Developing prediction models for the cross-sectional areas of tunnels during drilling and blasting

One of the major criteria of drilling and blasting quality during tunneling is the crosssectional area of a tunnel after blasting. The post-blasting cross-sectional area of a tunnel governs also the scope of work during tunnel driving, such as soil/rock removal and haulage, and tunnel lining. This article proposes two methods to predict a cross-sectional area of a tunnel after blasting: the first model uses the artificial intelligences and the adaptive network-based fuzzy inference system (ANFIS); the second model uses the multiple linear regression analysis (MLRA). For the predictive modeling of the post-blasting cross-sectional area of a tunnel, we performed a broad comparison of the numerical results obtained using the two methods (efficiency of a prediction model was estimated by the values of the root-mean square error RMSE and coefficient of determination R2). The values of RMSE and R2 in the MLRA model are 0.2135 and 0.9362, respectively, for the learning datasets, and are 0.1827 and 0.9605, respectively, for the test datasets. In the ANFIS model, RMSE and R2 are 0.099 and 0.9758 for the learning datasets, and are 0.1211 and 0.9704 for the test datasets. On this basis, the conclusion is drawn that the method of the artificial intelligence with the ANFIS model can be used for predicting a cross-sectional area of a tunnel after blasting at a sufficiently high accuracy.

Keywords: drill and blast tunneling, cross-sectional area of tunnel, Adaptive Network-Based Fuzzy Inference System (ANFIS), artificial intelligence method, Multiple Linear Regression Analysis (MLRA), Root-Mean Square Error (RMSE), coefficient of determination R2.
For citation:

Bui Manh Tung, Nguyen Chi Thanh, Gospodarikov A. P., Zatsepin M. A. Developing prediction models for the cross-sectional areas of tunnels during drilling and blasting. MIAB. Mining Inf. Anal. Bull. 2024;(6):31-49. [In Russ]. DOI: 10.25018/0236_1493_2024_6_0_31.

Acknowledgements:

The study was supported by the Ministry of Education and Training of Vietnam and by the Hanoi University of Mining and Geology.

Issue number: 6
Year: 2024
Page number: 31-49
ISBN: 0236-1493
UDK: 622.235
DOI: 10.25018/0236_1493_2024_6_0_31
Article receipt date: 04.08.2022
Date of review receipt: 13.03.2024
Date of the editorial board′s decision on the article′s publishing: 10.05.2024
About authors:

Bui Manh Tung1, PhD (Eng.), e-mail: buimanhtung@humg.edu.vn, ORCID ID: 0000-0003-3559-0656,
Nguyen Chi Thanh1, PhD (Eng.), e-mail: nguyenthanh.xdctn47@gmail.com, ORCID ID: 0000-0003-4455-0234,
A.P. Gospodarikov2, Dr. Sci. (Eng.), Head of Chair, e-mail: kvm1823@mail.ru, ORCID ID: 0000-0003-1018-6841,
M.A. Zatsepin2, Cand. Sc. (Phys.-Math.), Assistant Professor, e-mail: zatsepin_ma@pers.spmi.ru, ORCID ID: 0000-0002-6304-8349,
1 Hanoi University of Mining and Geology, Hanoi, Vietnam,
2 Empress Catherine II Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia.

 

For contacts:

M.A. Zatsepin, e-mail: zatsepin_ma@pers.spmi.ru.

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