Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144665
Title: Performance based support design for horseshoe-shaped rock caverns using 2D numerical analysis
Authors: Nie, Wen
Zhao, Zhiye
Goh, Anthony T. C.
Song, M. K.
Guo, Wei
Zhu, Xing
Keywords: Engineering::Civil engineering
Issue Date: 2018
Source: Nie, W., Zhao, Z., Goh, A. T. C., Song, M. K., Guo, W., & Zhu, X. (2018). Performance based support design for horseshoe-shaped rock caverns using 2D numerical analysis. Engineering Geology, 245, 266-279. doi:10.1016/j.enggeo.2018.09.007
Journal: Engineering Geology
Abstract: Excavation in rock may change the stress field and induce excavation damaged zones (EDZ) in the surrounding rock mass. To consider the development of the EDZ, the support design could be evaluated using the convergence-confinement method (CCM). In this paper, an efficient approach is proposed to evaluate the support design based on rock cavern performance. 2D plane strain models are adopted to simulate the excavation effects of horseshoe cross-section rock caverns using the progressive core replacement method. The performance of rock cavern is investigated using CCM. Parameteric studies are carried out to analyze the effects of rock condition and sequential excavation. It shows the roof displacement is not changing significantly during excavation if Q > 10. It also presents the sequencial excavation can reduce the range of the EDZ, but there are no obvious relationships for different subdivision methods. Using the data from numerical analysis, the relationships among the rock conditions, the sequential excavation parameters and the cavern performances are mapped using artificial neural network (ANN). An evaluation chart for the support design of a rock cavern is proposed by integrating the ANN models into EXCEL software. A case study is presented to verify the accuracy of the proposed method. It illustrates the feasibility of the proposed approach for practical applications with much less computing time.
URI: https://hdl.handle.net/10356/144665
ISSN: 0013-7952
DOI: 10.1016/j.enggeo.2018.09.007
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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