TY - JOUR
T1 - Wavelet Analysis for tunneling-induced ground settlement based on a stochastic model
AU - Ding, Lieyun
AU - Ma, Ling
AU - Luo, Hanbin
AU - Yu, Minghui
AU - Wu, Xianguo
PY - 2011/9
Y1 - 2011/9
N2 - Current practice in predicting tunneling-induced ground settlement has some limitations in describing the time-dependent settlement process due to the existence of measurement error. In this study, settlement data was considered as time series by establishing a stochastic model, while measurement error was regarded as a stationary and normally distributed stochastic process. Furthermore, Wavelet Analysis was introduced to filter the measurement error and extract the actual settlement value, which is similar to denoising in signal processing. In addition, methods such as the unit root test, normality test and ANOVA, were used to testify whether the characteristics of the filtered part of settlement data were consistent with those of measurement error. As a result, an optimal selection of wavelet basis and decomposition level could be made when using Discrete Wavelet Transform. Finally, extensive instrumentation data obtained from a real tunnel project supported our model hypothesis and proved the feasibility of this approach, and decomposing at level 4 with wavelet D16 was proved to achieve the best performance.
AB - Current practice in predicting tunneling-induced ground settlement has some limitations in describing the time-dependent settlement process due to the existence of measurement error. In this study, settlement data was considered as time series by establishing a stochastic model, while measurement error was regarded as a stationary and normally distributed stochastic process. Furthermore, Wavelet Analysis was introduced to filter the measurement error and extract the actual settlement value, which is similar to denoising in signal processing. In addition, methods such as the unit root test, normality test and ANOVA, were used to testify whether the characteristics of the filtered part of settlement data were consistent with those of measurement error. As a result, an optimal selection of wavelet basis and decomposition level could be made when using Discrete Wavelet Transform. Finally, extensive instrumentation data obtained from a real tunnel project supported our model hypothesis and proved the feasibility of this approach, and decomposing at level 4 with wavelet D16 was proved to achieve the best performance.
KW - Ground settlement
KW - NATM
KW - Stochastic model
KW - Time-series analysis
KW - Wavelet Analysis
UR - http://www.scopus.com/inward/record.url?scp=79955776768&partnerID=8YFLogxK
UR - https://www.journals.elsevier.com/tunnelling-and-underground-space-technology
U2 - 10.1016/j.tust.2011.03.005
DO - 10.1016/j.tust.2011.03.005
M3 - Article
AN - SCOPUS:79955776768
VL - 26
SP - 619
EP - 628
JO - Tunnelling and Underground Space Technology
JF - Tunnelling and Underground Space Technology
SN - 0886-7798
IS - 5
ER -