ArticleScientific reports2026
Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multistage cyclic loading.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
To address the limitations of insufficient accuracy and timeliness in the early warning of coal instability under multistage cyclic loading, this paper proposes an intelligent recognition and dual early warning method for coal damage based on acoustic emission parameters. The multistage cyclic compression test of coal is performed to obtain five acoustic emission parameters, and the average event intensity (ERR) is used to characterize the sudden changes in coal energy release. The Sparrow Search Algorithm (SSA) is adopted to optimize the hybrid model of the Transformer and Gated Recurrent Unit (GRU) to achieve high-precision recognition of coal damage stages. On this basis, the early warning coefficient (EW) is calculated by using the Isolation Forest algorithm and the CRITIC-TODIM method, and a dual early warning system of "preliminary prompt with ERR and comprehensive confirmation with EW" is established. The results show that the recognition accuracy of the optimized model for the fourth damage stage is 11.7% higher than that of the SSA-GRU model. The early warning lead time of ERR is between 60.5 and 185.7 s, and that of EW is between 10.5 and 101.5 s. The proposed method integrates physical mechanisms and data-driven technology, effectively improving the accuracy and reliability of coal damage early warning under multistage cyclic loading and providing theoretical support and technical reference for the early warning of dynamic disasters in deep coal mines.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.