Ground Penetrating Radar (GPR) has emerged as a pivotal tool for subsurface explorations, particularly in detecting subsurface defects that might endanger structural integrity. While GPR B-scan data visually depict underground conditions, it represents the time delay and amplitude of the returned Electromagnetic (EM) waves, making them complex to interpret due to both their image- like appearance and their inherent waveform changes. To address this complexity, this paper introduces the novel Temporal-Spatial Synthesis Network (TSSNet), designed to harness both temporal and spatial features for enhanced subsurface defect detection in GPR B-scan data. The Echo State Network (ESN), underpinned by Reservoir Computing, is utilized to fit the GPR data and capture its “temporal features”, emphasizing the temporal variations present in the EM waves. Concurrently, the Convolutional Neural Network (CNN) focuses on discerning “spatial features” from the B-scan images, spotlighting spatial patterns that possibly indicate subsurface defects. After extracting these temporal and spatial features, they are synthesized to form a comprehensive representation of the GPR data. The enhanced synthesized feature facilitates precise classification, resulting in heightened differentiation between normal and defect-contained subsurface areas. Experiments on real-world GPR datasets are conducted, with the results underscoring the efficacy of the proposed approach.

This paper analyzes the impact of environmental vibration caused by road traffic operations on precision instrument vibration isolation platforms. The research results show that there are directional differences in the speed response of the vibration isolation platform caused by vehicle load. The speed response is highest in the horizontal direction perpendicular to the vehicle’s motion. The dominant frequency of the dynamic response of the isolation platform caused by road vehicle loads was concentrated at 2.5 Hz, mainly low-frequency vibrations. The monitoring results are basically consistent with the trend of structural vibration changes in the numerical simulations.