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Advanced Structural Health Monitoring: From Theory to Applications, 3rd Edition
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Advanced Structural Health Monitoring: From Theory to Applications, 3rd Edition

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

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 6985

Special Issue Editors

Dr. Hugo Rodrigues

E-Mail Website
Guest Editor
Civil Engineering Department, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: earthquake engineering; structural analysis; seismic analysis of RC buildings; structural repair and maintenance of buildings; structural health monitoring; structural testing and modelling; all aimin
Special Issues, Collections and Topics in MDPI journals
Dr. Ivan Duvnjak

E-Mail Website
Guest Editor
Department for Engineering Mechanics, Faculty of Civil Engineering, University of Zagreb, 10257 Zagreb, Croatia
Interests: assessment of structures; SHM; damage detection; theory of elasticity; static and dynamic testings of structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After the second Special Issue entitled “Advanced Structural Health Monitoring: From Theory to Applications”, we have decided to propose a 3rd edition that will try again to bring together specialists in the field and showcase the last advances and findings related to the topic.

It is well known that structural health monitoring (SHM) is a strategic tool for the monitoring and non-invasive assessment of the health state of existing infrastructures and systems and can be applied in several areas, such as the aeronautical, mechanical, civil, and electrical fields. During their life, systems are subject to several actions and environmental conditions that can lead to structural and nonstructural damage. Recent progress in sensing technology and techniques has allowed us to gain insight into the diagnosis of material degradation and structural and nonstructural damages.

Today, there is great interest in increasing the service life of structures. They are commonly assessed periodically based on the results of visual inspection or local limited nondestructive testing methods. Although visual inspections are essential, the results can often lead to subjective conclusions; therefore, structural health monitoring is essential as a tool that can detect degradation continuously at an early stage of occurrence. SHM can provide decision support for reducing operational costs and risks throughout life cycles.

The present Special Issue focuses on recent developments in theoretical, computational, experimental, and practical aspects in the field and aims to cover different topics, namely: sensors for structural health monitoring; damage detection and characterization algorithms; structural warning systems; model-based structural service life prediction methods; the application of SHM for different exceptional loading; influence of environmental and operational conditions; innovative sensing solutions for SHM; cultural heritage damage detection and health monitoring; bridge damage detection and health monitoring; case study applications; and short-term monitoring systems for diagnostic load testing of structures.

Dr. Hugo Rodrigues
Dr. Ivan Duvnjak
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

  • structural health monitoring
  • sensing and measurement techniques
  • damage detection algorithms and characterization
  • data analysis
  • structural assessment

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Published Papers (3 papers)

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Research

31 pages, 10253 KiB  
Article
Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation
by Srikulnath Nilnoree, Attaphongse Taparugssanagorn, Kamol Kaemarungsi and Tsukasa Mizutani
Appl. Sci. 2024, 14(8), 3494; https://doi.org/10.3390/app14083494 - 20 Apr 2024
Viewed by 3985
Abstract
This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption [...] Read more.
This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications Full article
(This article belongs to the Special Issue Advanced Structural Health Monitoring: From Theory to Applications, 3rd Edition)
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23 pages, 25619 KiB  
Article
Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
by Kultigin Demirlioglu and Emrah Erduran
Appl. Sci. 2024, 14(7), 2969; https://doi.org/10.3390/app14072969 - 31 Mar 2024
Cited by 1 | Viewed by 1346
Abstract
Bridges serve as vital engineering structures crafted to facilitate secure and effective transportation networks. Throughout their life-cycle, they withstand various factors, including diverse environmental conditions, natural hazards, and substantial loads. Recent bridge failures underscore the significant risks posed to the structural integrity of [...] Read more.
Bridges serve as vital engineering structures crafted to facilitate secure and effective transportation networks. Throughout their life-cycle, they withstand various factors, including diverse environmental conditions, natural hazards, and substantial loads. Recent bridge failures underscore the significant risks posed to the structural integrity of bridges. Damage detection techniques, being core components of structural health monitoring, play a crucial role in objectively assessing bridge conditions. This article introduces a novel framework for identifying damage in bridges utilizing continuous wavelet analysis of accelerations recorded using two sensors mounted on a vehicle traversing the bridge. The proposed method leverages changes in the static response of the bridge, which has proven to be more sensitive to damage than its dynamic counterpart. By doing so, the method eliminates the reliance on modal parameters for damage identification, addressing a significant challenge in the field. The proposed framework also addresses key challenges encountered by drive-by monitoring methods. It mitigates the adverse effects of road roughness by utilizing residual accelerations and efficiently detects and locates damage even in the absence of corresponding data from an undamaged bridge. Numerical investigations demonstrate the robustness of the proposed method against various parameters, including damage location and extent, vehicle speeds, road roughness levels, different boundary conditions, and multi-damage scenarios. Full article
(This article belongs to the Special Issue Advanced Structural Health Monitoring: From Theory to Applications, 3rd Edition)
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22 pages, 2270 KiB  
Article
Damage Classification of a Three-Story Aluminum Building Model by Convolutional Neural Networks and the Effect of Scarce Accelerometers
by Emre Ercan, Muhammed Serdar Avcı, Mahmut Pekedis and Çağlayan Hızal
Appl. Sci. 2024, 14(6), 2628; https://doi.org/10.3390/app14062628 - 21 Mar 2024
Cited by 1 | Viewed by 961
Abstract
Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their [...] Read more.
Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures. Full article
(This article belongs to the Special Issue Advanced Structural Health Monitoring: From Theory to Applications, 3rd Edition)
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