Real-time forecasting of brittle rock failure: a robust acoustic emission approach validated on a large experimental dataset
Abstract Accurate and real-time forecasting of brittle rock failure remains a major challenge in geotechnical engineering and mining safety. In this study, a novel failure forecasting method based on acoustic emission (AE) monitoring was proposed and validated using a large experimental dataset of 403 sandstone specimens subjected to uniaxial compression. A new early warning indicator, WI b —the b-value change rate per unit time—was developed to capture the frequency and amplitude of b-value fluctuations, which intensify as microcracking evolves toward macroscopic failure. WI b was computed continuously during loading, and a statistically adaptive threshold was established using the evolving mean and standard deviation of WI b . The results demonstrated that WI b consistently escalated before failure in all specimens, with warning signals occurring at loads averaging 76.7% of the ultimate compressive strength. Sensitivity analyses confirmed the robustness of the method across variations in b-value calculation parameters and AE sensor configurations. Importantly, WI b and its threshold were calculated solely from real-time data without requiring knowledge of the full loading path, ensuring practical applicability. The proposed approach offers a physically meaningful and operationally viable solution for real-time rock failure forecasting and has potential for integration into field-scale AE or microseismic monitoring systems.
Authors
- Chaodong Xi (ORCID: https://orcid.org/0000-0002-3137-9741)
- Fuqiang Gao
- Zhiguo Lu
- Wenju Liu
- Lei Yang
- Rui Wu
Institutions
- China University of Mining and Technology (CN)
- China Coal Technology and Engineering Group Corp (China) (CN)
- China Coal Research Institute (China) (CN)
Publication Details
- Journal
- GeoEnergy Communications
- Published
- 2026-01-13
- DOI
- https://doi.org/10.1007/s44421-025-00014-1
- Citations
- 1
- Primary Topic
- Rock Mechanics and Modeling
- Type
- article
- Field-Weighted Citation Impact
- 18.34
Funders
- National Natural Science Foundation of China