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Special Issue "Smart Sensors for Structural Health Monitoring"

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

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Dr. Simon Laflamme

Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Website | E-Mail
Interests: dense sensor networks; sensing skins; flexible electronics; structural health monitoring; data fusion
Guest Editor
Prof. Dr. Filippo Ubertini

Department of Civil and Environmental Engineering University of Perugia, Perugia, Italy
Website | E-Mail
Interests: smart concrete; smart bricks; earthquake-induced damage detection; vibration-based SHM; Self-sensing cement-based nanocomposites
Guest Editor
Dr. Jian Li

Department of Civil, Environmental and Architectural Engineering, University of Kansas. Lawrence, KS 66045, USA
Website | E-Mail
Interests: data assimilation in SHM; wireless smart sensor networks; innovative sensing techniques; computer vision; uncertainty quantification; risk assessment and mitigation

Special Issue Information

Dear Colleagues,

Smart sensors are technologies designed to facilitate the monitoring operations. For instance, power consumption can be minimized through on-board processing and smart interrogation algorithms, and state detection enhanced through collaboration between sensor nodes. Applied to structural health monitoring, smart sensors are key enablers of sparse and dense sensor networks capable of monitoring full-scale structures and components. They are also critical in empowering operators with decision making capabilities. The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensing technologies for structural health monitoring applications, with a focus on decision-enabling systems. Topics of interest include, but are not limited to:

  • On-board processing algorithms
  • Novel sensors and transducers
  • Intelligent signal processing
  • Smart sensor interrogation
  • Sensing node collaboration
  • Smart sparse and dense sensor networks
  • Smart operation strategies for sensor networks
  • Integrated systems
  • Adaptive monitoring

Dr. Simon Laflamme
Dr. Filippo Ubertini
Dr. Jian Li
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

  • Smart sensors
  • Smart sensing
  • Structural health monitoring
  • Intelligent systems
  • Condition assessment

Published Papers (13 papers)

