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Advances in Structural Health Monitoring for Infrastructures
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Special Issue "Advances in Structural Health Monitoring for Infrastructures"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Materials Characterization".

Deadline for manuscript submissions: 20 September 2023 | Viewed by 2152

Special Issue Editors

Prof. Dr. Wei Xu

E-Mail Website
Guest Editor
Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; structural damage identification; vibro-acoustics
Prof. Dr. Rafal Burdzik

E-Mail Website
Guest Editor
Department of Road Transport, Silesian University of Technology, 40-019 Katowice, Poland
Interests: structural health monitoring; structural damage identification; structural dynamics; vibro-acoustics
Special Issues, Collections and Topics in MDPI journals
Dr. Mahmoud Bayat

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA
Interests: structural health monitoring; probabilistic analysis; nonlinear vibrations; machine learning; earthquake engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maosen Cao

E-Mail Website
Guest Editor
Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; structural damage identification; smart materials and structures; applied soft computing; structural vibration and control
Special Issues, Collections and Topics in MDPI journals
Dr. Nizar Faisal Alkayem

E-Mail Website
Guest Editor Assistant
College of Civil Engineering, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; data-driven modeling and artificial intelligence; applied soft computing; model updating

Special Issue Information

Dear Colleagues,

A considerable amount of infrastructure has been constructed over the past few decades. Building materials of infrastructures could encounter degradation, corrosion, fatigue, etc., due to long-term services, as well as environmental and loading factors. Initial defects and damage could become more severe over time and threaten the safety and integrity of infrastructures. In this context, in-service infrastructures are anticipated to face a massive increase in demand for structural health monitoring (SHM) in the next decade. Advanced sensing networks and emerging technologies of big data and artificial intelligence have brought new opportunities and shown significant potential in the SHM field. Hence, it is of great significance to develop advanced sensing, processing, and evaluating technologies for SHM of infrastructures.

This Special Issue aims to collect papers that focus on all aspects of advances in SHM for infrastructures. The collections will be published after being peer-reviewed. The topics include but are not limited to:

  • Structural health monitoring
  • Structural damage identification
  • SHM-oriented building materials
  • Smart sensors
  • In-suit/wireless/online sensing networks
  • Big data/artificial intelligence technologies
  • Modal analysis and signal processing
  • Load identification and monitoring
  • Structural model updating
  • Vibration isolation and control
  • Failure prognostics and early warning
  • Structural health management
  • Structural condition/integrity/safety assessment

Prof. Dr. Wei Xu
Prof. Dr. Rafal Burdzik
Dr. Mahmoud Bayat
Prof. Dr. Maosen Cao
Guest Editors

Dr. Nizar Faisal Alkayem
Guest Editor Assistant

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. Materials 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 2300 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.

Published Papers (2 papers)

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Article
The Study of Localized Crack-Induced Effects of Nonlinear Vibro-Acoustic Modulation
by
Dariusz Broda
,
Krzysztof Mendrok
,
Vadim V. Silberschmidt
,
Lukasz Pieczonka
and
Wieslaw J. Staszewski
Materials 2023, 16(4), 1653; https://doi.org/10.3390/ma16041653 - 16 Feb 2023
Cited by 1 | Viewed by 661
Abstract
The nonlinear interaction of longitudinal vibration and ultrasound in beams with cracks is investigated. The central focus is on the localization effect of this interaction, i.e., the locally enhanced nonlinear vibro-acoustic modulation. Both numerical and experimental investigations are undertaken. The finite element (FE) [...] Read more.
The nonlinear interaction of longitudinal vibration and ultrasound in beams with cracks is investigated. The central focus is on the localization effect of this interaction, i.e., the locally enhanced nonlinear vibro-acoustic modulation. Both numerical and experimental investigations are undertaken. The finite element (FE) method is used to investigate different crack models, including the bi-linear crack, open crack, and breathing crack. A parametric study is performed considering different crack depths, locations, and boundary conditions in a two-dimensional beam model. The study shows that observed nonlinearities (i.e., nonlinear crack–wave modulations) are particularly strong in the vicinity of the crack, allowing not only for crack localization but also for the separation of the crack-induced nonlinearity from other sources of nonlinearity. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring for Infrastructures)
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Article
Probabilistic Structural Model Updating with Modal Flexibility Using a Modified Firefly Algorithm
by
Zhouquan Feng
,
Wenzan Wang
and
Jiren Zhang
Materials 2022, 15(23), 8630; https://doi.org/10.3390/ma15238630 - 03 Dec 2022
Cited by 1 | Viewed by 721
Abstract
Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified [...] Read more.
Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified heuristic optimization algorithm named modified Nelder–Mead firefly algorithm (m-NMFA) was proposed to find the most probable values (MPV) of model parameters for the maximum a posteriori probability (MAP) estimate. The proposed m-NMFA was compared to the original firefly algorithm (FA), the genetic algorithm (GA), and the particle swarm algorithm (PSO) through the numerical illustrative examples of 18 benchmark functions and a twelve-story shear frame model. Then, a six-story shear frame model test was performed to identify the inter-story stiffness of the structure in the original and the damage states, respectively. By comparing the two, the position and extent of damage were accurately found and quantified in a probabilistic manner. In terms of optimization, the proposed m-NMFA was powerful to find the MPVs much faster and more accurately. In the incomplete measurement case, only the m-NMFA achieved target damage identification results. The proposed Bayesian model updating method has the advantages of high precision, fast convergence, and strong robustness in MPV finding and the ability of parameter uncertainty quantification. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring for Infrastructures)
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