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New Sensor-Based Methods for Structural Health Monitoring—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 2928

Special Issue Editors


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Guest Editor
Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
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Guest Editor
Departamento de Ingeniería Eléctrica y Electrónica, Edificio 411 oficina 210, Ciudad Universitaria, Universidad Nacional de Colombia, Bogotá, Colombia
Interests: structural health monitoring; pattern recognition; condition monitoring; sensors; digital design; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to introduce the second edition of our Special Issue focusing on the evolving realm of structural health monitoring (SHM). With the rapidly advancing integration of technology in both civilian and defense infrastructure, the significance of SHM in devising intelligent structures has become more pronounced than ever before.

Central to the efficacy of SHM is the ability to harness accurate, actionable data. To do that, deploying advanced sensor systems and networks and applying sophisticated signal processing, data fusion, and interpretation techniques are necessary. Whether it is about delivering immediate, real-time insights or comprehensive offline analyses, our mission is to create SHM systems that minimize false positives and preemptively thwart potential calamities.

For this Special Issue, we are eagerly seeking groundbreaking research and applications that bolster the evolution of SHM. We welcome submissions delineating fresh perspectives, innovative methodologies, and impactful applications. Topics of interest encompass, but are not limited to:

  • Advanced damage classification techniques;
  • Innovations in sensor fault detection;
  • Development and applications of intelligent structures;
  • Next-generation smart sensors and their integrations;
  • Cutting-edge data preprocessing and normalization strategies;
  • Expanding and optimizing sensor network systems;
  • Novel pattern recognition algorithms tailored for SHM;
  • Breakthroughs in machine learning for SHM applications;
  • Pioneering multivariate analyses;
  • Leading-edge data-driven algorithms and models;
  • IoT advancements and its convergences in SHM;
  • Techniques for environmental and operational data compensation.

Dr. Francesc Pozo
Dr. Diego Alexander Tibaduiza Burgos
Guest Editors

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Published Papers (2 papers)

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Research

17 pages, 7779 KiB  
Article
Prediction of the Released Mechanical Energy of Loaded Lap Shear Joints by Acoustic Emission Measurements
by Thomas Wolfsgruber, Martin Schagerl and Christoph Kralovec
Sensors 2024, 24(22), 7230; https://doi.org/10.3390/s24227230 - 12 Nov 2024
Cited by 1 | Viewed by 961
Abstract
In lightweight design, the usage of different optimised materials is widespread. The interfaces between two different materials are prone to damage and, therefore, the Structural Health Monitoring (SHM) of these areas is of interest. A new method for the damage evaluation of joints [...] Read more.
In lightweight design, the usage of different optimised materials is widespread. The interfaces between two different materials are prone to damage and, therefore, the Structural Health Monitoring (SHM) of these areas is of interest. A new method for the damage evaluation of joints is developed and validated. The released mechanical energy (RME) during static loading of a metal–composite lap shear joint is considered as a damage assessment parameter and is set into relation to the detected Acoustic Emission (AE) energy. Eleven specimens with identical geometry but different surface treatments are used to form a statistical database for the method, i.e. to calculate the energy ratio and the fluctuation range, and the twelfth specimen is used for the validation of the method. The energy ratio varies significantly, but, considering the fluctuation analysis, the RME with a known range can be predicted on the basis of the AE signal. The whole process is repeated twelve times to validate the methodology. This method can be applied to different geometries and load cases without sophisticated modelling of the damage behaviour. However, load–displacement curves of the pristine joint need to be known, and the monitored joints need to be damage-tolerant and must show similar damage behaviour. Full article
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22 pages, 7226 KiB  
Article
Online Collaborative Perception of Full Bridge Deck Driving Visual of Far Blind Area on Suspension Bridge during Vortex-Induced Vibration
by Danhui Dan, Gang Zeng and Xuewen Yu
Sensors 2024, 24(6), 1934; https://doi.org/10.3390/s24061934 - 18 Mar 2024
Cited by 1 | Viewed by 1092
Abstract
During a vertical vortex-induced vibration (VVIV), an undulating bridge deck will affect drivers’ sightlines, causing the phenomenon of drifting and changes in the far blind area, thus presenting a potential threat to driving safety. Consequently, to ensure the safety of driving on a [...] Read more.
During a vertical vortex-induced vibration (VVIV), an undulating bridge deck will affect drivers’ sightlines, causing the phenomenon of drifting and changes in the far blind area, thus presenting a potential threat to driving safety. Consequently, to ensure the safety of driving on a suspension bridge deck under VVIV, it is necessary to perceive the far blind spot caused by the occlusion of the driving sightlines under this condition, and to establish an online perception and evaluation mechanism for driving safety. With a long-span suspension bridge experiencing VVIV as the engineering background, this paper utilizes the acceleration integration algorithm and the sine function fitting method to achieve the online perception of real-time dynamic configurations of the main girder. Then, based on the configurations, the maximum height of the driver’s far blind area and effective sight distance are calculated accordingly, and the impact of different driving conditions on them is discussed. The proposed technical framework for driving safety perception in far blind spots is feasible, as it can achieve real-time estimation of the maximum height and effective distance of the far blind area, thereby providing technical support for bridge–vehicle–human collaborative perception and traffic control during vortex-induced vibration. Full article
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