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Special Issue "Innovative Sensors for Civil Infrastructure Condition Assessment"

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

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editor

Prof. Dr. Genda Chen
Website1 Website2 SciProfiles
Guest Editor
Professor and Robert W. Abbett Distinguished Chair in Civil Engineering
Director, Center for Intelligent Infrastructure
Director, INSPIRE University Transportation Center
Missouri University of Science and Technology, Parker Hall, USA
Interests: structural health monitoring; structural control; interface mechanics and deterioration; multihazard mitigation; bridge inspection and maintenance

Special Issue Information

Dear Colleagues,

Civil infrastructure, including bridges, buildings, dams, pipelines, power plants, roads, tunnels, and wastewater treatment plants, is a large and complex network system. Each type of infrastructure represents a capital investment of the private and/or public sectors in a society. Their condition and operational capacity directly impacts economic development of the society and the welfare of citizens. Innovative sensors, together with advanced data analytics, can play a unique role in providing mission critical data for informed decision-making about infrastructure functionality and safety.

Civil infrastructure operates in an open and harsh environment, leading to aging deterioration due to electrochemical reactions in steel, alkali silica reactions in concrete, and other detrimental reactions. Their designs (size and material selections) are determined not only by normal operational loads (i.e., vehicular trucks) but also extreme loads, such as earthquakes and winds. Under these uncertain loads, civil infrastructure can experience various structural behaviors, such as yielding, buckling, cracking, scour, excessive vibration, fatigue, and fracture. To fully understand the behavior of a civil infrastructural system, multiple (even special) sensors for measurement and fusion of heterogeneous data at various scales are required. This Special Issue aims to provide a forum of advanced sensors for expanded and enabled capabilities towards the assessment of civil infrastructure conditions under operational and extreme loads.

Prof. Dr. Genda Chen
Guest Editor

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 2000 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

  • ground, airborne, and remote sensing
  • infrastructure resilience
  • multifunctional nanomaterials
  • smart materials
  • smart sensors
  • sensor network and data fusion
  • sensor placement and durability

Published Papers (17 papers)

