Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.9
Journals
Sensors
Special Issues
Structural Health Monitoring Based on Sensing Technologies in Bridge Structures
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Structural Health Monitoring Based on Sensing Technologies in Bridge Structures

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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 5046

Special Issue Editors

Dr. Furui Wang

E-Mail Website
Guest Editor
National Key Laboratory of Science and Technology on Helicopter Transmission, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: structural health monitoring; prognosis and health management; smart materials and structures
Prof. Dr. Shuang Hou

E-Mail Website
Guest Editor
Engineering and Transportation, South China Univ. of Technology, Guangzhou 510640, China
Interests: structural health monitoring; bridge structures; concrete structures

Special Issue Information

Dear Colleagues,

As a typical transportation infrastructure, the bridge is widely used, and it is subjected to severe working conditions, including storms, snow, solarization, etc. These poor environmental issues will downgrade the construction materials (e.g., steel and concrete) of bridge structures. On the other hand, the aging or deterioration process of a bridge structure will be expedited by several physical factors, such as over-traffic and impact loading. Therefore, it is necessary to employ effective structural health monitoring approaches (particularly sensing technologies) to detect or provide a real-time assessment of health status of the bridge structure. 

This Special Issue will focus on the health monitoring of bridge structures, and the topics of interest for this Special Issue include, but are not limited to, the following:

  • SHM of bridge structures via vibration signals
  • SHM of bridge structures via strain signals (strain gauges or fiber optics sensors)
  • SHM of bridge structures via ultrasonic signals (e.g., guided waves)
  • SHM of bridge structures via acoustic emission signals
  • Implementation of sensors (network, wireless, etc.) for the SHM of bridge structures
  • Advanced signal processing strategies regarding the above different types of signals

The submission of both review papers and original research articles is highly welcomed. We hope that this collection of high-quality works in the structural health monitoring of bridge structures will serve as an inspiration for future research in the field.

Dr. Furui Wang
Prof. Dr. Shuang Hou
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
  • sensing technologies
  • bridge structures

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Review

29 pages, 1495 KiB  
Review
Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review
by Omar S. Sonbul and Muhammad Rashid
Sensors 2023, 23(9), 4230; https://doi.org/10.3390/s23094230 - 24 Apr 2023
Cited by 10 | Viewed by 4649
Abstract
Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing [...] Read more.
Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements. Full article
(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technologies in Bridge Structures)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop