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Model-Free Structural Health Monitoring Approaches
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Model-Free Structural Health Monitoring Approaches

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4469

Special Issue Editors

Dr. Donya Hajializadeh

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Surrey, Guildford CM2 7XH, UK
Interests: virtual monitoring of bridges; data-drive structural health monitoring; advanced numerical modeling; risk and reliability assessment of bridges
Dr. Boulent Imam

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences (c5), University of Surrey, Guildford GU2 7XH, UK
Interests: structural health monitoring of bridges; fatigue assessment; metallic bridges; scour and corrosion assessment; reliability assessment of bridges
Special Issues, Collections and Topics in MDPI journals
Dr. Ying Wang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK
Interests: Structural health monitoring; Structural dynamics; Guided wave; Artificial intelligence algorithm and data analytics for civil infrastructure; Concrete with waste materials

Special Issue Information

Dear Colleagues,

With many structures and infrastructures typically well aged and used past their life expectancy and carrying loads beyond their original design capacity, there is an urgent need to develop efficient and reliable structural health monitoring and damage identification systems.

In a broad categorization, structural health monitoring (SHM) systems can be divided into model-based and model-free (data-driven) approaches. The model-based approach detects damages using a numerical model and physical description of the structure behavior. The model-free approach generally relies on the analysis of the structure behavior using data-driven algorithms and without developing a numerical model of the structure. The main advantage of the model-free approach of SHM is its great potential for network-based real-time SHM. This Special Issue will be focused on studies that present novel data-driven and model-free structural health monitoring systems for any type of structure or infrastructure. We welcome all studies that demonstrate the application of physical sensors and remote and smart sensing for developing a data-driven SHM system.

Dr. Donya Hajializadeh
Dr. Boulent Imam
Dr. Ying Wang
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. Sensors 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 2600 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

  • structural health monitoring (SHM)
  • data-driven
  • model-free
  • machine learning

Published Papers (2 papers)

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Research

26 pages, 13378 KiB  
Article
Singular Spectrum Analysis for Modal Estimation from Stationary Response Only
by Chang-Sheng Lin and Yi-Xiu Wu
Sensors 2022, 22(7), 2585; https://doi.org/10.3390/s22072585 - 28 Mar 2022
Cited by 1 | Viewed by 1726
Abstract
Conventional experimental modal analysis uses excitation and response information to estimate the frequency response function. However, many engineering structures face excitation signals that are difficult to measure, so output-only modal estimation is an important issue. In this paper, singular spectrum analysis is employed [...] Read more.
Conventional experimental modal analysis uses excitation and response information to estimate the frequency response function. However, many engineering structures face excitation signals that are difficult to measure, so output-only modal estimation is an important issue. In this paper, singular spectrum analysis is employed to construct a Hankel matrix of appropriate dimensions based on the measured response data, and the observability of the system state space model is used to treat the Hankel matrix as three components containing system characteristics, excitation and noise. Singular value decomposition is used to factorize the data matrix and use the characteristics of the left and right singular matrices to reduce the dimension of the data matrix to improve calculation efficiency. Furthermore, the singular spectrum is employed to estimate the minimum order to reconstruct the Hankel matrix; then, the excitation and noise components can be removed, and the system observability matrix can be obtained. By appropriately a factorizing system observability matrix, we obtain the system matrix to estimate the modal parameters. In addition, the fictitious modes produced by increasing the order of the matrix can be eliminated through the stabilization diagram. Full article
(This article belongs to the Special Issue Model-Free Structural Health Monitoring Approaches)
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27 pages, 5295 KiB  
Article
Damage Detection and Localization under Variable Environmental Conditions Using Compressed and Reconstructed Bayesian Virtual Sensor Data
by Jyrki Kullaa
Sensors 2022, 22(1), 306; https://doi.org/10.3390/s22010306 - 31 Dec 2021
Cited by 9 | Viewed by 1774
Abstract
Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from [...] Read more.
Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and reconstructed signals are used for damage detection and localization in the time domain using spatial or spatiotemporal correlation. Whitening transformation is applied to the training data to take the environmental or operational influences into account. The first principal component of the residuals is used to localize damage and also to design the extreme value statistics control chart for damage detection. The proposed method was studied with a numerical model of a frame structure with a dense accelerometer or strain sensor network. Only five acceleration or three strain signals out of the total 59 signals were stored. The stored and reconstructed data outperformed the raw measurement data in damage detection and localization. Full article
(This article belongs to the Special Issue Model-Free Structural Health Monitoring Approaches)
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