A strategy for obtaining travel time in 3-D Rayleigh wave tomography. (15th March 2022)

• Record Type: Journal Article
• Authors: Luo, Guang-Heng; Pan, Jian-Wen; Wang, Jin-Ting; Jin, Feng
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part B(2022)
• Publication Date: 2022-03-15
• Subjects: Rayleigh wave -- SASW -- Tomography -- Data processing -- Mass concrete -- Void; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108594
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A strategy for obtaining travel time in 3-D Rayleigh wave tomography is presented. Theoretical deduction and FEM are conducted to study the new strategy on 3-D tomography. A small probability event was used to quantitatively determine the void. Suitable sensors arrangement are recommended for engineering detection. Void location, void depth, and void size are analyzed to verify the robustness of strategy. Abstract: The spectral analysis of surface waves (SASW) is widely used to reverse the travel time in three-dimensional (3-D) Rayleigh wave tomography, in which data processing is essential to reduce high-frequency harmonics and diffraction. However, data processing depends on visual judgment and is not suitable for large-scale detection. This study proposes a strategy based on theoretical derivation to process data for the SASW method that can be regarded as an improved SASW method. It retains the component of the Rayleigh wave by setting the part after the maximum value of the signal to zero. Compared with traditional visual recognition, the improved SASW method demonstrates higher efficiency in obtaining travel time in 3-D Rayleigh wave tomography. Numerical experiments were conducted to detect voids with different locations, depths, and sizes using the improved SASW method. A small probability event was used to quantitatively determine the velocity range to distinguish a void from the measured region. The results show that the improved method is robust, and the small probability event can effectively identify voids.