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Research

Open AccessArticle Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array
Sensors 2019, 19(8), 1843; https://doi.org/10.3390/s19081843
Received: 22 March 2019 / Revised: 14 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
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Abstract
Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have [...] Read more.
Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Robust Identification of Strain Waves due to Low-Velocity Impact with Different Impactor Stiffness
Sensors 2019, 19(6), 1283; https://doi.org/10.3390/s19061283
Received: 12 February 2019 / Revised: 6 March 2019 / Accepted: 9 March 2019 / Published: 14 March 2019
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Abstract
Low-velocity impacts represent a major concern for aeronautical structures, sometimes producing barely detectable damage that could severely hamper the aircraft safety, even with regards to metallic structures. For this reason, the development of an automated impact monitoring system is desired. From a passive [...] Read more.
Low-velocity impacts represent a major concern for aeronautical structures, sometimes producing barely detectable damage that could severely hamper the aircraft safety, even with regards to metallic structures. For this reason, the development of an automated impact monitoring system is desired. From a passive monitoring perspective, any impact generates a strain wave that can be acquired using sensor networks; signal processing techniques allow for extracting features useful for impact identification, possibly in an automatic way. However, impact wave characteristics are related to the impactor stiffness; this presents a problem for the evaluation of an impact-related feature and for the development of an automatic approach to impact identification. This work discusses the problem of reducing the influence of the impactor stiffness on one of the features typically characterizing the impact event, i.e., the time of arrival (TOA). Two passive sensor networks composed of accelerometers and piezoelectric sensors are installed on two metallic specimens, consisting of an aluminum skin and a sandwich panel, with aluminum skins and NOMEXTM honeycomb core. The effect of different impactor stiffnesses is investigated by resorting to an impact hammer, equipped with different tips. Subsequently, a method for data processing is defined to obtain a feature insensitive to the impactor stiffness, and this method is applied to multiple impact signals for feature uncertainty evaluation. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Research on Damage Detection of a 3D Steel Frame Model Using Smartphones
Sensors 2019, 19(3), 745; https://doi.org/10.3390/s19030745
Received: 7 January 2019 / Revised: 8 February 2019 / Accepted: 9 February 2019 / Published: 12 February 2019
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Abstract
Smartphones which are built into the suite of sensors, network transmission, data storage, and embedded processing capabilities provide a wide range of response measurement opportunities for structural health monitoring (SHM). The objective of this work was to evaluate and validate the use of [...] Read more.
Smartphones which are built into the suite of sensors, network transmission, data storage, and embedded processing capabilities provide a wide range of response measurement opportunities for structural health monitoring (SHM). The objective of this work was to evaluate and validate the use of smartphones for monitoring damage states in a three-dimensional (3D) steel frame structure subjected to shaking table earthquake excitation. The steel frame is a single-layer structure with four viscous dampers mounted at the beam-column joints to simulate different damage states at their respective locations. The structural acceleration and displacement responses of undamaged and damaged frames were obtained simultaneously by using smartphones and conventional sensors, while the collected response data were compared. Since smartphones can be used to monitor 3D acceleration in a given space and biaxial displacement in a given plane, the acceleration and displacement responses of the Y-axis of the model structure were obtained. Wavelet packet decomposition and relative wavelet entropy (RWE) were employed to analyze the acceleration data to detect damage. The results show that the acceleration responses that were monitored by the smartphones are well matched with the traditional sensors and the errors are generally within 5%. The comparison of the displacement acquired by smartphones and laser displacement sensors is basically good, and error analysis shows that smartphones with a displacement response sampling rate of 30 Hz are more suitable for monitoring structures with low natural frequencies. The damage detection using two kinds of sensors are relatively good. However, the asymmetry of the structure’s spatial stiffness will lead to greater RWE value errors being obtained from the smartphones monitoring data. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Influence of Uniaxial Stress on the Shear-Wave Spectrum Propagating in Steel Members
Sensors 2019, 19(3), 492; https://doi.org/10.3390/s19030492
Received: 24 December 2018 / Revised: 22 January 2019 / Accepted: 23 January 2019 / Published: 25 January 2019
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Abstract
Structural health monitoring technologies have provided extensive methods to sense the stress of steel structures. However, monitored stress is a relative value rather than an absolute value in the structure’s current state. Among all the stress measurement methods, ultrasonic methods have shown great [...] Read more.
Structural health monitoring technologies have provided extensive methods to sense the stress of steel structures. However, monitored stress is a relative value rather than an absolute value in the structure’s current state. Among all the stress measurement methods, ultrasonic methods have shown great promise. The shear-wave amplitude spectrum and phase spectrum contain stress information along the propagation path. In this study, the influence of uniaxial stress on the amplitude and phase spectra of a shear wave propagating in steel members was investigated. Furthermore, the shear-wave amplitude spectrum and phase spectrum were compared in terms of characteristic frequency (CF) collection, parametric calibration, and absolute stress measurement principles. Specifically, the theoretical expressions of the shear-wave amplitude and phase spectra were derived. Three steel members were used to investigate the effect of the uniaxial stress on the shear-wave amplitude and phase spectra. CFs were extracted and used to calibrate the parameters in the stress measurement formula. A linear relationship was established between the inverse of the CF and its corresponding stress value. The test results show that both the shear-wave amplitude and phase spectra can be used to evaluate uniaxial stress in structural steel members. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Recent Advances in Piezoelectric Wafer Active Sensors for Structural Health Monitoring Applications
