High-precision MFMM-SAFT imaging with variable velocity for grouting materials in shield segments multilayer structure
Source abstract
Abstract
Nondestructive testing of grouting in shield tunnels is critical for structural safety. While ultrasonic testing is less susceptible to rebar interference than ground-penetrating radar (GPR), its traditional array imaging methods often fail in complex multilayer structures due to the constant velocity imaging assumption, resulting in image defocusing and positioning errors. To address this issue, we propose a layer velocity based Multi-stencils Fast Marching Method-Synthetic Aperture Focusing Technique (MFMM-SAFT). This method implements multiple suppression in the Radon domain, performs velocity analysis based on common midpoint (CMP) gathers, and employs MFMM for precise wave travel times calculations. Validated through a series of numerical simulations and field experiments, MFMM-SAFT consistently controls imaging errors below 5 %. Layer velocity imaging outperforms root-mean-square (RMS) velocity methods in both accuracy and focusing capability. The detection depth reaches approximately twice the sensor array length. Beyond this range, however, imaging accuracy becomes comparable to traditional constant velocity SAFT due to the inability to precisely obtain layer velocities. This method provides a robust, high-precision tool for structural assessment in grouted shield tunnels, significantly advancing technological progress in tunnel maintenance. • MFMM-SAFT framework offers high precision imaging for grouting materials in shield segments multilayer structure. • Layer velocity imaging keeps quantitative errors below 5 % with superior defect detection. • The effective detection depth of this method can reach twice the length of the sensor array.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Ultrasonics and Acoustic Wave Propagation · Seismic Waves and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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