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Sensor and Embedded System Applications in Structural Engineering and Engineering Seismology

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 34992

Special Issue Editor

Lab of R/C and Masonry Structures, School of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: reinforced concrete; finite elements; computational engineering; earthquake engineering; experimental methods; embedded systems; structural monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, the growing availability of compact and inexpensive hardware/software development platforms, self-contained electronic modules, and small‑form sensors for detecting and measuring almost any physical quantity has allowed the research community to develop novel prototype (one‑off) or end-product embedded systems for a wide range of engineering applications. The key factor that led to this recent boost of innovation across different engineering disciplines was the open source rationale of the above building components, supported by ample online information. As a result, the apparent multidisciplinary challenge of developing electronic devices for applications on dissimilar engineering fields has become gradually feasible. More specifically, the use of sensor‑enabled systems in the fields of Civil Engineering and Engineering Seismology is already exceptionally beneficial for designing resilient and safer structures and infrastructures, mitigating seismic risk, and planning recovery from natural disasters. The present Special Issue of Sensors is dedicated to original research on the development and verification of novel sensor-based embedded systems for laboratory, structural, construction site or free-field applications. Topics related to this Special Issue of Sensors include but are not limited to:

  • Experimental measuring
  • Earthquake detection, monitoring, and early-warning systems
  • Structural health monitoring
  • Active/hybrid seismic control systems
  • Sensors and sensor fusion
  • Sensor networks and IoT
  • Smart materials and structural components
  • Energy storage and harvesting
  • Device firmware and ad-hoc software for data manipulation

Dr. Vassilis Papanikolaou
Guest Editor

Manuscript Submission Information

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Keywords

  • structural engineering
  • engineering seismology
  • measurements
  • sensors
  • monitoring
  • networks
  • IoT
  • embedded systems
  • earthquakes

Published Papers (12 papers)

