Structural Health Monitoring and Vibration Control

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 4877

Special Issue Editors


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Guest Editor
Department of Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
Interests: stochastic optimal control; random system identification; optimal parameter estimation; bayesian inference; stochastic system dynamics; machine learning

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Guest Editor
Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: structural health monitoring; bayesian inference and machine learning

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Guest Editor
Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: vehicle-bridge coupling interaction; structural health monitoring and control of maglev system; suspension control

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Guest Editor
Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: structural health monitoring; structural dynamics and control; smart materials and structures; sensors and actuators; bayesian inference and machine learning; high-speed rail and maglev safety
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Special Issue Information

Dear Colleagues,

Structural health monitoring and vibration control are two important research subjects that are expanding in the engineering and theoretical fields. With the development of estimation methods, diagnosis technology, control strategies, optimization algorithms, multi-physics sensing, and actuating technology, etc., vibration-based structural health monitoring and structural vibration control, especially for uncertain systems under random excitations due to the application of artificial intelligence technology, have been experiencing tremendous progress. Buildings are an important class of engineered structures, which generally have uncertain parameters and are subjected to random excitations due to their structural complexity and environmental uncertainty. Accordingly, research focusing on the technology and theory of health monitoring and vibration control related to built structures will lead to their advancement. This Special Issue focuses on structural health monitoring and vibration control for various extensive engineering and theoretical problems using various estimation, control, optimization, and intelligence technologies, including estimation and identification methods, control strategies and methods, sensing and actuating technology, application analysis and experiments, data processing, machine learning and inference, neural network representation, etc. This issue will bring together and share recent relevant research, aiming to enable extensive development in this area. The potential topics for this issue include, but are not limited to, the following: 

  • Structural health monitoring;
  • Estimation method and applications;
  • Vibration control;
  • Control method and applications;
  • Sensor and actuator technology and applications;
  • Data processing technology and applications;
  • Machine learning and inference and applications;
  • Neural network algorithm and applications;
  • Smart structural dynamics.

Prof. Dr. Zuguang Ying
Dr. Youwu Wang
Dr. Sumei Wang
Prof. Dr. Yi-Qing Ni
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. Buildings is an international peer-reviewed open access monthly 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
  • parameter estimation
  • vibration control
  • data processing
  • machine learning
  • neural network
  • smart structural dynamics
  • stochastic vibration

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Published Papers (4 papers)

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Research

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20 pages, 8425 KiB  
Article
An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection
by Junfang Wang, Xiangxiong Li, Huike Zeng, Jianfu Lin, Shiming Xue, Jing Wang and Yanfeng Zhou
Buildings 2024, 14(9), 2657; https://doi.org/10.3390/buildings14092657 - 27 Aug 2024
Viewed by 655
Abstract
Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve [...] Read more.
Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)
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23 pages, 4283 KiB  
Article
Stochastic Optimal Bounded Parametric Control of Periodic Viscoelastomer Sandwich Plate with Supported Mass Based on Dynamical Programming Principle
by Zhi-Gang Ruan, Zu-Guang Ying, Zhao-Zhong Ying, Hua Lei, Wen Wang and Lei Xia
Buildings 2024, 14(8), 2309; https://doi.org/10.3390/buildings14082309 - 25 Jul 2024
Viewed by 636
Abstract
The sandwich plate (SP) with supported mass can model structural systems such as platform or floor with installed vibration-sensitive apparatus under random loading. The stochastic optimal control (in time domain) of periodic (in space) viscoelastomer (VE) SP with supported mass subjected to random [...] Read more.
The sandwich plate (SP) with supported mass can model structural systems such as platform or floor with installed vibration-sensitive apparatus under random loading. The stochastic optimal control (in time domain) of periodic (in space) viscoelastomer (VE) SP with supported mass subjected to random excitation is an important research subject, which can fully use VE controllability, but it is a challenging problem on optimal bounded parametric control (OBPC). In this paper, a stochastic OBPC for periodic VESP with supported mass subjected to random base loading is proposed according to the stochastic dynamical programming (SDP) principle. Response-reduction capability using the proposed OBPC is studied to demonstrate further control effectiveness of periodic SP via SDP. Controllable VE core modulus of SP is distributed periodically in space. Differential equations for coupling vibration of periodic SP with supported mass are derived and transformed into multi-dimensional system equations with parameters as nonlinear functions of bounded control. The OBPC problem is established by the system equations and performance index with bound constraint. Then, an SDP equation is derived according to the SDP principle. The OBPC law is obtained from the SDP equation under bound constraint. Optimally controlled responses are calculated and compared with passively controlled responses to evaluate control effectiveness. Numerical results on responses and statistics of SP via the proposed OBPC show further remarkable control effectiveness. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)
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15 pages, 3566 KiB  
Article
Stochastic Response of Composite Post Insulators under Seismic Excitation
by Haibo Wang, Yongfeng Cheng, Zhicheng Lu, Ronghua Huan, Qiangfeng Lü and Zhenlin Liu
Buildings 2024, 14(6), 1539; https://doi.org/10.3390/buildings14061539 - 25 May 2024
Cited by 1 | Viewed by 632
Abstract
Composite post insulators are crucial facilities in substations and are prone to significant damage during seismic disasters. However, existing research lacks seismic motion models suitable for power facilities and rarely involves stochastic models. Furthermore, considering the non-stationary characteristics of seismic motion, predicting the [...] Read more.
Composite post insulators are crucial facilities in substations and are prone to significant damage during seismic disasters. However, existing research lacks seismic motion models suitable for power facilities and rarely involves stochastic models. Furthermore, considering the non-stationary characteristics of seismic motion, predicting the response of nonlinear systems under non-stationary excitation becomes exceedingly challenging. In view of this, the stochastic response of composite post insulators under the non-stationary stochastic seismic excitation appropriate for power facilities has been studied. First, a stochastic ground motion model, conforming to the Code for Seismic Design of Electrical Insulators in China, is established, incorporating amplitude and frequency non-stationarity. Next, the nonlinear dynamic system, accounting for multi-section composite post insulators and the nonlinearity of flange connections, is established under stochastic ground motion conditions. Based on this stochastic nonlinear dynamic model, the dynamic behavior of the system was analyzed using the stochastic dynamics method (the wavelet-Galerkin method), and the influence of nonlinear stiffness on the system response was discussed. The stochastic seismic response analysis method proposed in this paper can serve as a valuable reference for the seismic design of pillar-type electrical equipment. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)
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Review

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36 pages, 4790 KiB  
Review
A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems
by Qi Zhu, Su-Mei Wang and Yi-Qing Ni
Buildings 2024, 14(3), 837; https://doi.org/10.3390/buildings14030837 - 20 Mar 2024
Cited by 3 | Viewed by 2443
Abstract
Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation [...] Read more.
Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)
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