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Sensors in Structural Health Monitoring and Smart Structural Control
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Sensors in Structural Health Monitoring and Smart Structural Control

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 28425

Special Issue Editors

Prof. Dr. Tzu-Kang Lin

E-Mail Website
Guest Editor
Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan
Interests: structural health monitoring; smart structural control; bridge engineering; Artificial Intelligence; bio-inspired concept; information theory
Special Issues, Collections and Topics in MDPI journals
Dr. Chia-Ming Chang

E-Mail Website
Co-Guest Editor
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: structural control; advanced large-scale structural testing; smart structures; earthquake engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Chronopoulos

E-Mail Website
Co-Guest Editor
Department of Mechanical Engineering & Mecha(tro)nic System Dynamics (LMSD), KU Leuven, 9000 Leuven, Belgium
Interests: ultrasound metamaterials; metamaterials for structural health monitoring applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of structural health monitoring has been directed to practical applications which can be widely found in civil, mechanical, and aerospace engineering. Advanced diagnosis algorithms are proposed to provide reliable results. Meanwhile, smart structural control has also been extended from an individual objective such as building and facility to a regional and territorial concept.

Sensors undoubtedly play the most important role for both structural health monitoring and smart structural control.  Various sensors including contacting and non-contacting types provide a strong basis for reflecting the desired structural responses, and the monitoring and control process can be then conducted and implemented.  

In this Special Issue, a wide range of topics are covered, including the design, optimization, verification, application, and system integration of structural health monitoring or structural control. Topics include, but are not limited to:

  • Advanced sensing technologies for structural health monitoring;
  • Structural health monitoring using artificial intelligence;
  • Damage diagnosis and prognosis;
  • Inspection using UAVs and UGVs;
  • Quantification and localization;
  • Early alert system;
  • Optimal sensor placement;
  • Adaptive structures using active and semi-active control;
  • Control system integration;
  • Nonlinear stochastic dynamics.

Dr. Tzu-Kang Lin
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. Sensors 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 2600 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

  • Advanced Sensing
  • Structural Health Monitoring
  • Smart Structural Control
  • Optimization.

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Published Papers (7 papers)

