Abstract
Advanced geological prediction of tunnels has become an indispensable task to ensure the safety and effectiveness of tunnel construction before excavation in karst areas. Geological disasters caused by unfavorable geological conditions, such as karst caves, faults, and broken zones ahead of a tunnel face, are highly sudden and destructive. Determining how to predict the spatial location and geometric size of unfavorable geological bodies accurately is a challenging problem. In order to facilitate a three-dimensional quantitative analysis of the filler material ahead of the tunnel face, a biorthogonal wavelet with short support, linear phase, and highly matching waveform of ground penetrating radar (GPR) wavelet is constructed by lifting a simple and general initial filter on the basis of lifting wavelet theory. A method for a time-energy density analysis of wavelet transforms (TEDAWT) is proposed in accordance with the biorthogonal wavelet. Fifteen longitudinal and horizontal survey lines are used to detect void fillers of different heights. Then, static correction, DC bias, gain, band-pass filtering, and offset processing are performed in the original GPR profile to enhance reflected signals and converge diffraction signals. A slice map of GPR profile is generated in accordance with the relative position of longitudinal and horizontal survey lines in space. The wavelet transform analysis of a single-channel signal of each survey line is performed by adopting the TEDAWT method because of the similar rule of the single-channel signal of GPR on the waveform overlay and the ability of the constructed wavelet basis to highlight the time-frequency characteristics of GPR signals. The characteristic value points of the first and second interfaces of the void fillers can be clearly determined, and the three-dimensional spatial position and geometric sizes of different void fillers can be obtained. Therefore, the three-dimensional visualization of GPR data is realized. Results show that the TEDAWT method has a good practical application effect in the quantitative identification of void fillers, which provides a basis for the interpretation of advanced geological prediction data of tunnels and for the construction decision.
Highlights
Infrastructure monitoring during construction and service has made significant progress in recent decades [1,2,3,4], benefitting from the rapid advances in sensing techniques [5,6,7] and signal-processing algorithms [8,9,10]
The three-dimensional visualization of the spatial position and geometric size of the void fillers is realized, which provides a reference for the interpretation of advanced geological prediction data of tunnel engineering and is highly advantageous for the safety and efficiency of onsite construction
Three longitudinal survey lines, which are denoted as x1, x2, and x3, are arranged perpendicularly to the x-axis to describe the three-dimensional size information of void fillers; x2 is arranged along the center of the model box; and x1
Summary
Infrastructure monitoring during construction and service has made significant progress in recent decades [1,2,3,4], benefitting from the rapid advances in sensing techniques [5,6,7] and signal-processing algorithms [8,9,10]. For the vicinity of the tunnel working surface that is a narrow detection space including busy construction operations, GPR is the best detection equipment for unfavorable geological conditions, such as karst caves, faults, joint fissures, weak fracture zones, and water-bearing structures. The three-dimensional visualization of the spatial position and geometric size of the void fillers is realized, which provides a reference for the interpretation of advanced geological prediction data of tunnel engineering and is highly advantageous for the safety and efficiency of onsite construction. The scale and dual-scale filter coefficients of the new biorthogonal wavelet filter bank are obtained by using dual and primal lifting of the initial filter and combining with the particle swarm algorithm for optimization search, as shown as follows. In accordance with a Moyal inner product theorem, the following equation is given
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