Enhancing TBM operations under complex geological conditions using data-driven methods

With the rapid pace of urbanization and increasing demand for underground space, tunnel construction gains popularity for its contribution to the subway transportation system. Tunnel Boring Machines (TBM) have been extensively applied in tunnel construction due to its efficiency, safety, and less im...

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Main Author: Fu, Xianlei
Other Authors: Tiong Lee Kong, Robert
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/171997
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1719972023-12-01T01:52:37Z Enhancing TBM operations under complex geological conditions using data-driven methods Fu, Xianlei Tiong Lee Kong, Robert School of Civil and Environmental Engineering CLKTIONG@ntu.edu.sg Engineering::Civil engineering::Construction technology With the rapid pace of urbanization and increasing demand for underground space, tunnel construction gains popularity for its contribution to the subway transportation system. Tunnel Boring Machines (TBM) have been extensively applied in tunnel construction due to its efficiency, safety, and less impact on the ambient environment. During tunnel construction, the TBM monitoring system generates a huge volume of real-time data, which could be beneficial for improving the TBM performance during tunnel construction. However, due to complex geological conditions and knowledge gaps in TBM-soil interaction mechanism, there is a lack of an effective approach to extract useful knowledge from the data. As a result, the industry still heavily relies on human experience for TBM operations, which may impose risk on safety and compromise the performance of TBM. Therefore, the motivation of this Thesis is to establish a reliable approach that can enhance the overall TBM operation during excavation using data-driven methods. To achieve the overall research objectives, 4 major steps are proposed which starts with quantitative analysis of soil-TBM interaction mechanism, followed by geological detection, TBM performance estimation and lastly the TBM operating parameters optimization with digital twin which the details are elaborated in research objectives 1 to 4, respectively. The key findings are summarized as follows: (1) Through the proposed clustering method, TBM’s key operating parameters behave differently under various geological conditions. (2) The proposed ensemble MobileNets (EMNet) method can identify the soil condition using the photos of the excavated mucks with high accuracy. (3) By considering the spatial, temporal, and causal relationships among the TBM operating parameters, the proposed causal temporal graph convolution network (CT-GCN) method predicts TBM’s parameter with high accuracy. And (4) The established digital twin model enables physical-cyber interaction through the Internet of Things (IoT) sensors, which achieves real-time monitoring of overall TBM working status and enhances TBM’s overall performances through the proposed online-optimization approach. Doctor of Philosophy 2023-11-20T00:23:44Z 2023-11-20T00:23:44Z 2023 Thesis-Doctor of Philosophy Fu, X. (2023). Enhancing TBM operations under complex geological conditions using data-driven methods. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171997 https://hdl.handle.net/10356/171997 10.32657/10356/171997 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). 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::Construction technology
spellingShingle Engineering::Civil engineering::Construction technology
Fu, Xianlei
Enhancing TBM operations under complex geological conditions using data-driven methods
description With the rapid pace of urbanization and increasing demand for underground space, tunnel construction gains popularity for its contribution to the subway transportation system. Tunnel Boring Machines (TBM) have been extensively applied in tunnel construction due to its efficiency, safety, and less impact on the ambient environment. During tunnel construction, the TBM monitoring system generates a huge volume of real-time data, which could be beneficial for improving the TBM performance during tunnel construction. However, due to complex geological conditions and knowledge gaps in TBM-soil interaction mechanism, there is a lack of an effective approach to extract useful knowledge from the data. As a result, the industry still heavily relies on human experience for TBM operations, which may impose risk on safety and compromise the performance of TBM. Therefore, the motivation of this Thesis is to establish a reliable approach that can enhance the overall TBM operation during excavation using data-driven methods. To achieve the overall research objectives, 4 major steps are proposed which starts with quantitative analysis of soil-TBM interaction mechanism, followed by geological detection, TBM performance estimation and lastly the TBM operating parameters optimization with digital twin which the details are elaborated in research objectives 1 to 4, respectively. The key findings are summarized as follows: (1) Through the proposed clustering method, TBM’s key operating parameters behave differently under various geological conditions. (2) The proposed ensemble MobileNets (EMNet) method can identify the soil condition using the photos of the excavated mucks with high accuracy. (3) By considering the spatial, temporal, and causal relationships among the TBM operating parameters, the proposed causal temporal graph convolution network (CT-GCN) method predicts TBM’s parameter with high accuracy. And (4) The established digital twin model enables physical-cyber interaction through the Internet of Things (IoT) sensors, which achieves real-time monitoring of overall TBM working status and enhances TBM’s overall performances through the proposed online-optimization approach.
author2 Tiong Lee Kong, Robert
author_facet Tiong Lee Kong, Robert
Fu, Xianlei
format Thesis-Doctor of Philosophy
author Fu, Xianlei
author_sort Fu, Xianlei
title Enhancing TBM operations under complex geological conditions using data-driven methods
title_short Enhancing TBM operations under complex geological conditions using data-driven methods
title_full Enhancing TBM operations under complex geological conditions using data-driven methods
title_fullStr Enhancing TBM operations under complex geological conditions using data-driven methods
title_full_unstemmed Enhancing TBM operations under complex geological conditions using data-driven methods
title_sort enhancing tbm operations under complex geological conditions using data-driven methods
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/171997
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