and rock layers have been determined. Preprocessing data techniques, such as normalization, have been applied to address challenges such as noise and bias in raw data. Also, neural networks with varying architectures were evaluated using mean square error and correlation coefficient as evaluation metrics. The architecture (1-12-8) of this research demonstrates superior performance with a mean square error of 1.630 and a correlation coefficient of 0.932. This shows a strong relationship between predicted and actual penetration rate values. The findings of this research highlight the effectiveness of neural networks in estimating the penetration rate. Accurate estimations of the non-linear penetration rate were achieved by employing a single-layer neural network with multiple neurons using appropriate transfer functions. Overall, this research contributes to the understanding of geotechnical considerations for urban train routes and demonstrates the accuracy of neural networks for penetration rate estimation. These insights have implications for the design and engineering of similar projects. | |
| کلیدواژهها | |
| Tunnelling؛ Mechanized excavation؛ Neural networks؛ Geotechnical parameters؛ Penetration rate | |
| عنوان مقاله [English] | |
| Estimating Penetration Rate of Excavation Machine Using Geotechnical Parameters and Neural Networks in Tabriz Metro | |
| نویسندگان [English] | |
| Samaneh Khodaee Ashestani؛ Hamid Chakeri؛ Mohammad Darbor؛ Erfan Khoshzaher؛ Seyyed Shahab Eddin Bazargan | |
| Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran | |
| چکیده [English] | |
| In this study, the penetration rate of the excavation machine in Tabriz Metro Line 2 using geotechnical parameters and neural networks is estimated. For this purpose, through comprehensive analysis, including borehole drilling, field and laboratory tests, and consideration of similar projects, the geotechnical parameters for soil and rock layers have been determined. Preprocessing data techniques, such as normalization, have been applied to address challenges such as noise and bias in raw data. Also, neural networks with varying architectures were evaluated using mean square error and correlation coefficient as evaluation metrics. The architecture (1-12-8) of this research demonstrates superior performance with a mean square error of 1.630 and a correlation coefficient of 0.932. This shows a strong relationship between predicted and actual penetration rate values. The findings of this research highlight the effectiveness of neural networks in estimating the penetration rate. Accurate estimations of the non-linear penetration rate were achieved by employing a single-layer neural network with multiple neurons using appropriate transfer functions. Overall, this research contributes to the understanding of geotechnical considerations for urban train routes and demonstrates the accuracy of neural networks for penetration rate estimation. These insights have implications for the design and engineering of similar projects. | |
| کلیدواژهها [English] | |
| Tunnelling, Mechanized excavation, Neural networks, Geotechnical parameters, Penetration rate | |
| مراجع | |
|
[1] Benato, A. and Oreste, P. (2015): Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics. International Journal of Rock Mechanics and Mining Sciences. 74, 119-127.
[2] Sindhwani, A., Murthy, V. M. S. R., Raphique, M. and Raina, A. K. (2023): Decoding rate of penetration of tunnel boring machine in Deccan Traps under varied geological and machine variables using response surface analysis. Bulletin of Engineering Geology and the Environment. 82(3), 73.
[3] Farmer, I., W. and Glossop, N. H. (1980): Mechanics of disc cutter penetration. Tunnels Tunnelling. 12(6).
[4] Sanio, H. P. (1985): Prediction of the performance of disc cutters in anisotropic rock. In International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts. 22, 3, 153-161.
[5] Yagiz, S. (2002): Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM Model basic penetration for hard rock tunneling machines.
[6] Ghasemi, E., Yagiz, S. and Ataei, M. (2014): Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bulletin of Engineering Geology and the Environment. 73, 23-35.
[7] Yagiz, S. and Karahan, H. (2011): Prediction of hard rock TBM penetration rate using particle swarm optimization. International Journal of Rock Mechanics and Mining Sciences. 48, 3, 427-433.
[8] Benardos, A. G. and Kaliampakos, D. C. (2004): Modelling TBM performance with artificial neural networks. Tunnelling and Underground Space Technology. 19, 6, 597-605.
[9] Koopialipoor, M., Fahimifar, A., Ghaleini, E. N., Momenzadeh, M. and Armaghani, D. J. (2020): Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Engineering with Computers. 36, 345-357.
[10] Huang, Z., Argyroudis, S. A., Pitilakis, K., Zhang, D. and Tsinidis, G. (2022): Fragility assessment of tunnels in soft soils using artificial neural networks. Underground Space. 7, 2, 242-253.
[11] Ding, Z., Zhao, L. S., Zhou, W. H. and Bezuijen, A. (2022): Intelligent prediction of multi-factor-oriented ground settlement during TBM tunneling in soft soil. Frontiers in Built Environment. 8.
