Abstract:Distributed acoustic sensing (DAS) can obtain real-time vibration signals from engineering events, and identifying these signal characteristics facilitates early warning and prevention of engineering disasters. To systematically investigate the characteristics of DAS signals under different conditions, a DAS monitoring model for geotechnical vibration signals was established using the high-performance discrete element method (DEM) software, MatDEM. The elastic Clump model was employed to construct optical fiber structures and record relative strain data in real time. By integrating DAS vibration signal data from field drop-weight impact tests, the model's validity and effectiveness were verified from three aspects: time-domain characteristics, frequency-domain characteristics, and variation patterns of signals influenced by vibration source parameters. The results demonstrated consistency between simulated and experimental signal characteristics. The signal waveforms consistently exhibited a single-peak pattern with rapid attenuation. Signal frequencies were concentrated, and amplitudes gradually decreased with increasing frequency. As the drop height and mass of the impact weight increased, the signal amplitudes and corresponding signal-to-noise ratios increased. This model can be used for research on vibration mechanism characteristics and for generating large-scale datasets of vibration signals, thereby facilitating further interpretation of DAS signals and their recognition through artificial intelligence technology.
