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Polymers
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Advances and Machine Learning Approaches for the Health Monitoring and...
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Advances and Machine Learning Approaches for the Health Monitoring and Integrity Assessment of Structures

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 1885

Special Issue Editors

Dr. Donato Perfetto

E-Mail Website
Guest Editor
Department of Engineering, University of Campania “Luigi Vanvitelli”, Roma St. 29, 81031 Aversa, Italy
Interests: product design; structural health monitoring (SHM); composite materials; finite element analysis (FEA)
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro De Luca

E-Mail Website
Guest Editor
Department of Engineering, University of Campania “Luigi Vanvitelli”, Roma St. 29, 81031 Aversa, Italy
Interests: structural health monitoring; finite element analysis; structural behavior; mechanical design; crashworthiness
Special Issues, Collections and Topics in MDPI journals
Dr. America Califano

E-Mail Website
Guest Editor
1. Department of Physics and Astronomy “Galileo Galilei”, University of Padua, Marzolo St. 8, 35131 Padua, Italy
2. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Richard Birkelands vei 2B, 7491 Trondheim, Norway
Interests: Structural Health Monitoring (SHM); composite materials; organic materials; machine learning; conservation and preservation
Dr. Nan Yue

E-Mail Website
Guest Editor
Center of Excellence in Artificial Intelligence for Structures, Prognostics and Health Management, Department of Aerospace Structures and Composite, Faculty of Aerospace Engineering, Delft University of Technology, 2629HS Delft, The Netherlands
Interests: Structural Health Monitoring (SHM); composite materials; damage diagnosis; remaining useful life prediction; explainable AI; SHM reliability

Special Issue Information

Dear Colleagues,

Structural Health Monitoring (SHM) and damage diagnosis approaches have widely demonstrated their importance in assessing the integrity of damage-tolerant components. The integration of such technology for engineering structures leads to numerous benefits in terms of maintenance costs, repair operations, and carbon footprint reductions. Nevertheless, the acquired data must be rigorously processed in order to obtain reliable information about the structure’s actual state of health.

This Special Issue will report on the advancements in SHM and machine learning approaches to assess the performance and conditions of engineering structures through monitoring data by taking into account challenging environmental and operational environments, new sensing technologies, and novel data analysis under the theme of Industry 4.0. The results of theoretical, analytical, numerical, or experimental investigation can be presented. Review articles can be also proposed.

The key focus of this Special Issue is on SHM strategies for fibre-reinforced composite materials. Articles on advances and machine learning-based approaches for SHM for other families of materials (smart, organic, additively manufactured, etc.) are also highly appreciated.

Potential topics of interest for this Special Issue include but are not limited to:

  • Machine learning algorithms and novel approaches for predicting unknown scenarios;
  • Numerical methods for SHM systems simulation;
  • Numerical and experimental investigations of damage detection and characterization;
  • Novel damage detection and characterization algorithms;
  • Novel signal and image processing algorithms for damage diagnosis and prognosis;
  • Assessment of load-carrying capacity of pristine and damaged structures;
  • Smart methods to enhance the durability of structures;
  • Environmental and operational effects on SHM reliability;
  • Novel multi-functional sensors for structural health monitoring;
  • Structural Health Monitoring-informed maintenance management.

Dr. Donato Perfetto
Dr. America Califano
Dr. Alessandro De Luca
Dr. Nan Yue
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SHM systems
  • damage detection
  • composite materials
  • organic materials
  • additively manufactured materials
  • data analysis
  • machine learning
  • structural analysis
  • finite element modelling
  • damage tolerance

Published Papers (2 papers)

