ArticleSensors (Basel, Switzerland)2019
A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed.
- Identification of Loading Location and Amplitude in Conductive Composite Materials via Deep Learning Method.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review.Sensors (Basel, Switzerland) · 2025Review
- Radar-Based Road Surface Classification Using Range-Fast Fourier Transform Learning Models.Sensors (Basel, Switzerland) · 2025Article
- Artificial Neural Networks for Impact Strength Prediction of Composite Barriers.Materials (Basel, Switzerland) · 2025Article
- A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples.Sensors (Basel, Switzerland) · 2025Article
- CNN-LSTM-Based Damage Localization of Plate Structure.Materials (Basel, Switzerland) · 2025Article
- New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform.Sensors (Basel, Switzerland) · 2025Article
- Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination.Materials (Basel, Switzerland) · 2023Article
- An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection.Sensors (Basel, Switzerland) · 2023Article
- Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation.Sensors (Basel, Switzerland) · 2023Article
- An Impact Localization Solution Using Embedded Intelligence-Methodology and Experimental Verification via a Resource-Constrained IoT Device.Sensors (Basel, Switzerland) · 2023Article
- Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network.Polymers · 2022Article
- A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images.Diagnostics (Basel, Switzerland) · 2022Article
- Hypervelocity Impact Detection and Location for Stiffened Structures Using a Probabilistic Hyperbola Method.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Approaches for Robust Time of Arrival Estimation in Acoustic Emission Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Development of Hybrid Piezoelectric-Fibre Optic Composite Patch Repair Solutions.Sensors (Basel, Switzerland) · 2021Article
- A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts.
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.