Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.0
Journals
Vibration
Special Issues
Inverse Dynamics Problems, Volume II
share announcement

Inverse Dynamics Problems, Volume II

A special issue of Vibration (ISSN 2571-631X).

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 12384

Special Issue Editor

Dr. Hamed Kalhori

E-Mail Website
Guest Editor
1. School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
2. Department of Mechanical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65167-38695, Iran
Interests: structural health monitoring; inverse problems; sensors and signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The estimation of system inputs or internal reactions by direct measurements for many real systems is complicated or impossible, either because the system input is inaccessible or unknown or simply because the nature of input is unknown and therefore cannot be instrumented. An inverse problem strategy is, therefore, a promising solution for such scenarios.

An inverse problem is about identifying the cause of an effect, utilizing a set of observations and measurement of the system response. As opposed to a forward problem yielding the system response, an inverse problem manipulates the effects, considering the system’s natural behavior to predict the inputs to the system.

Inverse dynamics in particular, focusing on structural dynamics and/or inverse rigid body dynamics, calculates the applied forces or internal forces and moments from measurements of structural vibrations and/or rigid body motions. These types of problems are normally challenging as there are uncertainties that are usually amplified through the inverse process and therefore need to be properly addressed.

The objective of this Special Issue is to create a forum of discussion for research scientists and engineers working in the area of inverse structural dynamics and inverse rigid body kinematics. We invite researchers to submit both original research and review articles.

The Special Issue will cover a range of topics including but not limited to the following:

  • Impact force identification
  • Time-varying load identification
  • Moving load identification
  • Bridge-weight-in-motion systems
  • Vehicle–bridge interaction dynamics
  • Regularization in force identification
  • Uncertainties in inverse dynamics problems
  • Time-varying system identification
  • Sound source reconstruction
  • Experimental modal analysis
  • Operational modal analysis
  • Inverse dynamics with application in structural health monitoring
  • Human body and animal body inverse dynamics problems

Dr. Hamed Kalhori
Guest Editor

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. Vibration is an international peer-reviewed open access quarterly 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 1600 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

  • inverse dynamics
  • force identification
  • force reconstruction
  • bridge-weigh-in-motion
  • modal analysis
  • structural health monitoring
  • human body dynamics
  • regularization

Related Special Issue

Published Papers (3 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

13 pages, 12812 KiB  
Article
Detection of Water Leaks in Suburban Distribution Mains with Lift and Shift Vibro-Acoustic Sensors
by Lili Bykerk and Jaime Valls Miro
Vibration 2022, 5(2), 370-382; https://doi.org/10.3390/vibration5020021 - 16 Jun 2022
Cited by 3 | Viewed by 3316
Abstract
Leaks in Water Distribution Networks (WDNs) account for a large proportion of Non-Revenue Water (NRW) for utilities worldwide. Typically, a leak is only confirmed once water surfaces, allowing the leak to be traced; however, a high percentage of leaks may never surface, incurring [...] Read more.
Leaks in Water Distribution Networks (WDNs) account for a large proportion of Non-Revenue Water (NRW) for utilities worldwide. Typically, a leak is only confirmed once water surfaces, allowing the leak to be traced; however, a high percentage of leaks may never surface, incurring large water losses and costs for utilities. Active Leak Detection (ALD) methods can be used to detect hidden leaks; however, the success of such methods is highly dependent on the available detection instrumentation and the experience of the operator. To aid in the detection of both hidden and surfacing leaks, deployment of vibro-acoustic sensors is being increasingly explored by water utilities for temporary structural health monitoring. In this paper, data were collected and curated from a range of temporary Lift and Shift (L&S) vibro-acoustic sensor deployments across suburban Sydney. Time-frequency and frequency-domain features were generated to assess the performance and suitability of two state-of-the-art binary classification models for water leak detection. The results drawn from the extensive field data sets are shown to provide reliable leak detection outcomes, with accuracies of at least 97% and low false positive rates. Through the use of such a reliable leak detection system, utilities can streamline their leak detection and repair processes, effectively mitigating NRW and reducing customer disruptions. Full article
(This article belongs to the Special Issue Inverse Dynamics Problems, Volume II)
►▼ Show Figures

Figure 1

22 pages, 4812 KiB  
Article
Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response
by Md Riasat Azim and Mustafa Gül
Vibration 2021, 4(2), 422-443; https://doi.org/10.3390/vibration4020028 - 14 May 2021
Cited by 8 | Viewed by 3745
Abstract
Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to [...] Read more.
Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis. Full article
(This article belongs to the Special Issue Inverse Dynamics Problems, Volume II)
►▼ Show Figures

Figure 1

16 pages, 1401 KiB  
Article
Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning
by Jessada Sresakoolchai and Sakdirat Kaewunruen
Vibration 2021, 4(2), 341-356; https://doi.org/10.3390/vibration4020022 - 7 Apr 2021
Cited by 29 | Viewed by 4178
Abstract
Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined [...] Read more.
Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm. Full article
(This article belongs to the Special Issue Inverse Dynamics Problems, Volume II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop