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
State-of-the-Art Structural Health Monitoring in Civil Engineering
share announcement

State-of-the-Art Structural Health Monitoring in Civil Engineering

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 19438

Special Issue Editors

Dr. Raffaele Zinno

E-Mail Website
Guest Editor
Department of Environmental Engineering, University of Calabria, Via P. Bucci Cubo 44A, 87036 Rende, Italy
Interests: structural health monitoring (SHM); self-monitoring materials and structures; computational mechanics; civil infrastructures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eugene J. OBrien

E-Mail Website
Guest Editor
School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: bridge health monitoring and assessments; weigh-in-motion; sensor-based monitoring; structural dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past, when structures contained elements which were perishable over time (e.g., wood), the maintenance of houses, bridges, etc. was considered of vital importance to be able to use them safely and preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary element. However, since it was realized that even for works made with these materials the useful life has an end, and that we are approaching it, planning maintenance has become an important and non-negligible aspect. Thus was born the concept of structural health monitoring (SHM), and to keep civil engineering works under constant control, multidisciplinary methods have been designed and implemented. In fact, computational mechanics, the static and dynamic analysis of structures, electronics, sensors, and recently also the Internet of Things (IoT) and artificial intelligence (AI) come into play. However, it is also important to consider new materials—especially those with intrinsic characteristics of self-diagnosis, just as it is important to make use of measurement and survey methods typical of modern geomatics, which also makes use of satellite surveys and uses highly sophisticated laser tools. We propose this Special Issue to investigate all these issues.

As it is clear that SHM is multidisciplinary, the Topic Editors request that the scientific community contribute to this Special Issue, discussing the innovations relating to the methods and materials that allow us to evaluate the health status of a structure or infrastructure, limited to those of civil use.

Prof. Dr. Raffaele Zinno
Prof. Dr. Eugene J. OBrien
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

  • computational mechanics in SHM
  • theoretical analysis of structures
  • new materials and innovative techniques in SHM
  • electronics and sensors
  • Artificial Intelligence

Published Papers (8 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:

Editorial

Jump to: Research, Review

3 pages, 180 KiB  
Editorial
State-of-the-Art Structural Health Monitoring in Civil Engineering
by Raffaele Zinno and Eugene J. OBrien
Appl. Sci. 2023, 13(21), 11609; https://0-doi-org.brum.beds.ac.uk/10.3390/app132111609 - 24 Oct 2023
Viewed by 1021
Abstract
In the past, when structures contained elements which were prone to deterioration over time [...] Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)

Research

Jump to: Editorial, Review

35 pages, 4941 KiB  
Article
A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring
by Yves Reuland, Panagiotis Martakis and Eleni Chatzi
Appl. Sci. 2023, 13(4), 2708; https://0-doi-org.brum.beds.ac.uk/10.3390/app13042708 - 20 Feb 2023
Cited by 6 | Viewed by 1992
Abstract
Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety [...] Read more.
Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

18 pages, 5583 KiB  
Article
A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
by Chengjun Tan, Hua Zhao, Nasim Uddin and Banfu Yan
Appl. Sci. 2022, 12(21), 11277; https://0-doi-org.brum.beds.ac.uk/10.3390/app122111277 - 07 Nov 2022
Cited by 4 | Viewed by 1203
Abstract
Recently, the ‘drive-by’ or vehicle scanning technique has attracted increasing attention over the last decade for the purpose of bridge health monitoring. The feasibility of this technique has been demonstrated by many field tests. In comparison to conventional bridge SHM, the concept of [...] Read more.
Recently, the ‘drive-by’ or vehicle scanning technique has attracted increasing attention over the last decade for the purpose of bridge health monitoring. The feasibility of this technique has been demonstrated by many field tests. In comparison to conventional bridge SHM, the concept of the drive-by bridge technique shows many advantages in terms of efficiency, economy, convenience, and mobility. It has been verified that wavelet transforms can successfully identify bridge damage and its location using the responses of a moving vehicle. However, the validity of this method is challenged by road roughness. This paper proposes a wavelet-based approach to detect bridge defects using wavelet energy. In addition, a damage index based on component wavelet energy is developed to localize the damage. A numerical simulation is modeled to verify the feasibility of the proposed approach, and the result shows that the proposed approach performs well even when considering road roughness in the vehicle and bridge interaction. Moreover, the effects of road surface profile, vehicle velocity, vehicle mass, noise signal, and different damage severity on the proposed approach are investigated. The proposed approach shows a great potential application in bridge health monitoring using indirect measurements from a moving vehicle. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

