Multiple linear regression analysis model and artificial neural network model to calculate and estimate the blast induced area of the tunnel face. A case study Deo Ca tunnel

  • Cơ quan:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Saint Petersburg Mining University, Saint Petersburg, Russia Federation

  • *Tác giả liên hệ:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Nhận bài: 06-01-2022
  • Sửa xong: 23-04-2022
  • Chấp nhận: 20-05-2022
  • Ngày đăng: 30-06-2022
Trang: 43 - 52
Lượt xem: 4214
Lượt tải: 2374
Yêu thích: 1.0, Số lượt: 236
Bạn yêu thích

Tóm tắt:

The area of the tunnel face after the blasting is a very important factor in underground excavations where the drilling and blasting method is used. The area of the tunnel face, this is a significant factor that has affected the cost and safety of underground constructions in case of using the drilling and blasting method in underground excavations. Because the area of the tunnel after the blasting depends on many different parameters, such as geological conditions in the area where the tunnel is located, the parameters of the explosion, and other parameters of the tunnel, it is very difficult to accurately determine the value of the tunnel face area after blasting. This paper uses the data obtained in the actual blasting of the Deo Ca tunnel (39 datasets) to build the computational and prediction models for the area of the tunnel face after blasting by two methods, the multiple linear regression analysis method and the method of using artificial neural network (ANN). Determination coefficient R2 of multiple linear regression analysis (MLRA) method and ANN method were obtained at 0.9224, and 0.9449, respectively. The applicability of the multiple linear regression analysis method and ANN method in calculating and predicting tunnel face area after blasting were validated based on a comparison with the results of the tunnel face area after blasting in practice.

Trích dẫn
Thanh Chi Nguyen, Anh Ngoc Do, Vi Van Pham và Gospodarikov Alexandr, 2022. Multiple linear regression analysis model and artificial neural network model to calculate and estimate the blast induced area of the tunnel face. A case study Deo Ca tunnel, Tạp chí Khoa học kỹ thuật Mỏ - Địa chất, số 63, kỳ 3, tr. 43-52.
Tài liệu tham khảo

Alsarraf A., Moayedi H., Rashid A. S. A. , Muazu M. A. , Shahsavar A., (2019). Application of PSO-ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system. Eng Comput 36:1-14.

Armaghani, D. J., Hajihassani, M., Mohamad, E. T., Marto, A., Noorani, S. A., (2014). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian J. Geosci. 7 (12), 5383-5396.

Armaghani D. J., Shoib R., Faizi K., Rashid A. S. A., (2017). Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391-405.

Danial J. A., Shoib R. S. N., Faizi K., Rashid A. S. A., (2015). Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput and Applic. DOI 10.1007/s00521-015-2072-z.

Dey, K., Murthy, V. M. S. R., (2012). Prediction of blast induced over break from un-controlled burn-cut blasting in tunnel driven through medium rock class. Tunn. Undergr. Space Technol. 28, 49-56.

Esmaeili, M., Osanloo, M., Rashidinejad, F., Aghajani, A. B., Taji, M., (2014). Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng. Comput. 30 (4), 549-558.

Gordan, B., Armaghani, D. J., Hajihassani, M., Monjezi, M., (2016). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng. Comput. 32 (1), 85-97.

Hajihassani, M., Armaghani, D. J., Sohaei, H., Tonnizam, E. M., Marto, A., (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl. Acoust. 80, 57-67.

Hasanipanah, M., Noorian-Bidgoli, M., Armaghani, D. J., Khamesi, H., (2016a). Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng. Comput. 32 (4), 705-715.

Hasanipanah, M., Naderi, R., Kashir, J., Noorani, S. A., Qaleh, A. Z. A., (2017a). Prediction of blast-produced ground vibration using particle swarm optimization. Engineering with Computers 33 (2), 173-179.

Holland, (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor, USA.

Hecht-Nielsen R., (1987). Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, USA, pp 11-14.

Jang, H., Topal, E., (2013). Optimizing over break prediction based on geological parameters comparing multiple regression analysis and artificial neural network. Tunn. Undergr. Space Technol. 38, 161-169.

Koopialipoor, M., Armaghani, D. J., Haghighi, M., Ghaleini, E. N., (2017). A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull. Eng. Geol. Environ. http://dx.doi.org/10.1007/s10064-017-1116 -2.

Lawal A. I., Kwon S., (2022). Application of artificial intelligence to rock mechanics: An overview. Journal of Rock Mechanics and Geotechnical Engineering. 13(1): 248-266. 

Longqi L., Moayedi H., Ahmad S. A., Rahman S. S., Nguyen H., (2019). Optimizing an ANN model with genetic algorithm (GA) predicting load‑settlement behaviours of eco‑friendly raft‑pile foundation (ERP) system. Engineering with Computers. https://doi.org /10.1007/ s00366-019-00767 -4.

Liu, Y., Hou, S., (2019). Rockburst prediction based on particle swarm optimization and machine learning algorithm. In: Proceedings of the 3rd international conference. ICITG, pp. 290-303.

Mahtab, M. A., Rossier, K., Kalamaras, G. S., Grasso, P., (1997). Assessment of geological over break for tunnel design and contractual claims. Int. J. Rock Mech. Min. Sci. 34 (3-4). 

Moayedi H., Foong L. K., Nguyen H., Bui D. T., Jusoh W. A. W., Rashid A. S. A., (2019). Optimizing ANN models with PSO for predicting in short building seismic response. Eng Comput 36:1-16.

Mohammad E., Morteza O., Farshad R., Abbas A. B., Mohammad T., (2014). Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with Computers. 30, 549-558

Mohamad E. T., Faradonbeh R. S., Armaghani D. J., Monjezi M., Majid M. Z. A., (2017). An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28:393-406.

Mohsen H., Armaghani D. J., Monjezi M., Mohamad E. T., Marto A., (2015). Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci. DOI 10.1007/s12665-015-4274-1.

Monjezi M., Dehghani H., (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446-1453.

Mottahedi, A., Sereshki, F., Ataei, M., (2018). Development of overbreak prediction models in drill and blast tunneling using soft computing methods. Eng. Comput. 34 (1), 45-58.

Nguyen H., Bui X. N., Tran Q. H., Moayedi H., (2019). Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam. Environmental Earth Sciences 78(15). DOI:10.1007/s12665-019-8491-x.

Poli, R., Kennedy, J., Blackwell, T., (2007). Particle swarm optimization an overview. Swarm Intell. 1, 33-57.

Rodríguez del Águilaa N. M., Benítez-Parejo N., (2011). Simple linear and multivariate regression models. Allergologia et Immunopathologia. 39(3), 159-173.

Shahnazar, A., Rad, H. N., Hasanipanah, M., Tahir, M. M., Armaghani, D. J., Ghoroqi, M., (2017). A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ. Earth Sci. 76 (15), 527.

Simpson P. K., (1990) Artificial neural system: foundation, paradigms applications and implementations. Pergamon, New York.

Whitley, D., (1993a). An executable model of a simple genetic algorithm. In Foundations of Genetic Algorithms 2, ed. D. Whitley. Morgan Kaufmann, San Mateo, CA.

Zorlu K., C. Gokceoglu, F. Ocakoglu, H. A. Nefeslioglu, and S. Acikalin, (2008) “Prediction of uniaxial compressive strength of sandstones using petrography- based models,” Engineering Geology, vol. 96, no. 3-4, pp. 141-158, 2008.

Các bài báo khác