Sensors 2019, 19(2), 383; https://doi.org/10.3390/s19020383
Received: 28 November 2018 / Revised: 10 January 2019 / Accepted: 16 January 2019 / Published: 18 January 2019
Cited by 4 | PDF Full-text (10870 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, some recent piezoelectric wafer active sensors (PWAS) progress achieved in our laboratory for active materials and smart structures (LAMSS) at the University of South Carolina: http: //www.me.sc.edu/research/lamss/ group is presented. First, the characterization of the PWAS materials shows that no [...] Read more.
In this paper, some recent piezoelectric wafer active sensors (PWAS) progress achieved in our laboratory for active materials and smart structures (LAMSS) at the University of South Carolina: http: //www.me.sc.edu/research/lamss/ group is presented. First, the characterization of the PWAS materials shows that no significant change in the microstructure after exposure to high temperature and nuclear radiation, and the PWAS transducer can be used in harsh environments for structural health monitoring (SHM) applications. Next, PWAS active sensing of various damage types in aluminum and composite structures are explored. PWAS transducers can successfully detect the simulated crack and corrosion damage in aluminum plates through the wavefield analysis, and the simulated delamination damage in composite plates through the damage imaging method. Finally, the novel use of PWAS transducers as acoustic emission (AE) sensors for in situ AE detection during fatigue crack growth is presented. The time of arrival of AE signals at multiple PWAS transducers confirms that the AE signals are originating from the crack, and that the amplitude decay due to geometric spreading is observed. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Optimal Placement of Virtual Masses for Structural Damage Identification
Sensors 2019, 19(2), 340; https://doi.org/10.3390/s19020340
Received: 11 December 2018 / Revised: 13 January 2019 / Accepted: 15 January 2019 / Published: 16 January 2019
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Abstract
Adding virtual masses to a structure is an efficient way to generate a large number of natural frequencies for damage identification. The influence of a virtual mass can be expressed by Virtual Distortion Method (VDM) using the response measured by a sensor at [...] Read more.
Adding virtual masses to a structure is an efficient way to generate a large number of natural frequencies for damage identification. The influence of a virtual mass can be expressed by Virtual Distortion Method (VDM) using the response measured by a sensor at the involved point. The proper placement of the virtual masses can improve the accuracy of damage identification, therefore the problem of their optimal placement is studied in this paper. Firstly, the damage sensitivity matrix of the structure with added virtual masses is built. The Volumetric Maximum Criterion of the sensitivity matrix is established to ensure the mutual independence of measurement points for the optimization of mass placement. Secondly, a method of sensitivity analysis and error analysis is proposed to determine the values of the virtual masses, and then an improved version of the Particle Swarm Optimization (PSO) algorithm is proposed for placement optimization of the virtual masses. Finally, the optimized placement is used to identify the damage of structures. The effectiveness of the proposed method is verified by a numerical simulation of a simply supported beam structure and a truss structure. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Characterization of a Patch Antenna Sensor’s Resonant Frequency Response in Identifying the Notch-Shaped Cracks on Metal Structure
Sensors 2019, 19(1), 110; https://doi.org/10.3390/s19010110
Received: 29 November 2018 / Revised: 20 December 2018 / Accepted: 24 December 2018 / Published: 30 December 2018
Cited by 1 | PDF Full-text (6160 KB) | HTML Full-text | XML Full-text
Abstract
Patch antenna sensor is a novel sensor that has great potential in structural health monitoring. The two resonant frequencies of a patch antenna sensor are affected by the crack on its ground plane, which enables it to sense the crack information. This paper [...] Read more.
Patch antenna sensor is a novel sensor that has great potential in structural health monitoring. The two resonant frequencies of a patch antenna sensor are affected by the crack on its ground plane, which enables it to sense the crack information. This paper presents a detailed characterization of the relationship between the resonant frequencies of a patch antenna sensor and notch-shaped cracks of different parameters, including the length, the orientation, and the center location. After discussing the principle of crack detection using a patch antenna sensor, a parametric study was performed to understand the response of the sensor’s resonant frequencies to various crack configurations. The results show that the crack parameters affect the resonant frequencies in a way that can be represented by the crack’s cutting effect on the sensor’s current flow. Therefore, we introduced a coefficient φ to comprehensively describe this interaction between the crack and the current distribution of the antenna radiation modes. Based on the definition of coefficient φ , an algorithm was proposed for predicting the resonant frequency shifts caused by a random notch-shaped crack and was verified by the experimental measurements. The presented study aims to provide the foundation for the future use of the patch antenna sensor in tracking the propagation of cracks of arbitrary orientation and location in metal structures. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors
Sensors 2018, 18(12), 4480; https://doi.org/10.3390/s18124480
Received: 18 November 2018 / Revised: 11 December 2018 / Accepted: 14 December 2018 / Published: 18 December 2018
Cited by 1 | PDF Full-text (6565 KB) | HTML Full-text | XML Full-text
Abstract
Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even [...] Read more.
Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle A Method for Settlement Detection of the Transmission Line Tower under Wind Force
Sensors 2018, 18(12), 4355; https://doi.org/10.3390/s18124355
Received: 28 October 2018 / Revised: 5 December 2018 / Accepted: 6 December 2018 / Published: 10 December 2018
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Abstract