[12] Umair, M., Kim, D. and Choi, M. (2020): Impact of climate, rising atmospheric carbon dioxide, and other environmental factors on water-use efficiency at multiple land cover types. Scientific Reports. 10 ,1, 1-13.
[13] Yu, H., Qin, C., Tao, J., Liu, C. and Liu, Q. (2023): A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction. Tunnelling and Underground Space Technology. 133, 104949.
[14] Yu, H., Tao, J., Huang, S., Qin, C., Xiao, D. and Liu, C. (2021): A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead. Automation in Construction. 124, 103603.
[15] Xia, P., Huang, Y., Li, P., Liu, C. and Shi, L. (2021): Fault knowledge transfer assisted ensemble method for remaining useful life prediction. IEEE Transactions on Industrial Informatics. 18, 3, 1758-1769.
[16] Xiao, D., Qin, C., Yu, H., Huang, Y., Liu, C. and Zhang, J. (2021): Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals. Measurement. 176, 109186.
[17] Qin, C., Xiao, D., Tao, J., Yu, H., Jin, Y., Sun, Y. and Liu, C. (2022): Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement. 194, 111090.
[18] Armaghani, D. J., Koopialipoor, M., Marto, A. and Yagiz, S. (2019): Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, 11,4, 779-789.
[19] Ahmadi, M. A., Ebadi, M., Shokrollahi, A. and Majidi, S. M. J. (2013): Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Applied Soft Computing. 13, 2, 1085-1098.
[20] Armaghani, D. J., Mohamad, E. T., Narayanasamy, M. S., Narita, N. and Yagiz, S. (2017): Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology. 63, 29-43.
[21] Armaghani, D. J., Yagiz, S., Mohamad, E. T. and Zhou, J. (2021): Prediction of TBM performance in fresh through weathered granite using empirical and statistical approaches. Tunnelling and Underground Space Technology. 118, 104183.
[22] Farrokh, E., Rostami, J. and Laughton, C. (2012): Study of various models for estimation of penetration rate of hard rock TBMs. Tunnelling and Underground Space Technology. 30, 110-123.
[23] Wang, X., Wu, J., Yin, X., Liu, Q., Huang, X., Pan, Y. and Miao, S. (2023): QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency. Frontiers of Structural and Civil Engineering. 1-12.
[24] Hassan, S. A., Shitote, S. M. and Kiplangat, D. C. (2022): Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement. Civil Engineering Journal. 8, 11, 2424-2444.
[25] Mahdevari, S. and Torabi, S. R. (2012): Prediction of tunnel convergence using artificial neural networks. Tunnelling and Underground Space Technology. 28, 218-228.
[26] Mahdevari, S., Torabi, S. R. and Monjezi, M. (2012): Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon. International Journal of Rock Mechanics and Mining Sciences. 55, 33-44.
[27] Zhou, J., Qiu, Y., Armaghani, D. J., Zhang, W., Li, C., Zhu, S. and Tarinejad, R. (2021): Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques. Geoscience Frontiers. 12, 3, 101091.
[28] Koopialipoor, M., Nikouei, S. S., Marto, A., Fahimifar, A., Jahed Armaghani, D. and Mohamad, E. T. (2019): Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bulletin of Engineering Geology and the Environment. 78, 3799-3813.
[29] Hecht-Nielsen, R. (1987): Kolmogorov’s mapping neural network existence theorem. In Proceedings of the international conference on Neural Networks. 3, 11-14.
[30] Ripley, B. D. (1993): Statistical aspects of neural networks. Natworks and Chaos-Statistical and Probabilistic Aspects. 40-123.
[31] Paola J. (1994): Neural network classification of multispectral imagery. Master Tezi.
[32] Wang, C. (1994): A theory of generalization in learning machines with neural network applications. University of Pennsylvania.
[33] Masters T. (1993): Practical neural network recipes in C++: Morgan Kaufmann.
[34] Kaastra, I. and Boyd, M. (1996): Designing a neural network for forecasting financial and economic time series. Neurocomputing. 10, 3, 215-236.
[35] Kanellopoulos, I. and Wilkinson, G. G. (1997): Strategies and best practice for neural network image classification. International Journal of Remote Sensing. 18,4, 711-725.
[36] Demuth, H. B. and Beale, M. H. (2004): Neural network toolbox for use with MATLAB. User guide. Math Works.
[37] Pajohesh Omran Rahvar, (2009): Geotechnical Reports of Tabriz Metro Line 2,.
|
|
آمار
تعداد مشاهده مقاله: 391
تعداد دریافت فایل اصل مقاله: 396