Special Issue "Novel Approaches for Structural Health Monitoring"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editor

Prof. Cecilia Surace
E-Mail Website
Guest Editor
Department of Structural Engineering, Construction and Soil Mechanics, Politecnico di Torino, 10129 Turin, Italy
Interests: structural health monitoring; machine learning; nonlinear dynamics; signal processing; structural engineering; vibration analysis; structural dynamics

Special Issue Information

Dear Colleagues,

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Unmodeled nonlinearities, ineffective sensor placement, and the effects of confounding influences due to operational and environmental variability still harm the effectiveness of state-of-art SHM apparatuses. Unprecedented conditions such as hypersonic flight, stricter safety requirements, and ageing civil infrastructure pose new challenges for confrontation. Therefore, the aim of this Special Issue is to gather the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring. Studies concerning nondestructive testing, machine learning, signal processing, sensor fusion, vibration-based techniques, and related fields are all welcome, both numerical and experimental.

Prof. Cecilia Surace
Guest Editor

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 papers will be 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. Applied Sciences 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 1800 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
  • nondestructive testing
  • machine learning
  • vibration-based
  • signal processing

Published Papers (2 papers)

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Research

Open AccessArticle
Railway Wheel Flat Recognition and Precise Positioning Method Based on Multisensor Arrays
Appl. Sci. 2020, 10(4), 1297; https://doi.org/10.3390/app10041297 - 14 Feb 2020
Abstract
Wheel flats have become a major problem affecting the long-term service of railway systems. Wheels with flats create intermittent impact loads to trains and rails. This not only accelerates the deterioration of vehicle and track components but also leads to abnormal wheel-rail contact [...] Read more.
Wheel flats have become a major problem affecting the long-term service of railway systems. Wheels with flats create intermittent impact loads to trains and rails. This not only accelerates the deterioration of vehicle and track components but also leads to abnormal wheel-rail contact conditions. An effective method for detecting wheel conditions is urgently needed to ensure the operation of the railway and provide guidance for the repair of wheels. However, most previous researches have used qualitative detection methods, and hence have been unable to achieve accurate positioning of the wheel flats. In addition, the theoretical basis for the layout scheme for wheel flat detection sensors is lacking, making it impossible to meet the needs of field applications. In this study, we simulated the spatial distribution characteristics of rail strain, under different wheel flat conditions, and based on this, a layout scheme of multisensor arrays was proposed which more effectively captured the responses of the wheel flats. A wheel flat recognition and precise positioning method based on multisensor fusion was designed. The algorithm was validated through the combination of experimental and simulation methods. The result shows that the algorithm can ideally detect and locate the wheel flats under complex conditions. Full article
(This article belongs to the Special Issue Novel Approaches for Structural Health Monitoring)
Open AccessArticle
Monitoring and Analysis of Dynamic Characteristics of Super High-rise Buildings using GB-RAR: A Case Study of the WGC under Construction, China
Appl. Sci. 2020, 10(3), 808; https://doi.org/10.3390/app10030808 - 23 Jan 2020
Abstract
Accurate dynamic characteristics of super high-rise buildings serve as a guide in their construction and operation. Ground-based real aperture radar (GB-RAR) techniques have been applied in monitoring and analyzing the dynamic characteristics of different buildings, but only few studies have utilized them to [...] Read more.
Accurate dynamic characteristics of super high-rise buildings serve as a guide in their construction and operation. Ground-based real aperture radar (GB-RAR) techniques have been applied in monitoring and analyzing the dynamic characteristics of different buildings, but only few studies have utilized them to derive the dynamic characteristics of super high-rise buildings, especially those higher than 400 m and under construction. In this study, we proposed a set of technical methods for monitoring and analyzing the dynamic characteristics of super high-rise buildings based on GB-RAR and wavelet analysis. A case study was conducted on the monitoring and analysis of the dynamic characteristics of the Wuhan Greenland Center (WGC) under construction (5–7 July 2017) with a 636 m design height. Displacement time series was accurately derived through GB-RAR and wavelet analysis, and the accuracy reached the submillimeter level. The maximum horizontal displacement amplitudes at the top of the building in the north–south and east–west directions were 18.84 and 15.94 mm, respectively. The roof displacement trajectory of the WGC was clearly identified. A certain negative correlation between the temperature and displacement changes at the roof of the building was identified. Study results demonstrate that the proposed method is effective for the dynamic monitoring and analysis of super high-rise buildings with noninvasive and nondestructive characteristics. Full article
(This article belongs to the Special Issue Novel Approaches for Structural Health Monitoring)
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