24 pages, 3858 KiB  
Article
Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
by Yifei Ren, Eugene J. OBrien, Daniel Cantero and Jennifer Keenahan
Appl. Sci. 2022, 12(10), 4972; https://0-doi-org.brum.beds.ac.uk/10.3390/app12104972 - 14 May 2022
Cited by 6 | Viewed by 2014
Abstract
This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-β integration scheme on data from a batch of trains. In a [...] Read more.
This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-β integration scheme on data from a batch of trains. In a self-calibration process, an optimization algorithm is used to find vehicle dynamic properties and speed. For bridge health monitoring, the apparent profile of the bridge is first determined, i.e., the true profile plus components of ballast and bridge deflection under the moving train. The apparent profile is used, in turn, to calculate the moving reference deflection influence line, i.e., the deflection due to a moving (static) unit load. The moving reference influence line is shown to be a good indicator of bridge stiffness. This numerical approach is assessed using an elaborate finite element model operated by an independent research group. The results show that the moving reference influence line can be found accurately and that it constitutes an effective indicator of the condition of a bridge. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

17 pages, 2316 KiB  
Article
Tracking of Stiffness Variation in Structural Members Using Input Error Function Observers
by Prasad Dharap and Satish Nagarajaiah
Appl. Sci. 2021, 11(24), 11857; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411857 - 14 Dec 2021
Viewed by 1890
Abstract
This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system identification techniques. In practice, mathematical models used during [...] Read more.
This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system identification techniques. In practice, mathematical models used during numerical simulations differ from the actual status of the structure, and thus, accurate mathematical models are rarely available for reference. Noise is an unwanted signal in the input–output measurements but unavoidable in real-world applications (as in long span bridge trusses) and hard to imitate during numerical simulations. Simulation data from the truss system clearly indicates the effectiveness of the proposed structural damage detection method for estimating the severity of the damage. Optimization of the input error function can further automate the stiffness estimation in structural members and address critical aspects such as system uncertainties and the presence of noise in input–output measurements. Stiffness tracking in one of the planar truss members indicates the potential of optimization of the input error function for online structural health monitoring and implementing condition-based maintenance. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

17 pages, 2746 KiB  
Article
A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage
by Shuo Wang, Eugene J. OBrien and Daniel P. McCrum
Appl. Sci. 2021, 11(16), 7271; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167271 - 07 Aug 2021
Cited by 9 | Viewed by 2150
Abstract
This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge [...] Read more.
This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Review

Jump to: Editorial, Research

23 pages, 2383 KiB  
Review
The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges
by Raffaele Zinno, Sina Shaffiee Haghshenas, Giuseppe Guido, Kaveh Rashvand, Alessandro Vitale and Ali Sarhadi
Appl. Sci. 2023, 13(1), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010097 - 21 Dec 2022
Cited by 19 | Viewed by 3886
Abstract
The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology [...] Read more.
The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

22 pages, 3088 KiB  
Review
Review of Development and Application of Digital Image Correlation Method for Study of Stress–Strain State of RC Structures
by Yaroslav Blikharskyy, Nadiia Kopiika, Roman Khmil, Jacek Selejdak and Zinoviy Blikharskyy
Appl. Sci. 2022, 12(19), 10157; https://0-doi-org.brum.beds.ac.uk/10.3390/app121910157 - 10 Oct 2022
Cited by 10 | Viewed by 3334
Abstract
Reliable assessment and prediction of the technical condition of reinforced concrete structures require accurate data of the stress–strain state of the structure at all stages of loading. The most appropriate technique to obtain such information is digital image correlation. Digital image correlation is [...] Read more.
Reliable assessment and prediction of the technical condition of reinforced concrete structures require accurate data of the stress–strain state of the structure at all stages of loading. The most appropriate technique to obtain such information is digital image correlation. Digital image correlation is a class of contactless methods which includes the following stages: obtaining an image from a studied physical object, saving it in digital form, and further analysis in order to obtain the necessary information about the stress–strain state of the structure. In this research, a detailed analysis of theoretical and experimental findings of digital image correlations was conducted. In the article, the main areas of scientific interest and computational approaches in digital image correlation issues were identified. Moreover, comparative analysis of alternative non-contact techniques, which also could be used for diagnostics of RC structures’ stress–strain state was conducted. The novelty of the study consists of a thorough comparative analysis with the indication of specific features of digital image correlation, which determine its wide application among the other similar methods. On the basis of the conducted literature review, it can be seen that the digital image correlation technique has gone through multi-stage evolution and transformation. Among the most widely studied issues are: image recognition and matching procedures, calibration methods and development of analytical concepts. The digital image correlation technique enables us to study cracking and fracture processes in structural elements, obtaining the full field of deformations and stresses. Further development of image processing methods would provide more precise measuring of stress–strain parameters and reliable assessment of structural behavior. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Back to TopTop