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Research

14 pages, 4737 KiB  
Article
Distributed Strain Monitoring Using Nanocomposite Paint Sensing Meshes
by Sijia Li, Yening Shu, Yun-An Lin, Yingjun Zhao, Yi-Jui Yeh, Wei-Hung Chiang and Kenneth J. Loh
Sensors 2022, 22(3), 812; https://doi.org/10.3390/s22030812 - 21 Jan 2022
Cited by 6 | Viewed by 2998
Abstract
Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, [...] Read more.
Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, prior to their deployment. There is an opportunity to design smart and multifunctional paints that can be directly pre-applied onto structural surfaces to serve as a sensing layer among their other layers of functional paints. Therefore, the objective of this study was to design a strain-sensitive paint that can be used for structural monitoring. Carbon nanotubes (CNT) were dispersed in paint by high-speed shear mixing, while paint thinner was employed for adjusting the formulation’s viscosity and nanomaterial concentration. The study started with the design and fabrication of the CNT-based paint. Then, the nanocomposite paint’s electromechanical properties and its sensitivity to applied strains were characterized. Third, the nanocomposite paint was spray-coated onto patterned substrates to form “Sensing Meshes” for distributed strain monitoring. An electrical resistance tomography (ERT) measurement strategy and algorithm were utilized for reconstructing the conductivity distribution of the Sensing Meshes, where the magnitude of conductivity (or resistivity) corresponded to the magnitude of strain, while strain directionality was determined based on the strut direction in the mesh. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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21 pages, 51076 KiB  
Article
Verification of a Stiffness-Variable Control System with Feed-Forward Predictive Earthquake Energy Analysis
by Tzu-Kang Lin, Tappiti Chandrasekhara, Zheng-Jia Liu and Ko-Yi Chen
Sensors 2021, 21(22), 7764; https://doi.org/10.3390/s21227764 - 22 Nov 2021
Cited by 2 | Viewed by 1860
Abstract
Semi-active isolation systems with controllable stiffness have been widely developed in the field of seismic mitigation. Most systems with controllable stiffness perform more robustly and effectively for far-field earthquakes than for near-fault earthquakes. Consequently, a comprehensive system that provides comparable reductions in seismic [...] Read more.
Semi-active isolation systems with controllable stiffness have been widely developed in the field of seismic mitigation. Most systems with controllable stiffness perform more robustly and effectively for far-field earthquakes than for near-fault earthquakes. Consequently, a comprehensive system that provides comparable reductions in seismic responses to both near-fault and far-field excitations is required. In this regard, a new algorithm called Feed-Forward Predictive Earthquake Energy Analysis (FPEEA) is proposed to identify the ground motion characteristics of and reduce the structural responses to earthquakes. The energy distribution of the seismic velocity spectrum is considered, and the balance between the kinetic energy and potential energy is optimized to reduce the seismic energy. To demonstrate the performance of the FPEEA algorithm, a two-degree-of-freedom structure was used as the benchmark in the numerical simulation. The peak structural responses under two near-fault and far-field earthquakes of different earthquake intensities were simulated. The isolation layer displacement was suppressed most by the FPEEA, which outperformed the other three control methods. Moreover, superior control on superstructure acceleration was also supported by the FPEEA. Experimental verification was then conducted with shaking table test, and the satisfactory performance of the FPEEA on both isolation layer displacement and superstructure acceleration was demonstrated again. In summary, the proposed FPEEA has potential for practical application to unexpected near-fault and far-field earthquakes. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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16 pages, 7058 KiB  
Article
Integration with 3D Visualization and IoT-Based Sensors for Real-Time Structural Health Monitoring
by Hung-Fu Chang and Mohammad Shokrolah Shirazi
Sensors 2021, 21(21), 6988; https://doi.org/10.3390/s21216988 - 21 Oct 2021
Cited by 8 | Viewed by 3963
Abstract
Real-time monitoring on displacement and acceleration of a structure provides vital information for people in different applications such as active control and damage warning systems. Recent developments of the Internet of Things (IoT) and client-side web technologies enable a wireless microcontroller board with [...] Read more.
Real-time monitoring on displacement and acceleration of a structure provides vital information for people in different applications such as active control and damage warning systems. Recent developments of the Internet of Things (IoT) and client-side web technologies enable a wireless microcontroller board with sensors to process structural-related data in real-time and to interact with servers so that end-users can view the final processed results of the servers through a browser in a computer or a mobile phone. Unlike traditional structural health monitoring (SHM) systems that deliver warnings based on peak acceleration of earthquake, we built a real-time SHM system that converts raw sensor results into movements and rotations on the monitored structure’s three-dimensional (3D) model. This unique approach displays the overall structural dynamic movements directly from measured displacement data, rather than using force analysis, such as finite element analysis, to predict the displacement statically. As an application to our research outcomes, patterns of movements related to its structure type can be collected for further cross-validating the results derived from the traditional stress-strain analysis. In this work, we overcome several challenges that exist in displaying the 3D effects in real-time. From our proposed algorithm that converts the global displacements into element’s local movements, our system can calculate each element’s (e.g., column’s, beam’s, and floor’s) rotation and displacement at its local coordinate while the sensor’s monitoring result only provides displacements at the global coordinate. While we consider minimizing the overall sensor usage costs and displaying the essential 3D movements at the same time, a sensor deployment method is suggested. To achieve the need of processing the enormous amount of sensor data in real-time, we designed a novel structure for saving sensor data, where relationships among multiple sensor devices and sensor’s spatial and unique identifier can be presented. Moreover, we built a sensor device that can send the monitoring data via wireless network to the local server or cloud so that the SHM web can integrate what we develop altogether to show the real-time 3D movements. In this paper, a 3D model is created according to a two-story structure to demonstrate the SHM system functionality and validate our proposed algorithm. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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22 pages, 10208 KiB  
Article
Structural Response Prediction for Damage Identification Using Wavelet Spectra in Convolutional Neural Network
by Edisson Alberto Moscoso Alcantara, Michelle Diana Bong and Taiki Saito
Sensors 2021, 21(20), 6795; https://doi.org/10.3390/s21206795 - 13 Oct 2021
