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Interdisciplinary Research and Practice in Structural Health Monitoring and Condition...
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Interdisciplinary Research and Practice in Structural Health Monitoring and Condition Assessment of Civil Infrastructures

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15311

Special Issue Editors

Dr. Chuanzhi Dong

E-Mail Website
Guest Editor
Palo Alto Research Center, Palo Alto, CA 94304, USA
Interests: structural identification; structural health monitoring; computer vision; fiber optical sensor; machine learning
Prof. Dr. Bin Chen

E-Mail Website
Guest Editor
Zhejiang University City College, Hangzhou 310015, Zhejiang, China
Interests: weigh-in-motion; load identification and modelling; fiber optical sensor; bridge inspection; fatigue evaluation
Prof. Dr. Yizhou Zhuang

E-Mail Website
Guest Editor
College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: fiber optical sensor; bridge condition assessment; fatigue and reliability evaluation; load rating

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is the process of tracking the operational status, assessing the condition, and detecting the damage or change of various types of structures. Over the last three decades, SHM has played significant roles in the assessment, management, and maintenance of structures to assist assets’ owners for decision making. With the interdisciplinary nature of this area, SHM has made great achievements and achieved tremendous benefits from the developments of technologies from other areas, such as smart sensing, advanced analytics methods, computer vision, artificial intelligent algorithms, the Internet of Things (IoT), machine learning/deep learning, physical or data-driven modeling, virtual reality (VR), augmented reality (AR) and mixed reality (MR), etc. The aim of this Special Issue is to provide a platform for scientists, engineers, and industrial practitioners to present their latest interdisciplinary research and practice in structural health monitoring and condition assessment of civil infrastructures. High-quality research articles and reviews are welcome. Papers are solicited in but not limited to the following and related topics:

  • Smart sensing technologies for SHM of civil infrastructures;
  • Applications of computer vision in structural health monitoring and condition assessment;
  • Advances of emerging technologies such as robotics, UAV, IoT, VR, AR, and MR for SHM;
  • Artificial intelligence algorithms for SHM;
  • Machine learning/deep learning for SHM data analytics;
  • Physical or data-driven, or hybrid modeling;
  • Nondestructive testing (NDT) and nondestructive evaluation (NDE) for civil infrastructures;
  • Intelligent infrastructures.

Dr. Chuanzhi Dong
Prof. Dr. Bin Chen
Prof. Dr. Yizhou Zhuang 
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.

Published Papers (4 papers)

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Research

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13 pages, 2311 KiB  
Article
Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter
by Ning Yang, Jun Li, Mingqiang Xu and Shuqing Wang
Sensors 2022, 22(11), 4212; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114212 - 31 May 2022
Cited by 5 | Viewed by 1626
Abstract
The real-time identification of time-varying cable force is critical for accurately evaluating the fatigue damage of cables and assessing the safety condition of bridges. In the context of unknown wind excitations and only one available accelerometer, this paper proposes a novel cable force [...] Read more.
The real-time identification of time-varying cable force is critical for accurately evaluating the fatigue damage of cables and assessing the safety condition of bridges. In the context of unknown wind excitations and only one available accelerometer, this paper proposes a novel cable force identification method based on an improved adaptive extended Kalman filter (IAEKF). Firstly, the governing equation of the stay cable motion, which includes the cable force variation coefficient, is expressed in the modal domain. It is transformed into a state equation by defining an augmented Kalman state vector with the cable force variation coefficient concerned. The cable force variation coefficient is then recursively estimated and closely tracked in real time by the proposed IAEKF. The contribution of this paper is that an updated fading-factor matrix is considered in the IAEKF, and the adaptive noise error covariance matrices are determined via an optimization procedure rather than by experience. The effectiveness of the proposed method is demonstrated by the numerical model of a real-world cable-supported bridge and an experimental scaled steel stay cable. Results indicate that the proposed method can identify the time-varying cable force in real time when the cable acceleration of only one measurement point is available. Full article
(This article belongs to the Special Issue Interdisciplinary Research and Practice in Structural Health Monitoring and Condition Assessment of Civil Infrastructures)
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19 pages, 3641 KiB  
Article
Structural Damage Identification Based on Transmissibility in Time Domain
by Yunfeng Zou, Xuandong Lu, Jinsong Yang, Tiantian Wang and Xuhui He
Sensors 2022, 22(1), 393; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010393 - 05 Jan 2022
Cited by 5 | Viewed by 2066
Abstract
Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novel structural damage identification method based on transmissibility in the [...] Read more.
Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novel structural damage identification method based on transmissibility in the time domain is proposed. The method takes the discrepancy of transmissibility of structure response in the time domain before and after damage as the basis of finite element model updating. The damage is located and quantified through iteration by minimizing the difference between the measurements at gauge locations and the reconstruction response extrapolated by the finite element model. Taking advantage of the response reconstruction method based on empirical mode decomposition, damage information can be obtained in the absence of prior knowledge on excitation. Moreover, this method directly collects time-domain data for identification without modal identification and frequent time–frequency conversion, which can greatly improve efficiency on the premise of ensuring accuracy. A numerical example is used to demonstrate the overall damage identification method, and the study of measurement noise shows that the method has strong robustness. Finally, the present work investigates the method through a simply supported overhanging beam. The experiments collect the vibration strain signals of the beam via resistance strain gauges. The comparison between identification results and theoretical values shows the effectiveness and accuracy of the method. Full article
(This article belongs to the Special Issue Interdisciplinary Research and Practice in Structural Health Monitoring and Condition Assessment of Civil Infrastructures)
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Review

