Special Issue "Structural Health Monitoring in Civil Infrastructure"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Dr. Yang Liu
E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: structural health monitoring of bridge and tunnel; damage diagnosis of civil structures; modal test and modal parameter identification of structures
Prof. Dr. Hongye Gou
E-Mail Website
Guest Editor
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: bridge dynamic behavior research and safety evaluation; research on vehicle-bridge coupling vibration and traffic safety; intelligent inspection (monitoring); evaluation and reinforcement of bridges
Prof. Dr. Wanshui Han
E-Mail Website
Guest Editor
School of Highway, Chang’an University, Xi’an 710064, China
Interests: static and dynamic inspection; health monitoring and evaluation of bridges; research on wind-vehicle-bridge coupling vibration and damage detection; FEM software design for structural dynamic analysis

Special Issue Information

Dear Colleagues,

The maintenance safety of civil infrastructures, such as bridges, building structures, tunnels etc., is intimately affected by natural or human-made disasters, which has attracted widespread attention. It is of great significance to monitor and forecast the performance of structures in real time, in order to improve the operational efficiency of the engineering structures. Therefore, structural health monitoring (SHM) technology has become a hot issue in recent years.

SHM refers to the use of in-situ, nondestructive sensing and analysis of structural characteristics, including structural response, for the purpose of detecting changes that may indicate damage or degradation. The process of SHM includes obtaining structural dynamic responses from a series of sensors, extracting the damage sensitive characteristic factors from these monitoring data, and performing statistical analysis on these characteristic factors to obtain the current health condition of the structure.

With the development of advanced sensing, signal processing, and damage detection methods, SHM technology has been widely implemented in pratical civil structures. Meanwile, several new concepts have been proposed in SHM, incluing smart systems and smart materials. However, there are still many economic and practical challenges in further popularization and application of SHM systems.

This Special Issue will showcase some of the latest efforts to advance the frontiers of structural health monitoring in civil infrastructure. We invite researchers to submit original research papers that include new theoretical methods, numerical modeling, and practical or experimental studies. Review articles are also welcomed.

Potential topics around structural health monitoring of civil infrastructure include but are not limited to the following:

(1) Advanced sensing technology;

(2) Design and optimization of SHM systems;

(3) Signal processing and analysis of SHM systems;

(4) Damage diagnosis of structures based on monitoring data;

(5) Life cycle condition assessment of civil structures;

(6) Life extension of civil structures using SHM.

Prof. Dr. Yang Liu
Prof. Dr. Hongye Gou
Prof. Dr. Wanshui Han
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 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. Sustainability 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 1900 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

  • civil infrastructure
  • structural health monitoring
  • advanced sensing technique
  • damage diagnosis
  • condition sessessment

Published Papers (2 papers)

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Research

Article
Method for Diagnosing the Uneven Settlement of a Rail Transit Tunnel Based on the Spatial Correlation of High-Density Strain Measurement Points
Sustainability 2021, 13(16), 9245; https://doi.org/10.3390/su13169245 - 18 Aug 2021
Viewed by 321
Abstract
Rail transit tunnels span long distances, are large-scale structures and pass through complicated geological conditions; thus, the risk of uneven settlement cannot be ignored. To address this issue, a method for diagnosing the uneven settlement of regional railway tunnels based on the spatial [...] Read more.
Rail transit tunnels span long distances, are large-scale structures and pass through complicated geological conditions; thus, the risk of uneven settlement cannot be ignored. To address this issue, a method for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed in this study. First, with the distributed optical fiber sensing technology, a method for determining the intervals of strain measurement points with strong spatial correlations is proposed based on a support vector machine. Second, combined with the statistical analysis of the influence range of the uneven settlement of a tunnel, an algorithm for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual tunnel data. Full article
(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)
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Article
Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data
Sustainability 2021, 13(15), 8631; https://doi.org/10.3390/su13158631 - 02 Aug 2021
Viewed by 428
Abstract
As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we [...] Read more.
As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we focus on this problem and introduce a novel method based on the bridge structural health monitoring data. The method was based on the fact that the tails of the probability distribution governed the behavior of extreme values. The generalized Pareto distribution was applied to model the tail distribution of traffic load effect using the peak-over-threshold method, while the filtered Poisson process was used to model the traffic load effect stochastic process. The parameters of the extreme value distribution of traffic load effect during a service period could be determined by theoretical derivation if the parameters of tail distribution were estimated. Moreover, Bayes’ theorem was applied to update the distribution model to reduce the statistical uncertainty. Finally, the rationality of the proposed method was applied to analyze the monitoring data of concrete-filled steel tube arch bridge suspenders. The results proved that the approach was convenient and found that the extreme value distribution type III might be more suitable as the traffic load effect probability model. Full article
(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)
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