In view of the settlement problem of transmission tower foundation, the vibration characteristics of transmission towers under wind force are measured experimentally. In this paper, the 110 kV cat head transmission tower of Xi’an Polytechnic University is measured and analyzed. Firstly, the acceleration [...] Read more.
In view of the settlement problem of transmission tower foundation, the vibration characteristics of transmission towers under wind force are measured experimentally. In this paper, the 110 kV cat head transmission tower of Xi’an Polytechnic University is measured and analyzed. Firstly, the acceleration sensor and meteorological sensor are installed on the tower to collect the vibration response and environment parameters of the tower in real time. Then, an experiment platform is built to simulate the tower settlement, and the vibration response of the tower after settlement is measured in time. Finally, the low-order modal frequencies of the transmission tower before and after settlement under wind force load are extracted by stochastic subspace identification (SSI), and the relationship between modal frequencies of different modes is analyzed via temperature correction. By comparison and analysis, it is obvious that the X-direction modal frequencies before and after settlement under natural wind load are changed, and the change rate increases with the increase of settlement displacement, which can be used as effective evidence for judging the settlement of transmission tower foundation. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Sensor Distribution Optimization for Structural Impact Monitoring Based on NSGA-II and Wavelet Decomposition
Sensors 2018, 18(12), 4264; https://doi.org/10.3390/s18124264
Received: 14 October 2018 / Revised: 18 November 2018 / Accepted: 27 November 2018 / Published: 4 December 2018
Cited by 1 | PDF Full-text (2558 KB) | HTML Full-text | XML Full-text
Abstract
Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate [...] Read more.
Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate the impact, extracting numerical signals of the structure, and the wavelet decomposition is used to extract the band energy. Meanwhile, principal component analysis (PCA) is used to reduce the dimensions of the vibration signal. Following this, the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the placement of sensors. Finally, the experimental system is established, and the Product-based Neural Network is used to recognize different impact categories. Three sets of experiments are carried out to verify the optimal results. When three sensors are applied, the average accuracy of the impact recognition is 59.14%; when the number of sensors is four, the average accuracy of impact recognition is 76.95%. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Structural Damage Identification of Bridges from Passing Test Vehicles
Sensors 2018, 18(11), 4035; https://doi.org/10.3390/s18114035
Received: 1 September 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 19 November 2018
Cited by 1 | PDF Full-text (6997 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents two approaches for the structural damage identification of a bridge from the dynamic response recorded from a test vehicle during its passage over the bridge. Using the acceleration response recorded by the vibration sensors mounted on a test vehicle during [...] Read more.
This paper presents two approaches for the structural damage identification of a bridge from the dynamic response recorded from a test vehicle during its passage over the bridge. Using the acceleration response recorded by the vibration sensors mounted on a test vehicle during its passage over the bridge, along with the computed displacement response, the bending stiffness of the bridge can be determined using either: (1) the frequency-domain method based on the improved directed stiffness method with the identified frequency and corresponding mode shape, or (2) the time-domain method based on the residual vector of the least squares method with a fourth-order displacement moment. By comparing the bending stiffness values identified from the vehicle-collected data for the bridge under the undamaged and damaged states that are monitored regularly by the test vehicle, the bridge damage location and severity can be identified. Through numerical simulations and field tests, the present approaches are shown to be effective and feasible. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model
Sensors 2018, 18(11), 3673; https://doi.org/10.3390/s18113673
Received: 10 September 2018 / Revised: 25 October 2018 / Accepted: 26 October 2018 / Published: 29 October 2018
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Abstract
This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance [...] Read more.
This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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Open AccessArticle An Optical Crack Growth Sensor Using the Digital Sampling Moiré Method
Sensors 2018, 18(10), 3466; https://doi.org/10.3390/s18103466
Received: 6 September 2018 / Revised: 6 October 2018 / Accepted: 9 October 2018 / Published: 15 October 2018
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Abstract
High-accuracy crack growth measurement is crucial for the health assessment of concrete structures. In this work, an optical crack growth sensor using the digital sampling moiré (DSM) method is developed for two-dimensional (2D) crack growth monitoring. The DSM method generates moiré fringes from [...] Read more.
High-accuracy crack growth measurement is crucial for the health assessment of concrete structures. In this work, an optical crack growth sensor using the digital sampling moiré (DSM) method is developed for two-dimensional (2D) crack growth monitoring. The DSM method generates moiré fringes from a single image through digital image processing, and it measures 2D displacements using the phase difference of moiré fringes between motion. Compared with the previous sensors using traditional photogrammetric algorithms such as the normalized cross-correlation (NCC) method, this new DSM-based sensor has several advantages: First, it is of a higher sensitivity and lower computational cost; second, it requires no prior calibration to get accurate 2D displacements which can greatly simplify the practical application for multiple crack monitoring. In addition, it is more robust to the change of imaging distance, which is determined by the height difference between two sides of a concrete crack. These advantages break the limitation of the NCC method and broaden the applicability of the crack growth sensor. These advantages have been verified with one numerical simulation and two laboratory tests. Full article
(This article belongs to the Special Issue Smart Sensors for Structural Health Monitoring)
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