Rock mass classification and support for cavern analysis
Q-system and Rock Mass Rating (RMR) are common and dependable rock classification systems used widely by rock engineering industry to distinguish different quality of rocks. These rocks have rock mass properties such as rock strength and deformation modulus, which varies according to their qualit...
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sg-ntu-dr.10356-706882023-03-03T17:19:09Z Rock mass classification and support for cavern analysis Chia, Emmanuel Song Jie Zhao Zhiye School of Civil and Environmental Engineering DRNTU::Engineering::Civil engineering Q-system and Rock Mass Rating (RMR) are common and dependable rock classification systems used widely by rock engineering industry to distinguish different quality of rocks. These rocks have rock mass properties such as rock strength and deformation modulus, which varies according to their qualities and in turn influence the stability of the rock cavern. Other factors such as the use of supports like bolting and shortcrete also contribute to the cavern’s stability but the focus of this report will be on the characterization of rock mass. With the shortage of land, rock engineering has become increasingly important in Singapore as the government explores the use of underground spaces. Even though several excavation projects have been successfully completed in Singapore, such as the Jurong Rock Cavern (JRC) project, much research is still ongoing to discover safer and more efficient ways for underground construction of caverns. This study will explore the effects of cavern dimensions and in-situ stress ratio, applied across five different quality of rocks, through the use of rocscience programme which judges stability based on critical Strength Reduction Factor (SRF) values. It will also evaluate JRC’s choice of 20m by 27m as their primary cavern dimension and suggest an alternate set of dimensions which may be safer. Bachelor of Engineering (Civil) 2017-05-09T06:03:30Z 2017-05-09T06:03:30Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70688 en Nanyang Technological University 60 p. application/pdf |
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DRNTU::Engineering::Civil engineering Chia, Emmanuel Song Jie Rock mass classification and support for cavern analysis |
description |
Q-system and Rock Mass Rating (RMR) are common and dependable rock classification systems
used widely by rock engineering industry to distinguish different quality of rocks. These rocks have
rock mass properties such as rock strength and deformation modulus, which varies according to
their qualities and in turn influence the stability of the rock cavern. Other factors such as the use of
supports like bolting and shortcrete also contribute to the cavern’s stability but the focus of this
report will be on the characterization of rock mass. With the shortage of land, rock engineering has
become increasingly important in Singapore as the government explores the use of underground
spaces. Even though several excavation projects have been successfully completed in Singapore,
such as the Jurong Rock Cavern (JRC) project, much research is still ongoing to discover safer and
more efficient ways for underground construction of caverns. This study will explore the effects of
cavern dimensions and in-situ stress ratio, applied across five different quality of rocks, through the
use of rocscience programme which judges stability based on critical Strength Reduction Factor
(SRF) values. It will also evaluate JRC’s choice of 20m by 27m as their primary cavern dimension
and suggest an alternate set of dimensions which may be safer. |
author2 |
Zhao Zhiye |
author_facet |
Zhao Zhiye Chia, Emmanuel Song Jie |
format |
Final Year Project |
author |
Chia, Emmanuel Song Jie |
author_sort |
Chia, Emmanuel Song Jie |
title |
Rock mass classification and support for cavern analysis |
title_short |
Rock mass classification and support for cavern analysis |
title_full |
Rock mass classification and support for cavern analysis |
title_fullStr |
Rock mass classification and support for cavern analysis |
title_full_unstemmed |
Rock mass classification and support for cavern analysis |
title_sort |
rock mass classification and support for cavern analysis |
publishDate |
2017 |
url |
http://hdl.handle.net/10356/70688 |
_version_ |
1759854339759800320 |