Grouting in rock cavern using data mining 1

Ground excavation, especially for underground caverns, is a more common sight these days as more underground spaces are being utilised to cope with decreasing land area. Grouting is then essential in preventing excessive water seepage, water level drawdown and maintaining the hydraulic gradient arou...

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Main Author: Nur Shafiyyah Suhaimi
Other Authors: Zhao Zhiye
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/150092
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1500922021-05-21T07:12:27Z Grouting in rock cavern using data mining 1 Nur Shafiyyah Suhaimi Zhao Zhiye School of Civil and Environmental Engineering CZZHAO@ntu.edu.sg Engineering::Civil engineering Ground excavation, especially for underground caverns, is a more common sight these days as more underground spaces are being utilised to cope with decreasing land area. Grouting is then essential in preventing excessive water seepage, water level drawdown and maintaining the hydraulic gradient around the cavern. An exact amount of grouting should be designed in order to avoid excessive grouting and high post-grouting cost due to insufficient grout. Past studies have shown that a more analytical and accurate approach to grout design is lacking. In this study, we propose to develop predictive models for grouting volume using a data-mining process called Artificial Neural Network (ANN). Key input parameters such as flow rate, Q value, location of drilling where water seepage starts and water ingress pressure will be used to generate the predictive models and obtain a more accurate grout volume. After a thorough neural network analysis, the grout volume at each individual station was found to be closely correlated to all four parameters that are a part of hydrogeological and geological conditions. The correlation coefficient value between the input parameters and the output were also found to be significantly affected by the engineering data. Improvements are then being made to ensure that future analysis will be carried out in a more accurate manner. Bachelor of Engineering (Civil) 2021-05-21T07:12:27Z 2021-05-21T07:12:27Z 2021 Final Year Project (FYP) Nur Shafiyyah Suhaimi (2021). Grouting in rock cavern using data mining 1. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150092 https://hdl.handle.net/10356/150092 en GE-24 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
spellingShingle Engineering::Civil engineering
Nur Shafiyyah Suhaimi
Grouting in rock cavern using data mining 1
description Ground excavation, especially for underground caverns, is a more common sight these days as more underground spaces are being utilised to cope with decreasing land area. Grouting is then essential in preventing excessive water seepage, water level drawdown and maintaining the hydraulic gradient around the cavern. An exact amount of grouting should be designed in order to avoid excessive grouting and high post-grouting cost due to insufficient grout. Past studies have shown that a more analytical and accurate approach to grout design is lacking. In this study, we propose to develop predictive models for grouting volume using a data-mining process called Artificial Neural Network (ANN). Key input parameters such as flow rate, Q value, location of drilling where water seepage starts and water ingress pressure will be used to generate the predictive models and obtain a more accurate grout volume. After a thorough neural network analysis, the grout volume at each individual station was found to be closely correlated to all four parameters that are a part of hydrogeological and geological conditions. The correlation coefficient value between the input parameters and the output were also found to be significantly affected by the engineering data. Improvements are then being made to ensure that future analysis will be carried out in a more accurate manner.
author2 Zhao Zhiye
author_facet Zhao Zhiye
Nur Shafiyyah Suhaimi
format Final Year Project
author Nur Shafiyyah Suhaimi
author_sort Nur Shafiyyah Suhaimi
title Grouting in rock cavern using data mining 1
title_short Grouting in rock cavern using data mining 1
title_full Grouting in rock cavern using data mining 1
title_fullStr Grouting in rock cavern using data mining 1
title_full_unstemmed Grouting in rock cavern using data mining 1
title_sort grouting in rock cavern using data mining 1
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150092
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