Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Advances in Structure Health Monitoring: Wave/Vibration-Based Techniques and Smart Materials
share announcement

Advances in Structure Health Monitoring: Wave/Vibration-Based Techniques and Smart Materials

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 4903

Special Issue Editors

Dr. Hossein Bisheh

E-Mail Website
Guest Editor
School of Engineering and Built Environment, Anglia Ruskin University, Chelmsford CM1 1SQ, UK
Interests: structural dynamics; structural health monitoring; smart materials; composite materials and structures; computational mechanics; biomechanics
Dr. Yen-Fang Su

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: infrastructure sensing; smart materials; artificial intelligence
Dr. Nan Wu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: solid mechanics; mechanical vibration; smart materials and structures; energy harvesting; structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring the integrity and health of structures is very important to prevent catastrophic damage and structural failure. Hence, development and advances in structural health monitoring techniques based on structural vibration and wave propagation, as a nondestructive method, and design of smart materials integrated with sensors/actuators play a significant role in accurately detecting damage, predicting remaining life, and preventing the failure of structures.

The aim of modern wave/vibration-based structural health monitoring using smart materials is to remotely detect any damage or defects and estimate the remaining life of structures before failure.

This Special Issue will be dedicated to new approaches in structural health monitoring of mechanical, civil, aeronautical, electrical, and other systems by development of smart materials and wave/vibration-based techniques.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of: 

  • Wave propagation methods for damage assessment;
  • Vibration techniques for damage detection;
  • Use of sensors and smart materials;
  • Predicting the remaining life of structures;
  • Piezoelectric materials for diagnostics;
  • Signal processing techniques for monitoring.

Dr. Hossein Bisheh
Dr. Yen-Fang Su
Dr. Nan Wu
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. Applied Sciences 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 2400 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

  • wave propagation
  • vibration
  • structural health monitoring
  • smart materials
  • sensors and actuators
  • signal processing
  • damage detection
  • piezoelectric materials

Published Papers (4 papers)

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

Research

24 pages, 8453 KiB  
Article
Modal Parameter Identification of a Structure Under Earthquake via a Wavelet-Based Subspace Approach
by Wei-Chih Su, Liane-Jye Chen and Chiung-Shiann Huang
Appl. Sci. 2024, 14(6), 2503; https://doi.org/10.3390/app14062503 - 15 Mar 2024
Viewed by 377
Abstract
This paper introduces a novel wavelet-based methodology for identifying the modal parameters of a structure in the aftermath of an earthquake. Our proposed approach seamlessly combines a subspace method with a stationary wavelet packet transform. By relocating the subspace method into the wavelet [...] Read more.
This paper introduces a novel wavelet-based methodology for identifying the modal parameters of a structure in the aftermath of an earthquake. Our proposed approach seamlessly combines a subspace method with a stationary wavelet packet transform. By relocating the subspace method into the wavelet domain and introducing a weighting function, complemented by a moving window technique, the efficiency of our approach is significantly augmented. This enhancement ensures the precise identification of the time-varying modal parameters of a structure. The capacity of the stationary wavelet packet transform for rich signal decomposition and exceptional time-frequency localization is harnessed in our approach. Different subspaces within the stationary wavelet packet transform encapsulate signals with distinct frequency sub-bands, leveraging the fine filtering property to not only discern modes with pronounced modal interference, but also identify numerous modes from the responses of a limited number of measured degrees of freedom. To validate our methodology, we processed numerically simulated responses of both time-invariant and time-varying six-floor shear buildings, accounting for noise and incomplete measurements. Additionally, our approach was applied to the seismic responses of a cable-stayed bridge and the nonlinear responses of a five-story steel frame during a shaking table test. The identified modal parameters were meticulously compared with published results, underscoring the applicability and reliability of our approach for processing real measured data. Full article
(This article belongs to the Special Issue Advances in Structure Health Monitoring: Wave/Vibration-Based Techniques and Smart Materials)
►▼ Show Figures