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Research

16 pages, 7360 KiB  
Article
Carbon Microfiber-Doped Smart Concrete Sensors for Strain Monitoring in Reinforced Concrete Structures: An Experimental Study at Various Scales
by Antonella D’Alessandro, Hasan Borke Birgin and Filippo Ubertini
Sensors 2022, 22(16), 6083; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166083 - 15 Aug 2022
Cited by 5 | Viewed by 2470
Abstract
Concrete constructions need widespread monitoring for the control of their state of integrity during their service life. In particular, after critical events such as earthquakes, this type of structure may experience the formation and development of cracks and damage. A quick and affordable [...] Read more.
Concrete constructions need widespread monitoring for the control of their state of integrity during their service life. In particular, after critical events such as earthquakes, this type of structure may experience the formation and development of cracks and damage. A quick and affordable assessment of structural behavior is indicated to identify conditions of danger for users and the incipient collapse of structural elements. This work presents investigations on multifunctional concretes with self-sensing capabilities to carry out static and dynamic monitoring. The materials were produced by the addition of conductive carbon microfibers to the concrete matrix. Electrical and sensing tests were carried out on samples with small-, medium-, and full-scale dimensions. The tests demonstrated the good electrical and electromechanical properties of the proposed smart concrete sensors, which appear promising for their use in civil elements or structures. In particular, tests on real-scale beams demonstrated the capability of the material to monitor the dynamic behavior of full-scale structural elements. Full article
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13 pages, 4352 KiB  
Article
Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process
by Voon-Kean Wong, Sarbudeen Mohamed Rabeek, Szu Cheng Lai, Marilyne Philibert, David Boon Kiang Lim, Shuting Chen, Muthusamy Kumarasamy Raja and Kui Yao
Sensors 2022, 22(15), 5724; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155724 - 30 Jul 2022
Cited by 5 | Viewed by 2839
Abstract
While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology [...] Read more.
While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology enabled by a direct-write transducer (DWT) array and edge computing process is proposed in this work. The operation feasibility of the monitoring function is demonstrated with Lamb wave excited and detected by a linear DWT array fabricated in situ from piezoelectric P(VDF-TrFE) polymer coating on an aluminum alloy plate with a simulated defect. The DWT array features lightweight, small profile, high conformability, and implementation scalability, whilst the edge-computing circuit dedicatedly designed for the active ultrasonic SHM is able to perform signal processing at the sensor nodes before wirelessly transmitting the data to a remote host device. The successful implementation of edge-computing processes is able to greatly decrease the amount of data to be transferred by 331 times and decrease the total energy consumption for the wireless module by 224 times. The results and analyses show that the combination of the piezoelectric DWT and edge-computing process provides a promising technical solution for realizing practical wireless active ultrasonic SHM system. Full article
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17 pages, 5279 KiB  
Article
Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis
by Behzad Ghahremani, Alireza Enshaeian and Piervincenzo Rizzo
Sensors 2022, 22(14), 5172; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145172 - 10 Jul 2022
Cited by 7 | Viewed by 2384
Abstract
This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to [...] Read more.
This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios. Full article
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28 pages, 9326 KiB  
Article
Smart Polymer Composite Deck Monitoring Using Distributed High Definition and Bragg Grating Fiber Optic Sensing
by Stephen Young, Dayakar Penumadu, Andrew D. Patchen, George Laggis, Joey Michaud, Abram Bradley, Ryan Davis, John Unser and Matthew Davis
Sensors 2022, 22(11), 4089; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114089 - 27 May 2022
Cited by 2 | Viewed by 2714
Abstract
Fiber-reinforced polymer composites are an excellent choice for bridge decks due to high strength, lightweight, resistance to corrosion, and long-term durability with a 100-year design life. Structural health monitoring is useful for the long-term assessment of the condition of the bridge structure and [...] Read more.
