Dexian Li

Accurate acoustic emission (AE) source localization in complex structures remains challenging due to non-straight wave propagation paths and the difficulty of obtaining reliable wave velocity models. To address these issues, this study proposes a velocity-free AE source localization method based on an any-angle pathfinding algorithm. The method integrates the Anya algorithm to estimate geometrically optimal propagation paths and a velocity-free objective function formulated under a weak anisotropy assumption. By avoiding the directional limitations of conventional grid-based pathfinding, the proposed approach provides more accurate distance estimates between potential sources and sensors without requiring prior velocity information. The effectiveness of the method was validated through an AE pulse experiment conducted on a granite specimen containing multiple cylindrical holes. The results demonstrate that the average location error of the proposed method is 19.02 mm, which is less than 38.14 mm of the traditional method and 22.90 mm of the velocity-free method using a grid-based search algorithm. Therefore, the proposed framework is applicable to two-dimensional structures or extruded three-dimensional structures, offering a feasible approach for AE source localization in complex environments.

With the widespread application of acoustic emission (AE) technology in geotechnical engineering, effectively separating and identifying dense AE signals generated during rock fracturing remains a critical challenge. This study proposes an AE event identification technique based on waveform energy envelopes and multi-indicator characteristic parameters. First, the waveform energy envelope is used to adaptively segment dense and partially overlapping AE waveforms without relying on fixed timing parameters. Then, a template sliding-window scan integrating waveform correlation, ring count, rise time, and signal energy is performed to identify candidate AE events. In addition, a time-difference correction and window-stacking strategy is adopted to improve multi-channel arrival picking. Experimental validation on representative single-peak single-event and double-peak multi-waveform cases extracted from laboratory rock-failure tests demonstrates that the proposed method can effectively separate and identify AE waveforms under the tested conditions. Compared with conventional timing-parameter-based segmentation and correlation-dominated matching, the proposed workflow is more robust to waveform attenuation and distortion. The method provides a methodological basis for AE waveform identification and arrival-time extraction in rock-failure monitoring and has potential to support early warning after further validation.