Figure 1

22 pages, 4905 KiB  
Article
Investigation of Modal Identification of Frame Structures Using Blind Source Separation Technique Based on Vibration Data
by Fanhao Meng, Yong Ma, Yongjun Xia, Yimin Ma and Ming Jiang
Appl. Sci. 2023, 13(12), 7249; https://doi.org/10.3390/app13127249 - 17 Jun 2023
Cited by 1 | Viewed by 915
Abstract
This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) [...] Read more.
This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) and generalized eigen decomposition method (GED), based on modern signal processing technology. Here, the frequency response function (FRF) method is introduced as an important reference to verify the effectiveness of the CP algorithm and GED algorithm. The effectiveness and accuracy of both types of algorithms are verified by numerical simulations and experiments on a suspension bridge. In addition, an engineering application of these two BSS methods is successfully implemented in an overhead transmission line-crossing frame. The results show that the two novel BSS learning rules (CP and GED) are capable of successfully identifying modal parameters of the civil structure under ambient excitation. Full article
(This article belongs to the Special Issue Advances in Structure Health Monitoring: Wave/Vibration-Based Techniques and Smart Materials)
►▼ Show Figures

Figure 1

37 pages, 14080 KiB  
Article
Numerical Verification of the Drive-By Monitoring Method for Identifying Vehicle and Bridge Mechanical Parameters
by Kyosuke Yamamoto, Ryota Shin and Eugene Mudahemuka
Appl. Sci. 2023, 13(5), 3049; https://doi.org/10.3390/app13053049 - 27 Feb 2023
Cited by 2 | Viewed by 1741
Abstract
The PRE (numerical simulation-based vehicle and bridge parameter and road roughness estimation) method uses vehicle vibration data to identify the vehicle’s and bridge’s mechanical parameters and estimate road unevenness simultaneously. This method randomly assumes the mechanical parameters first. Secondly, it solves the vehicle’s [...] Read more.
The PRE (numerical simulation-based vehicle and bridge parameter and road roughness estimation) method uses vehicle vibration data to identify the vehicle’s and bridge’s mechanical parameters and estimate road unevenness simultaneously. This method randomly assumes the mechanical parameters first. Secondly, it solves the vehicle’s IEP (input estimation problem) and the bridge’s DRS (dynamic response simulation) from the vehicle vibration data to obtain road profiles of the front and rear wheels. Repeat the random assumption of the mechanical parameters to minimize the residual between the obtained road unevenness because the road unevenness of the front and rear wheels are expected to match. To search for a better combination of the mechanical parameters, the MCMC (Monte Carlo Markov chain) algorithm is adopted in this paper. This paper also numerically simulates vehicle vibration data for the cases of the reduced-stiffness bridge model and examines whether this method can identify the position, range, and magnitude of stiffness reduction. The numerical simulation results show that bridge-stiffness reduction can be estimated reasonably. Full article
(This article belongs to the Special Issue Advances in Structure Health Monitoring: Wave/Vibration-Based Techniques and Smart Materials)
►▼ Show Figures

Figure 1

13 pages, 4662 KiB  
Article
Data-Driven Damage Classification Using Guided Waves in Pipe Structures
by Xin Zhang, Wensong Zhou, Hui Li and Yuxiang Zhang
Appl. Sci. 2022, 12(21), 10874; https://doi.org/10.3390/app122110874 - 26 Oct 2022
Cited by 2 | Viewed by 1190
Abstract
Damage types are important for structural condition assessment, however, for conventionally guided wave-based inspections, the characteristics extracted from the guided wave packets are usually used to detect, locate and quantify the damages, but not classify them. In this work, the data-driven method is [...] Read more.
Damage types are important for structural condition assessment, however, for conventionally guided wave-based inspections, the characteristics extracted from the guided wave packets are usually used to detect, locate and quantify the damages, but not classify them. In this work, the data-driven method is proposed to classify the common damages in the pipe utilizing the guided wave signals obtained from numerous damage detection tests. The fundamental torsional mode T(0,1) is selected to conduct the guided wave-based damage detection to reduce the complexity of signal processing for its almost non-dispersive property. A total of 520 groups of experimental data under different degrees of damage were obtained to verify the proposed method. Finally, with help of a deep neural network (DNN) algorithm, all response data from the damages in the pipes were all clearly classified with quite high probability. Full article
(This article belongs to the Special Issue Advances in Structure Health Monitoring: Wave/Vibration-Based Techniques and Smart Materials)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop