Grouting knowledge discovery based on data mining

The existence of highly complex and heterogeneous geological and hydrogeological conditions makes it cumbersome to determine grouting parameters for a cost-efficient grouting process. Although many empirical, numerical and analytical models have been proposed previously, there are still some gaps be...

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Main Authors: Liu, Qian, Xiao, Fei, Zhao, Zhiye
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160929
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1609292022-08-08T02:34:34Z Grouting knowledge discovery based on data mining Liu, Qian Xiao, Fei Zhao, Zhiye School of Civil and Environmental Engineering Nanyang Centre for Underground Space (NCUS) Engineering::Civil engineering Data Mining Grout Take The existence of highly complex and heterogeneous geological and hydrogeological conditions makes it cumbersome to determine grouting parameters for a cost-efficient grouting process. Although many empirical, numerical and analytical models have been proposed previously, there are still some gaps between the existing predictive models and practical grouting applications, leading to the fact that practical grouting design mainly depends on onsite engineers’ experience. In this study, we propose to use data mining to discover grouting knowledge from onsite data of a project in Singapore. After systematic analysis of data concerning the geological information, hydrogeological conditions and grouting records, an artificial neural network was structured to further extract grouting knowledge, based on which the grout take can be estimated under given geological and hydrogeological conditions. The grout take at individual station is found to be closely correlated with overall water inflow and Q value of rock mass, making it promising to estimate the potential grout take, once probe hole and face mapping information are given before pre-grouting. The degree of correlation between input parameters and the corresponding model accuracy are significantly affected by the classification methods used. 2022-08-08T02:34:34Z 2022-08-08T02:34:34Z 2020 Journal Article Liu, Q., Xiao, F. & Zhao, Z. (2020). Grouting knowledge discovery based on data mining. Tunnelling and Underground Space Technology, 95, 103093-. https://dx.doi.org/10.1016/j.tust.2019.103093 0886-7798 https://hdl.handle.net/10356/160929 10.1016/j.tust.2019.103093 2-s2.0-85073726656 95 103093 en Tunnelling and Underground Space Technology © 2019 Elsevier Ltd. All rights reserved.
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
Data Mining
Grout Take
spellingShingle Engineering::Civil engineering
Data Mining
Grout Take
Liu, Qian
Xiao, Fei
Zhao, Zhiye
Grouting knowledge discovery based on data mining
description The existence of highly complex and heterogeneous geological and hydrogeological conditions makes it cumbersome to determine grouting parameters for a cost-efficient grouting process. Although many empirical, numerical and analytical models have been proposed previously, there are still some gaps between the existing predictive models and practical grouting applications, leading to the fact that practical grouting design mainly depends on onsite engineers’ experience. In this study, we propose to use data mining to discover grouting knowledge from onsite data of a project in Singapore. After systematic analysis of data concerning the geological information, hydrogeological conditions and grouting records, an artificial neural network was structured to further extract grouting knowledge, based on which the grout take can be estimated under given geological and hydrogeological conditions. The grout take at individual station is found to be closely correlated with overall water inflow and Q value of rock mass, making it promising to estimate the potential grout take, once probe hole and face mapping information are given before pre-grouting. The degree of correlation between input parameters and the corresponding model accuracy are significantly affected by the classification methods used.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Liu, Qian
Xiao, Fei
Zhao, Zhiye
format Article
author Liu, Qian
Xiao, Fei
Zhao, Zhiye
author_sort Liu, Qian
title Grouting knowledge discovery based on data mining
title_short Grouting knowledge discovery based on data mining
title_full Grouting knowledge discovery based on data mining
title_fullStr Grouting knowledge discovery based on data mining
title_full_unstemmed Grouting knowledge discovery based on data mining
title_sort grouting knowledge discovery based on data mining
publishDate 2022
url https://hdl.handle.net/10356/160929
_version_ 1743119500591497216