Structural Assessment and Health Monitoring of Infrastructures

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 4536

Special Issue Editor


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Guest Editor
Department of Civil and Environmental Engineering, Myongji University, Yongin-si, Korea
Interests: structural health monitoring; damage detection; modal identification; bridge management systems; deterioration models; optimal sensor placements

Special Issue Information

Dear Colleagues,

The main focus of this Special Issue is on recent advances in the assessment methodologies and health monitoring techniques for civil infrastructures. Examples of those assessment methodologies include digital image correlation, ultrasound, optical fiber sensors, and traditional sensors if advanced algorithms have been developed. The accurate assessment of health state for existing structures is one of the inherent challenges.

Many algorithms have been developed to track the health state of civil infrastructure for last few decades. However, professional field engineers prefer to rely on the visual inspection to estimate the structural health state. For this reason, the specific suggestions of standardized SHM technologies and application studies on existing structures can be interesting topics for this Special Issue.

Dr. Minwoo Chang
Guest Editor

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Keywords

  • advances in sensor technology for SHM
  • accurate assessment of structural health state
  • methods for optimizing SHM procedures
  • noncontact assessment devices
  • new SHM algorithms and damage assessment techniques

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Published Papers (1 paper)

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Research

17 pages, 6273 KiB  
Article
Railway Track Loss-of-Stiffness Detection Using Bogie Filtered Displacement Data Measured on a Passing Train
by Abdollah Malekjafarian, Eugene J. OBrien, Paraic Quirke, Daniel Cantero and Fatemeh Golpayegani
Infrastructures 2021, 6(6), 93; https://doi.org/10.3390/infrastructures6060093 - 21 Jun 2021
Cited by 24 | Viewed by 3839
Abstract
This paper presents an innovative numerical framework for railway track monitoring using acceleration measurements from sensors installed on a passenger train. A numerical model including a 10 degrees of freedom train passing over a three-layer track is employed. The bogie filtered displacement (BFD) [...] Read more.
This paper presents an innovative numerical framework for railway track monitoring using acceleration measurements from sensors installed on a passenger train. A numerical model including a 10 degrees of freedom train passing over a three-layer track is employed. The bogie filtered displacement (BFD) is obtained from the bogie vertical acceleration using a numerical integration method and a band-pass filter. The BFD is compared to the filtered track longitudinal profile and can be seen to contain the main features of the track profile. This is also experimentally confirmed using field measurements where an in-service Irish Rail train was instrumented using inertial sensors. The proposed algorithm is employed to find the BFDs from the bogie accelerations. A track level survey was also undertaken to validate the measurements. It is shown that the BFDs from several passes are in good agreement with the surveyed profile. Finally, the BFDs are numerically used to find track defects such as hanging sleepers. The mean of the BFDs obtained from two populations of train passes over a healthy and a damaged track are employed to detect the loss of stiffness at the subgrade layer. The effect of the train forward speed variation and measurement noise are also investigated. Full article
(This article belongs to the Special Issue Structural Assessment and Health Monitoring of Infrastructures)
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