ArticleSensors (Basel, Switzerland)2019
Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review.Sensors (Basel, Switzerland) · 2025Review
- New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform.Sensors (Basel, Switzerland) · 2025Article
- Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives.Sensors (Basel, Switzerland) · 2023Review
- Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network.Polymers · 2022Article
- Deep Learning Approach to Impact Classification in Sensorized Panels Using Self-Attention.Sensors (Basel, Switzerland) · 2022Article
- Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review.Sensors (Basel, Switzerland) · 2021Review
- Correction: Seno, A.H.; Ferri Aliabadi, M.H. Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions.Sensors (Basel, Switzerland) · 2020Article
- A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A parametric investigation of the effect of impactor stiffness as well as environmental and operational conditions on impact contact behaviour and the subsequently generated lamb waves in composite structures is presented. It is shown that differing impactor stiffness generates the most significant changes in contact area and lamb wave characteristics (waveform, frequency, and amplitude). A novel impact localisation method was developed based on the above observations that allows for variations due to differences in impactor stiffness based on modifications of the reference database method and the Akaike Information Criterion (AIC) time of arrival (ToA) picker. The proposed method was compared against a benchmark method based on artificial neural networks (ANNS) and the normalised smoothed envelope threshold (NSET) ToA extraction method. The results indicate that the proposed method had comparable accuracy to the benchmark method for hard impacts under various environmental and operational conditions when trained only using a single hard impact case. However, when tested with soft impacts, the benchmark method had very low accuracy, whilst the proposed method was able to maintain its accuracy at an acceptable level. Thus, the proposed method is capable of detecting the location of impacts of varying stiffness under various environmental and operational conditions using data from only a single impact case, which brings it closer to the application of data driven impact detection systems in real life structures.
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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.