TY - JOUR
T1 - Similarity search and performance prediction of shield tunnels in operation through time series data mining
AU - Zhu, Hehua
AU - Wang, Xin
AU - Chen, Xueqin
AU - Zhang, Lianyang
N1 - Funding Information:
This study was supported by the Fundamental Research Funds for the Central Universities (30919011249), National Natural Science Foundation of China (51478341) ?Service performance deterioration model for segmental shield tunnel structure and application to tunnel maintenance and rehabilitation?, and National Key Research and Development Program (973 Program) (2011CB013800) ?Fundamental theory for the performance evolution and sensing-control of urban metro structures?. Shanghai Shentong Metro Co. Ltd. was sincerely acknowledged for providing the original data.
Funding Information:
This study was supported by the Fundamental Research Funds for the Central Universities ( 30919011249 ), National Natural Science Foundation of China ( 51478341 ) “Service performance deterioration model for segmental shield tunnel structure and application to tunnel maintenance and rehabilitation”, and National Key Research and Development Program (973 Program) ( 2011CB013800 ) “Fundamental theory for the performance evolution and sensing-control of urban metro structures”. Shanghai Shentong Metro Co., Ltd. was sincerely acknowledged for providing the original data.
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/6
Y1 - 2020/6
N2 - Performance analysis and prediction of shield tunnels in operation have become increasingly important during the maintenance strategy planning process. An existing assessment indicator, Tunnel Serviceability Index (TSI), was employed to evaluate the condition of shield tunnels in soft soils in this study. Data mining methods including Long Short-Term Memory (LSTM) and clustering analysis based on Dynamic Time Wrapping (DTW) were utilized to identify the different degradation patterns and predict the performance of shield tunnels. The data mining methods were applied to 4 tunnel intervals of Shanghai Metro Line 1. Four degradation patterns were determined objectively through the clustering approach, each of which showed similar trends and characteristics. Based on the distribution of clusters, interval 2 was determined to have the worst overall condition. The LSTM network was used for performance prediction in each cluster. Compared with the traditional multilayer neural network, the LSTM also exhibited good prediction effectiveness. The predicted data are well consistent with the observed data with correlation coefficient R2 equaling to 88.4%. Finally, a case study using the data from Shanghai Metro Line 2 is conducted for further validation of the above data mining methods.
AB - Performance analysis and prediction of shield tunnels in operation have become increasingly important during the maintenance strategy planning process. An existing assessment indicator, Tunnel Serviceability Index (TSI), was employed to evaluate the condition of shield tunnels in soft soils in this study. Data mining methods including Long Short-Term Memory (LSTM) and clustering analysis based on Dynamic Time Wrapping (DTW) were utilized to identify the different degradation patterns and predict the performance of shield tunnels. The data mining methods were applied to 4 tunnel intervals of Shanghai Metro Line 1. Four degradation patterns were determined objectively through the clustering approach, each of which showed similar trends and characteristics. Based on the distribution of clusters, interval 2 was determined to have the worst overall condition. The LSTM network was used for performance prediction in each cluster. Compared with the traditional multilayer neural network, the LSTM also exhibited good prediction effectiveness. The predicted data are well consistent with the observed data with correlation coefficient R2 equaling to 88.4%. Finally, a case study using the data from Shanghai Metro Line 2 is conducted for further validation of the above data mining methods.
KW - Clustering analysis
KW - Deep learning network
KW - Dynamic time wrapping
KW - Long Short-Term Memory
KW - Tunnel Serviceability Index (TSI)
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U2 - 10.1016/j.autcon.2020.103178
DO - 10.1016/j.autcon.2020.103178
M3 - Article
AN - SCOPUS:85081655819
VL - 114
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
M1 - 103178
ER -