Fiber-reinforced polymer composites are an excellent choice for bridge decks due to high strength, lightweight, resistance to corrosion, and long-term durability with a 100-year design life. Structural health monitoring is useful for the long-term assessment of the condition of the bridge structure and obtaining a response to complex loads considering environmental conditions. Bridge structures have been studied primarily using distributed fiber optic sensing, such as Brillouin scattering; however, critical events, including damage detection, can be missed due to low spatial resolution. There is also a critical need to conduct a comprehensive study of static and dynamic loading simultaneously for fiber-reinforced composite bridge structures. In this study, a novel approach was implemented using two sensor technologies, optical frequency domain reflectometry and fiber Bragg grating-based sensors, embedded in a glass-fiber-reinforced composite bridge deck to simultaneously monitor the deformation response of the bridge structure. The optical frequency domain reflectometry sensor utilizing Rayleigh scattering provides high spatial strain resolution were positioned strategically based on expected stress distributions to measure strain in the longitudinal, transverse, and diagonal directions along the span of the composite bridge. Furthermore, fiber Bragg grating based sensors are used to monitor the response to dynamic vehicular loading and deformations from an automotive-crash-type event on the bridge structure. To monitor environmental variables such as temperature, a custom wireless configured sensor package was developed for the study and integrated with a composite bridge located in Morgan County, Tennessee. Additionally, a triaxial accelerometer was used to monitor the vehicular dynamic loading of the composite bridge deck in parallel with fiber Bragg grating sensors. When appropriate, mid-point displacements were compared with strain-distribution measurements from the fiber optic sensor-based data. Full article
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25 pages, 14032 KiB  
Article
Structural Damage Detection through EMI and Wave Propagation Techniques Using Embedded PZT Smart Sensing Units
by Himanshi Gayakwad and Jothi Saravanan Thiyagarajan
Sensors 2022, 22(6), 2296; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062296 - 16 Mar 2022
Cited by 21 | Viewed by 3151
Abstract
Lead Zirconate Titanate (PZT) sensors have become popular in structural health monitoring (SHM) using the electromechanical impedance (EMI) technique for damage identification. The vibrations generated during the casting process in concrete structures substantially impact the conductance signature’s (real part of admittance) magnitude and [...] Read more.
Lead Zirconate Titanate (PZT) sensors have become popular in structural health monitoring (SHM) using the electromechanical impedance (EMI) technique for damage identification. The vibrations generated during the casting process in concrete structures substantially impact the conductance signature’s (real part of admittance) magnitude and sensitivity. The concept of smart sensing units (SSU) is presented, composed of a PZT patch, an adhesive layer, and a steel plate. It is embedded in the concrete structure to study the impact of damage since it has high sensitivity to detect any structural changes, resulting in a high electrical conductance signature. The conductance signatures are obtained from the EMI technique at the damage state in the 10–500 kHz high-frequency range. The wave propagation technique proposes implementing the novel embedded SSUs to detect damage in the host structure. The numerical simulation is carried out with COMSOL multiphysics, and the received voltage signal is compared between the damaged and undamaged concrete beam with the applied actuation signal. A five-cycle sine burst modulated by a Hanning window is employed as the transient excitation signal. For numerical investigation, six cases are explored to better understand how the wave travels when a structural discontinuity is accounted for. The changes in the received signal during actuator–receiver mode in the damage state of the host structure are quantified using time of flight (TOF). Furthermore, the numerical studies are carried out by combining the EMI-WP technique, which implies synchronous activation of EMI-based measurements and wave stimulation. The fundamental idea is to implement EMI-WP to improve the effectiveness of SSU patches in detecting both near-field and far-field damage in structures. One SSU is used as an EMI admittance sensor for local damage identification. Meanwhile, the same EMI admittance sensor is used to acquire elastic waves generated by another SSU to monitor damages outside the EMI admittance sensor’s sensing area. Finally, the experimental validation is carried out to verify the proposed methodology. The results show that combining both techniques is an effective SHM method for detecting damage in concrete structures. Full article
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19 pages, 1336 KiB  
Article
Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring
by Federica Zonzini, Antonio Carbone, Francesca Romano, Matteo Zauli and Luca De Marchi
Sensors 2022, 22(6), 2229; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062229 - 14 Mar 2022
Cited by 16 | Viewed by 3099
Abstract
Artificial Intelligence applied to Structural Health Monitoring (SHM) has provided considerable advantages in the accuracy and quality of the estimated structural integrity. Nevertheless, several challenges still need to be tackled in the SHM field, which extended the monitoring process beyond the mere data [...] Read more.