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33 pages, 10859 KiB  
Review
Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review
by Fikret Necati Catbas, Furkan Luleci, Mahta Zakaria, Ulas Bagci, Joseph J. LaViola, Jr., Carolina Cruz-Neira and Dirk Reiners
Sensors 2022, 22(23), 9560; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239560 - 06 Dec 2022
Cited by 16 | Viewed by 5909
Abstract
Condition assessment of civil engineering structures has been an active research area due to growing concerns over the safety of aged as well as new civil structures. Utilization of emerging immersive visualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed [...] Read more.
Condition assessment of civil engineering structures has been an active research area due to growing concerns over the safety of aged as well as new civil structures. Utilization of emerging immersive visualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in the architectural, engineering, and construction (AEC) industry has demonstrated that these visualization tools can be paradigm-shifting. Extended Reality (XR), an umbrella term for VR, AR, and MR technologies, has found many diverse use cases in the AEC industry. Despite this exciting trend, there is no review study on the usage of XR technologies for the condition assessment of civil structures. Thus, the present paper aims to fill this gap by presenting a literature review encompassing the utilization of XR technologies for the condition assessment of civil structures. This study aims to provide essential information and guidelines for practitioners and researchers on using XR technologies to maintain the integrity and safety of civil structures. Full article
(This article belongs to the Special Issue Interdisciplinary Research and Practice in Structural Health Monitoring and Condition Assessment of Civil Infrastructures)
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26 pages, 1685 KiB  
Review
A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments
by Yizhou Zhuang, Weimin Chen, Tao Jin, Bin Chen, He Zhang and Wen Zhang
Sensors 2022, 22(10), 3789; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103789 - 16 May 2022
Cited by 22 | Viewed by 4404
Abstract
Computer vision-based structural deformation monitoring techniques were studied in a large number of applications in the field of structural health monitoring (SHM). Numerous laboratory tests and short-term field applications contributed to the formation of the basic framework of computer vision deformation monitoring systems [...] Read more.
Computer vision-based structural deformation monitoring techniques were studied in a large number of applications in the field of structural health monitoring (SHM). Numerous laboratory tests and short-term field applications contributed to the formation of the basic framework of computer vision deformation monitoring systems towards developing long-term stable monitoring in field environments. The major contribution of this paper was to analyze the influence mechanism of the measuring accuracy of computer vision deformation monitoring systems from two perspectives, the physical impact, and target tracking algorithm impact, and provide the existing solutions. Physical impact included the hardware impact and the environmental impact, while the target tracking algorithm impact included image preprocessing, measurement efficiency and accuracy. The applicability and limitations of computer vision monitoring algorithms were summarized. Full article
(This article belongs to the Special Issue Interdisciplinary Research and Practice in Structural Health Monitoring and Condition Assessment of Civil Infrastructures)
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