Statistical study of squeezing for soft rocks based on factor and regression analyses of effective parameters

Akbariforouz, Mohammadreza1,2; Zhao, Qi2; Chen, Kewei3; Baghbanan, Alireza4; Dehnavi, Roohollah Narimani5; Zheng, Chunmiao1,3

2023-03-01

10.1016/j.ijrmms.2022.105306

International Journal of Rock Mechanics and Minings Sciences   Impact Factor & Quartile

1365-1609

1873-4545

The time-dependent deformation of rocks due to stress released by excavation is referred to as squeezing. Accurate evaluation of the squeezing at the design stage can dramatically reduce technical problems and the financial costs of underground structures. Although various methods are presented to predict tunnel squeezing at the preliminary stage, being site-specific and incorporating incomplete databases are deficiencies of the available procedures. In this study, based on a comprehensive literature review, we prepared a database of tunnel squeezing for soft rocks, including possible effective parameters. Statistical processing methods such as univariate, reduction, and cleaning were employed to improve the statistical quality of the database. The statistically-processed datasets were also validated based on various scales such as accuracy, convergence, and usefulness. Significant predictors of squeezing are recognized as the ratio of strength to stress and the rock mass classification system. New squeezing criteria were developed using binary and multi-class regression methods to predict the squeezing occurrence and intensity of soft rocks. The results are confirmed by a Multilayer Perceptron Feed-Forward Neural Network and are compared to well-known empirical equations. The developed equations are more accurate comparing the empirical equations used to predict the squeezing of soft rocks. This methodology can be utilized at the design stage for another database to predict squeezing rocks for topographic-stress and tectonic-stress-based cases.

Squeezing Statistical processing methods Regression analyses Neural network Empirical equations

SCI

English

Others

National Natural Science Foundation of China[41890852] ; FCE Startup Fund for New Recruits at the Hong Kong Polytechnic University[P0034042] ; Council of the Hong Kong Special Administrative Region, China[PolyU 25220021]

Engineering ; Mining & Mineral Processing

Engineering, Geological ; Mining & Mineral Processing

WOS:000925621300001

PERGAMON-ELSEVIER SCIENCE LTD

Web of Science

1.EIT Inst Adv Study, Ningbo, Peoples R China
2.Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Hong Kong, Peoples R China
3.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen, Peoples R China
4.Isfahan Univ Technol IUT, Dept Min Engn, Esfahan, Iran
5.Univ Leoben, Min Engn & Mineral Econ, Leoben, Austria

Akbariforouz, Mohammadreza,Zhao, Qi,Chen, Kewei,et al. Statistical study of squeezing for soft rocks based on factor and regression analyses of effective parameters[J]. International Journal of Rock Mechanics and Minings Sciences,2023,163.

Akbariforouz, Mohammadreza,Zhao, Qi,Chen, Kewei,Baghbanan, Alireza,Dehnavi, Roohollah Narimani,&Zheng, Chunmiao.(2023).Statistical study of squeezing for soft rocks based on factor and regression analyses of effective parameters.International Journal of Rock Mechanics and Minings Sciences,163.

Akbariforouz, Mohammadreza,et al."Statistical study of squeezing for soft rocks based on factor and regression analyses of effective parameters".International Journal of Rock Mechanics and Minings Sciences 163(2023).