Artificial Intelligence applied to Structural Health Monitoring (SHM) has provided considerable advantages in the accuracy and quality of the estimated structural integrity. Nevertheless, several challenges still need to be tackled in the SHM field, which extended the monitoring process beyond the mere data analytics and structural assessment task. Besides, one of the open problems in the field relates to the communication layer of the sensor networks since the continuous collection of long time series from multiple sensing units rapidly consumes the available memory resources, and requires complicated protocol to avoid network congestion. In this scenario, the present work presents a comprehensive framework for vibration-based diagnostics, in which data compression techniques are firstly introduced as a means to shrink the dimension of the data to be managed through the system. Then, neural network models solving binary classification problems were implemented for the sake of damage detection, also encompassing the influence of environmental factors in the evaluation of the structural status. Moreover, the potential degradation induced by the usage of low cost sensors on the adopted framework was evaluated: Additional analyses were performed in which experimental data were corrupted with the noise characterizing MEMS sensors. The proposed solutions were tested with experimental data from the Z24 bridge use case, proving that the amalgam of data compression, optimized (i.e., low complexity) machine learning architectures and environmental information allows to attain high classification scores, i.e., accuracy and precision greater than 96% and 95%, respectively. Full article
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18 pages, 920 KiB  
Article
Stretching Method-Based Damage Detection Using Neural Networks
by Emmanouil Daskalakis, Christos G. Panagiotopoulos and Chrysoula Tsogka
Sensors 2022, 22(3), 830; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030830 - 22 Jan 2022
Cited by 1 | Viewed by 1242
Abstract
We present in this paper a framework for damage detection and localization using neural networks. The data we use to train the network are m×d pixel images consisting of measurements of the relative variations of m natural frequencies of the structure [...] Read more.
We present in this paper a framework for damage detection and localization using neural networks. The data we use to train the network are m×d pixel images consisting of measurements of the relative variations of m natural frequencies of the structure under monitoring over a period of d-days. To measure the relative variations of the natural frequencies, we use the stretching method, which allows us to obtain reliable measurements amidst fluctuations induced by environmental factors such as temperature variations. We show that even by monitoring a single natural frequency over a few days, accurate damage detection can be achieved. The accuracy for damage detection significantly improves when a small number of natural frequencies is monitored instead of a single one. More importantly, monitoring multiple natural frequencies allows for damage localization provided that the network can be trained for both healthy and damaged scenarios. This is feasible under the assumption that damage occurs at a finite number of damage-prone locations. Several results obtained with numerically simulated data illustrate the effectiveness of the proposed approach. Full article
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26 pages, 5729 KiB  
Article
Design and Implementation of a Wireless Sensor Network for Seismic Monitoring of Buildings
by Julio Antonio Jornet-Monteverde, Juan José Galiana-Merino and Juan Luis Soler-Llorens
Sensors 2021, 21(11), 3875; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113875 - 04 Jun 2021
Cited by 14 | Viewed by 4216
Abstract
This article presents a new wireless seismic sensor network system, especially design for building monitoring. The designed prototype allows remote control, and remote and real-time monitoring of the recorded signals by any internet browser. The system is formed by several Nodes (based on [...] Read more.
This article presents a new wireless seismic sensor network system, especially design for building monitoring. The designed prototype allows remote control, and remote and real-time monitoring of the recorded signals by any internet browser. The system is formed by several Nodes (based on the CC3200 microcontroller of Texas Instruments), which are in charge of digitizing the ambient vibrations registered by three-component seismic sensors and transmitting them to a central server. This server records all the received signals, but also allows their real-time visualization in several remote client browsers thanks to the JavaScript’s Node.js technology. The data transmission uses not only Wi-Fi technology, but also the existing network resources that nowadays can be found usually in any official or residential building (lowering deployment costs). A data synchronization scheme was also implemented to correct the time differences between the Nodes, but also the long-term drifts found in the internal clock of the microcontrollers (improving the quality of records). The completed system is a low-cost, open-hardware and open-software design. The prototype was tested in a real building, recording ambient vibrations in several floors and observing the differences due to the building structure. Full article
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12 pages, 11042 KiB  
Communication
A Low-Cost Instrumentation System for Seismic Hazard Assessment in Urban Areas
by Vassilis K. Papanikolaou, Christos Z. Karakostas and Nikolaos P. Theodoulidis
Sensors 2021, 21(11), 3618; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113618 - 22 May 2021
Cited by 6 | Viewed by 2292
Abstract
The development and application of a low-cost instrumentation system for seismic hazard assessment in urban areas are described in the present study. The system comprises a number of autonomous triaxial accelerographs, designed and manufactured in house and together with dedicated software for device [...] Read more.