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Research

22 pages, 5460 KiB  
Article
Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network
by Haofan Yu, Aldyandra Hami Seno, Zahra Sharif Khodaei and M. H. Ferri Aliabadi
Polymers 2022, 14(19), 3947; https://doi.org/10.3390/polym14193947 - 21 Sep 2022
Cited by 6 | Viewed by 1681
Abstract
This paper proposes a novel method for multi-class classification and uncertainty quantification of impact events on a flat composite plate with a structural health monitoring (SHM) system by using a Bayesian neural network (BNN). Most of the existing research in passive sensing has [...] Read more.
This paper proposes a novel method for multi-class classification and uncertainty quantification of impact events on a flat composite plate with a structural health monitoring (SHM) system by using a Bayesian neural network (BNN). Most of the existing research in passive sensing has focused on deterministic approaches for impact detection and characterization. However, there are variability in impact location, angle and energy in real operational conditions which results in uncertainty in the diagnosis. Therefore, this paper proposes a reliability-based impact characterization method based on BNN for the first time. Impact data are acquired by a passive sensing system of piezoelectric (PZT) sensors. Features extracted from the sensor signals, such as their transferred energy, frequency at maximum amplitude and time interval of the largest peak, are used to develop a BNN for impact classification (i.e., energy level). To test the robustness and reliability of the proposed model to impact variability, it is trained with perpendicular impacts and tested by variable angle impacts. The same dataset is further applied in a method called multi-artificial neural network (multi-ANN) to compare its ability in uncertainty quantification and its computational efficiency against the BNN for validation of the developed meta-model. It is demonstrated that both the BNN and multi-ANN can measure the uncertainty and confidence of the diagnosis from the prediction results. Both have very high performance in classifying impact energies when the networks are trained and tested with perpendicular impacts of different energy and location, with 94% and 98% reliable predictions for BNN and multi-ANN, respectively. However, both metamodels struggled to detect new impact scenarios (angled impacts) when the data set was not used in the development stage and only used for testing. Including additional features improved the performance of the networks in regularization; however, not to the acceptable accuracy. The BNN significantly outperforms the multi-ANN in computational time and resources. For perpendicular impacts, both methods can reach a reliable accuracy, while for angled impacts, the accuracy decreases but the uncertainty provides additional information that can be further used to improve the classification. Full article
(This article belongs to the Special Issue Advances and Machine Learning Approaches for the Health Monitoring and Integrity Assessment of Structures)
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12 pages, 6730 KiB  
Article
Analysis of Force Sensing Accuracy by Using SHM Methods on Conventionally Manufactured and Additively Manufactured Small Polymer Parts
by Alireza Modir and Ibrahim Tansel
Polymers 2022, 14(18), 3755; https://doi.org/10.3390/polym14183755 - 08 Sep 2022
Cited by 2 | Viewed by 1298
Abstract
Fabricating complex parts using additive manufacturing is becoming more popular in diverse engineering sectors. Structural Health Monitoring (SHM) methods can be implemented to reduce inspection costs and ensure structural integrity and safety in these parts. In this study, the Surface Response to Excitation [...] Read more.
Fabricating complex parts using additive manufacturing is becoming more popular in diverse engineering sectors. Structural Health Monitoring (SHM) methods can be implemented to reduce inspection costs and ensure structural integrity and safety in these parts. In this study, the Surface Response to Excitation (SuRE) method was used to investigate the wave propagation characteristics and load sensing capability in conventionally and additively manufactured ABS parts. For the first set of the test specimens, one conventionally manufactured and three additively manufactured rectangular bar-shaped specimens were prepared. Moreover, four additional parts were also additively manufactured with 30% and 60% infill ratios and 1 mm and 2 mm top surface thicknesses. The external geometry of all parts was the same. Ultrasonic surface waves were generated using three different signals via a piezoelectric actuator bonded to one end of the part. At the other end of each part, a piezoelectric disk was bonded to monitor the response to excitation. It was found that hollow sections inside the 3D printed part slowed down the wave travel. The Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) were implemented for converting the recorded sensory data into time–frequency images. These image datasets were fed into a convolutional neural network for the estimation of the compressive loading when the load was applied at the center of specimens at five different levels (0 N, 50 N, 100 N, 150 N, and 200 N). The results showed that the classification accuracy was improved when the CWT scalograms were used. Full article
(This article belongs to the Special Issue Advances and Machine Learning Approaches for the Health Monitoring and Integrity Assessment of Structures)
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