Cited by 12 | Viewed by 4011
Abstract
If damage to a building caused by an earthquake is not detected immediately, the opportunity to decide on quick action, such as evacuating the building, is lost. For this reason, it is necessary to develop modern technologies that can quickly obtain the structural [...] Read more.
If damage to a building caused by an earthquake is not detected immediately, the opportunity to decide on quick action, such as evacuating the building, is lost. For this reason, it is necessary to develop modern technologies that can quickly obtain the structural safety condition of buildings after an earthquake in order to resume economic and social activities and mitigate future damage by aftershocks. A methodology for the prediction of damage identification is proposed in this study. Using the wavelet spectrum of the absolute acceleration record measured by a single accelerometer located on the upper floor of a building as input data, a CNN model is trained to predict the damage information of the building. The maximum ductility factor, inter-story drift ratio, and maximum response acceleration of each floor are predicted as the damage information, and their accuracy is verified by comparing with the results of seismic response analysis using actual earthquakes. Finally, when an earthquake occurs, the proposed methodology enables immediate action by revealing the damage status of the building from the accelerometer observation records. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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23 pages, 13851 KiB  
Article
Remaining Useful Life Estimation for Engineered Systems Operating under Uncertainty with Causal GraphNets
by Charilaos Mylonas and Eleni Chatzi
Sensors 2021, 21(19), 6325; https://doi.org/10.3390/s21196325 - 22 Sep 2021
Cited by 7 | Viewed by 3368
Abstract
In this work, a novel approach, termed GNN-tCNN, is presented for the construction and training of Remaining Useful Life (RUL) models. The method exploits Graph Neural Networks (GNNs) and deals with the problem of efficiently learning from time series with non-equidistant observations, which [...] Read more.
In this work, a novel approach, termed GNN-tCNN, is presented for the construction and training of Remaining Useful Life (RUL) models. The method exploits Graph Neural Networks (GNNs) and deals with the problem of efficiently learning from time series with non-equidistant observations, which may span multiple temporal scales. The efficacy of the method is demonstrated on a simulated stochastic degradation dataset and on a real-world accelerated life testing dataset for ball-bearings. The proposed method learns a model that describes the evolution of the system implicitly rather than at the raw observation level and is based on message-passing neural networks, which encode the irregularly sampled causal structure. The proposed approach is compared to a recurrent network with a temporal convolutional feature extractor head (LSTM-tCNN), which forms a viable alternative for the problem considered. Finally, by taking advantage of recent advances in the computation of reparametrization gradients for learning probability distributions, a simple, yet efficient, technique is employed for representing prediction uncertainty as a gamma distribution over RUL predictions. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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15 pages, 3893 KiB  
Article
Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study
by Jau-Yu Chou and Chia-Ming Chang
Sensors 2021, 21(18), 6248; https://doi.org/10.3390/s21186248 - 17 Sep 2021
Cited by 13 | Viewed by 3205
Abstract
Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing displacement transducers between the ground and floors in real-world buildings is unrealistic due [...] Read more.
Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing displacement transducers between the ground and floors in real-world buildings is unrealistic due to lack of reference points and structural scales and complexity. Alternatively, structural displacements can be acquired using computer vision-based motion extraction techniques. These extracted motions not only provide vibrational responses but are also useful for identifying the modal properties. In this study, three methods, including the optical flow with the Lucas–Kanade method, the digital image correlation (DIC) with bilinear interpolation, and the in-plane phase-based motion magnification using the Riesz pyramid, are introduced and experimentally verified using a four-story steel-frame building with a commercially available camera. First, the three displacement acquiring methods are introduced in detail. Next, the displacements are experimentally obtained from these methods and compared to those sensed from linear variable displacement transducers. Moreover, these displacement responses are converted into modal properties by system identification. As seen in the experimental results, the DIC method has the lowest average root mean squared error (RMSE) of 1.2371 mm among these three methods. Although the phase-based motion magnification method has a larger RMSE of 1.4132 mm due to variations in edge detection, this method is capable of providing full-field mode shapes over the building. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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19 pages, 4145 KiB  
Article
Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
by Daniel Janos and Przemysław Kuras
Sensors 2021, 21(16), 5552; https://doi.org/10.3390/s21165552 - 18 Aug 2021
Cited by 37 | Viewed by 7332
Abstract
Positioning with low-cost GNSS (Global Navigation Satellite System) receivers is becoming increasingly popular in many engineering applications. In particular, dual-frequency receivers, which receive signals of all available satellite systems, offer great possibilities. The main objective of this research was to evaluate the accuracy [...] Read more.
Positioning with low-cost GNSS (Global Navigation Satellite System) receivers is becoming increasingly popular in many engineering applications. In particular, dual-frequency receivers, which receive signals of all available satellite systems, offer great possibilities. The main objective of this research was to evaluate the accuracy of a position determination using low-cost receivers in different terrain conditions. The u-blox ZED-F9P receiver was used for testing, with the satellite signal supplied by both a dedicated u-blox ANN-MB-00 low-cost patch antenna and the Leica AS10 high-precision geodetic one. A professional Leica GS18T geodetic receiver was used to acquire reference satellite data. In addition, on the prepared test base, observations were made using the Leica MS50 precise total station, which provided higher accuracy and stability of measurement than satellite positioning. As a result, it was concluded that the ZED-F9P receiver equipped with a patch antenna is only suitable for precision measurements in conditions with high availability of open sky. However, the configuration of this receiver with a geodetic-grade antenna significantly improves the quality of results, beating even professional geodetic equipment. In most cases of the partially obscured horizon, a high precision positioning was obtained, making the ZED-F9P a valuable alternative to the high-end geodetic receivers in many applications. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Smart Structural Control)
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