The development and application of a low-cost instrumentation system for seismic hazard assessment in urban areas are described in the present study. The system comprises a number of autonomous triaxial accelerographs, designed and manufactured in house and together with dedicated software for device configuration, data collection and further postprocessing. The main objective is to produce a detailed view of strong motion variability in urban areas, for at least light intensity strong motion events. The overall cost of the developed devices is at least ten times lower than the respective commercial units, hence their deployment as an ultra-dense network over the area of interest can be significantly cost-effective. This approach is considered an efficient complement to traditional microzonation procedures, which are typically based on relatively few actual recordings and the application of theoretical methodologies to assess the strong motion distribution. The manufactured devices adopt micro-electro-mechanical (MEMS) digital sensor technology for recording acceleration, whereas the accompanying software suite provides various configuration options, quick browsing, analyzing and exporting of the recorded events, as well as GIS type functionality for seamlessly producing explicit seismic hazard maps of the considered area. The evaluation of system performance was based on shaking table and real field comparisons against high accuracy commercial accelerographs. The study concludes with a real application of the proposed system in the form of an ultra-dense network installed at the city of Lefkada, an earthquake prone urban area in Greece, and the following compilation of explicit shakemaps. Full article
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16 pages, 7115 KiB  
Article
A New Cable-Less Seismograph with Functions of Real-Time Data Transmitting and High-Precision Differential Self-Positioning
by Kang Liu, Qingyu You, Juan Wang, Xiqiang Xu, Pengcheng Shi, Kaoshan Dai, Zhenhua Huang, Shiquan Wang, Yuanfeng Shi and Zhibin Ding
Sensors 2020, 20(14), 4015; https://0-doi-org.brum.beds.ac.uk/10.3390/s20144015 - 19 Jul 2020
Cited by 4 | Viewed by 2826
Abstract
This study developed a new cable-less seismograph system, which can transmit seismic data in real-time and automatically perform high-precision differential self-positioning. Combining the ZigBee technology with the high-precision differential positioning module, this new seismograph system utilized the wireless personal area network (WPAN) and [...] Read more.
This study developed a new cable-less seismograph system, which can transmit seismic data in real-time and automatically perform high-precision differential self-positioning. Combining the ZigBee technology with the high-precision differential positioning module, this new seismograph system utilized the wireless personal area network (WPAN) and real-time kinematic (RTK) technologies to improve its on-site performances and to make the field quality control (QC) and self-positioning possible. With the advantages of low-cost, good scalability, and good compatibility, the proposed new cable-less seismograph system can improve the field working efficiency and data processing capability. It has potential applications in noise seismology and mobile seismic monitoring. Full article
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17 pages, 4740 KiB  
Article
Dynamic Responses of a Metro Train-Bridge System under Train-Braking: Field Measurements and Data Analysis
by Xuhui He, Kehui Yu, Chenzhi Cai, Yunfeng Zou and Xiaojie Zhu
Sensors 2020, 20(3), 735; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030735 - 29 Jan 2020
Cited by 4 | Viewed by 2864
Abstract
This paper focuses on the dynamic responses of a metro train–bridge system under train-braking. Experiments were performed on the elevated Metro Line 21 of Guangzhou (China). A continuous, three-span, rigid-frame bridge (42 m + 65 m + 42 m) and a standard B-type [...] Read more.
This paper focuses on the dynamic responses of a metro train–bridge system under train-braking. Experiments were performed on the elevated Metro Line 21 of Guangzhou (China). A continuous, three-span, rigid-frame bridge (42 m + 65 m + 42 m) and a standard B-type metro train were selected. The acceleration signals were measured at the center-points of the main span and one side-span, and the acceleration signals of the car body and the bogie frame were measured simultaneously. The train–bridge system’s vibration characteristics and any correlations with time and frequency were investigated. The Choi–Williams distribution method and wavelet coherence were introduced to analyze the obtained acceleration signals of the metro train–bridge system. The results showed that the Choi–Williams distribution provided a more explicit understanding of the time–frequency domain. The correlations between different parts of the bridge and the train–bridge system under braking conditions were revealed. The present study provides a series of measured dynamic responses of the metro train–bridge system under train-braking, which could be used as a reference in further investigations. Full article
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20 pages, 2795 KiB  
Article
Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution
by Cai Wu, Shujin Li and Yuanjin Zhang
Sensors 2019, 19(19), 4341; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194341 - 08 Oct 2019
Cited by 5 | Viewed by 2411
Abstract
Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the [...] Read more.
Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLSE) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLSE method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure. Full article
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