Prediction of Tunnel Cross-Sectional Area After Blastin

• Autorzy: Nguyen Chi Thanh , Nguyen Nghia Viet

Identyfikatory

DOI

10.29227/IM-2023-02-11

Warianty tytułu

PL

Prognozowanie pola przekroju poprzecznego tunelu po wykonaniu strzelania

• Konferencja: POL-VIET 2023 — the 7th International Conference POL-VIET
• Języki publikacji: EN

Abstrakty

EN

In this paper, two methods to predict and calculate the area of the tunnel face after the blasting were used. The first one is an artificial intelligence method using an artificial neural network system (ANN) model, and the second one – the support vector regression (SVR). After building predictive models for the area of the tunnel face after blasting by both methods, on the basis of comparing the results obtained in both methods, the performance of these models was assessed through the root mean square error RMSE and the coefficient of determination R2. RMSE and R2 values of the artificial neural network system (ANN) model were obtained as 0.1473 and 0.903 in training datasets, respectively. These values are 0.1497 and 0.9107 in testing datasets. In the SRV model, RMSE and R2 were equaled to 0.1228 and 0.9331 in training datasets, respectively. These values are 0.1708 and 0.9055, respectively in testing datasets. It can be concluded that artificial intelligence using ANN and SVM models can be used to predict the area of the tunnel face after blasting with high accuracy.

Słowa kluczowe

EN

ANN; SVR; tunnel; drilling-blasting method; cross-sectional area of tunnel; prediction

PL

tunele; ANN; SVR

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2023
• Tom: no. 2, vol. 1
• Strony: 39--47
• Opis fizyczny: Bibliogr. 13 poz., tab., wykr., zdj.

Twórcy

autor

Nguyen Chi Thanh

• Faculty of Civil Engineering, Hanoi University of Mining and Geology, 18 Vien Stress, Hanoi, VietNam

autor

Nguyen Nghia Viet

• Faculty of Civil Engineering, Hanoi University of Mining and Geology, 18 Vien Stress, Hanoi, VietNam

Bibliografia

• 1. 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.
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• 3. 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.
• 4. 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.
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• 6. 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.
• 7. 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.
• 8. Chi T. N., Do N. A, Pham V. V., Nguyen P. T., Gospodarikov. A., (2022). Prediction of blast-induced the area of the tunnel face in underground excavations using fuzzy set theory ANFIS and artificial neural network ANN. International Journal of GEOMATE, 2022, 23(95), 136-143.
• 9. Nguyen H, Bui XN, Tran QH, 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.
• 10. 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).
• 11. Mohammad Esmaeili, Morteza Osanloo, Farshad Rashidinejad, Abbas Aghajani Bazzazi, Mohammad Taji (2014). Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with Computers. 30, 549–558
• 12. 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.
• 13. Simpson PK (1990) Artificial neural system: foundation, paradigms applications and implementations. Pergamon, New York.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)