ArticleSensors (Basel, Switzerland)2026
Damage Localization on a Complex Composite Structure Based on NRMSD and Normal Distribution Using Ultrasonic Guided Waves.
Article in Sensors (Basel, Switzerland), 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
Continuous structural health monitoring is essential for ensuring the safe operation of critical engineering systems. Ultrasonic guided waves are widely used for damage detection and localization due to their ability to cover large areas with high sensitivity to structural changes. This work evaluates the robustness of a previously proposed guided-wave localization approach by applying it to the complex geometry of carbon fiber composite plates with integrated omega stringers. The measurement data used in this study were provided within the framework of the Open Guided Waves project. We employ an interpretable machine-learning framework based on the normalized root mean square deviation to extract damage-sensitive features. Damage localization is further improved by modeling the spatial damage probability using a normal distribution, which enhances spatial coverage of the structure. The influence of 13 damages with progressively increasing size on classification and localization performance is systematically analyzed. The proposed method achieves an average classification accuracy of 95% and a mean localization error of 5 mm, demonstrating its suitability for damage characterization in complex composite structures.
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.