Wavelet Analysis for tunneling-induced ground settlement based on a stochastic model

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.

► We introduce discrete wavelet transform (DWT) to filter the measurement error.
► We prove the measurement error is a stationary and normal process.
► We propose a selection criterion for choosing parameters when using DWT.
► We conclude the varying mode of daily settlement.