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Research

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Open AccessArticle
Developing IoT Sensing System for Construction-Induced Vibration Monitoring and Impact Assessment
Sensors 2020, 20(21), 6120; https://doi.org/10.3390/s20216120 (registering DOI) - 27 Oct 2020
Abstract
Construction activities often generate intensive ground-borne vibrations that may adversely affect structure safety, human comfort, and equipment functionality. Vibration monitoring systems are commonly deployed to assess the vibration impact on the surrounding environment during the construction period. However, traditional vibration monitoring systems are [...] Read more.
Construction activities often generate intensive ground-borne vibrations that may adversely affect structure safety, human comfort, and equipment functionality. Vibration monitoring systems are commonly deployed to assess the vibration impact on the surrounding environment during the construction period. However, traditional vibration monitoring systems are associated with limitations such as expensive devices, difficult installation, complex operation, etc. Few of these monitoring systems have integrated functions such as in situ data processing and remote data transmission and access. By leveraging the recent advances in information technology, an Internet of Things (IoT) sensing system has been developed to provide a promising alternative to the traditional vibration monitoring system. A microcomputer (Raspberry Pi) and a microelectromechanical systems (MEMS) accelerometer are adopted to minimize the system cost and size. A USB internet dongle is used to provide 4G communication with cloud. Time synchronization and different operation modes have been designed to achieve energy efficiency. The whole system is powered by a rechargeable solar battery, which completely avoids cabling work on construction sites. Various alarm functions, MySQL database for measurement data storage, and webpage-based user interface are built on a public cloud platform. The architecture of the IoT vibration sensing system and its working mechanism are introduced in detail. The performance of the developed IoT vibration sensing system has been successfully validated by a series of tests in the laboratory and on a selected construction site. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Monitoring Shear Behavior of Prestressed Concrete Bridge Girders Using Acoustic Emission and Digital Image Correlation
Sensors 2020, 20(19), 5622; https://doi.org/10.3390/s20195622 - 01 Oct 2020
Abstract
In the Netherlands, many prestressed concrete bridge girders are found to have insufficient shear–tension capacity. We tested four girders taken from a demolished bridge and instrumented these with traditional displacement sensors and acoustic emission (AE) sensors, and used cameras for digital image correlation [...] Read more.
In the Netherlands, many prestressed concrete bridge girders are found to have insufficient shear–tension capacity. We tested four girders taken from a demolished bridge and instrumented these with traditional displacement sensors and acoustic emission (AE) sensors, and used cameras for digital image correlation (DIC). The results show that AE can detect cracking before the traditional displacement sensors, and DIC can identify the cracks with detailed crack kinematics. Both AE and DIC methods provide additional information for the structural analysis, as compared to the conventional measurements: more accurate cracking load, the contribution of aggregate interlock, and the angle of the compression field. These results suggest that both AE and DIC are suitable options that warrant further research on their use in lab tests and field testing of prestressed bridges. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Measuring Three-Dimensional Temperature Distributions in Steel–Concrete Composite Slabs Subjected to Fire Using Distributed Fiber Optic Sensors
Sensors 2020, 20(19), 5518; https://doi.org/10.3390/s20195518 - 26 Sep 2020
Abstract
Detailed information about temperature distribution can be important to understand structural behavior in fire. This study develops a method to image three-dimensional temperature distributions in steel–concrete composite slabs using distributed fiber optic sensors. The feasibility of the method is explored using six 1.2 [...] Read more.
Detailed information about temperature distribution can be important to understand structural behavior in fire. This study develops a method to image three-dimensional temperature distributions in steel–concrete composite slabs using distributed fiber optic sensors. The feasibility of the method is explored using six 1.2 m × 0.9 m steel–concrete composite slabs instrumented with distributed sensors and thermocouples subjected to fire for over 3 h. Dense point clouds of temperature in the slabs were measured using the distributed sensors. The results show that the distributed sensors operated at material temperatures up to 960 °C with acceptable accuracy for many structural fire applications. The measured non-uniform temperature distributions indicate a spatially distributed thermal response in steel–concrete composite slabs, which can only be adequately captured using approaches that provide a high density of through-depth data points. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Design of a Small Unmanned Aircraft System for Bridge Inspections
Sensors 2020, 20(18), 5358; https://doi.org/10.3390/s20185358 - 18 Sep 2020
Abstract
Bridge inspections are an important procedure for maintaining the infrastructure vital to our economy and well-being. The current methodology of utilizing specialized equipment such as snooper trucks and scaffolding to support manned-inspections poses a significant financial cost, disrupts traffic, and is dangerous to [...] Read more.
Bridge inspections are an important procedure for maintaining the infrastructure vital to our economy and well-being. The current methodology of utilizing specialized equipment such as snooper trucks and scaffolding to support manned-inspections poses a significant financial cost, disrupts traffic, and is dangerous to the inspectors and public. The advent of unmanned aerial systems (UAS), more commonly called drones, presents a practical solution that promises reduced cost, enhanced safety, and is significantly less intrusive than previous methodologies. Current limitations in the implementation of UAS include the reliance on a skilled operator and/or the requirement for a UAS to operate in a cluttered, GPS-denied environment. A solution to these challenges is presented in this paper by utilizing commercial off-the-shelf (COTS) hardware including laser rangefinders, optical flow sensors, and live video telemetry. Included in the system is the obstacle avoidance equipped drone and a ground station intended to be manned by a pilot and bridge inspector. The proposed custom-fabricated UAS was implemented during eight inspections of Florida Department of Transportation (FDOT) bridges. The UAS was able to navigate under GPS-denied and obstacle-laden bridge decks with position-hold performance comparable to, if not better than, a COTS unit in an unobstructed environment. The position hold capability maintained an altitude of ±12.8 cm with a horizontal hold of ±435 cm. Details of the hardware, algorithm development, and suggestions for future research are discussed in this paper. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Structural Model Identification Using a Modified Electromagnetism-Like Mechanism Algorithm
Sensors 2020, 20(17), 4789; https://doi.org/10.3390/s20174789 - 25 Aug 2020
Abstract
A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction–repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original [...] Read more.
A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction–repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original algorithm, a new local search strategy, new charge and force calculation formulas, new particle movement and updating rules are proposed. The test results of benchmark functions show that the modified EM algorithm has better accuracy and faster convergence rate than the original EM algorithm and the particle swarm optimization (PSO) algorithm. In order to investigate the applicability of this approach in parameter identification of structural models, one numerical truss model and one experimental shear-building model are presented as illustrative examples. The identification results show that this approach can achieve remarkable parameter identification even in the case of large noise contamination and few measurements. The modified EM algorithm can also be used to solve other optimization problems. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
A Novel Sensor Prototype with Enhanced and Adaptive Sensitivity Based on Negative Stiffness Mechanism
Sensors 2020, 20(16), 4644; https://doi.org/10.3390/s20164644 - 18 Aug 2020
Abstract
Loess–mudstone/soil-rock interfacial landslide is one of the prominent landslide hazards that occurs in soil rock contacting zones. It is necessary to develop sensors with high sensitivity to weak and low frequency vibrations for the early warning of such interfacial landslides. In this paper, [...] Read more.
Loess–mudstone/soil-rock interfacial landslide is one of the prominent landslide hazards that occurs in soil rock contacting zones. It is necessary to develop sensors with high sensitivity to weak and low frequency vibrations for the early warning of such interfacial landslides. In this paper, a novel monitoring sensor prototype with enhanced and adaptive sensitivity is developed for this purpose. The novelty of the sensitive sensor is based on the variable capacitances and negative stiffness mechanism due to the electric filed forces on the vibrating plate. Owing to the feedback control of adjustable electrostatic field by an embedded micro controller, the sensor has adaptive amplification characteristics with high sensitivity to weak and low frequency input and low sensitivity to high input. The design and manufacture of the proposed sensor prototype by Micro-Electro-Mechanical Systems (MEMS) with proper packaging are introduced. Post-signal processing is also presented. Some preliminary testing of the prototype and experimental monitoring of sand interfacial slide which mimics soil–rock interfacial landslide were performed to demonstrate the performance of the developed sensor prototype with adaptive amplification and enhanced sensitivity. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Performance Evaluation of a Carbon Nanotube Sensor for Fatigue Crack Monitoring of Metal Structures
Sensors 2020, 20(16), 4383; https://doi.org/10.3390/s20164383 - 06 Aug 2020
Abstract
This article describes research that investigated the ability of a carbon nanotube (CNT) sensor to detect and monitor fatigue crack initiation and propagation in metal structures. The sensor consists of a nonwoven carrier fabric with a thin film of CNT that is bonded [...] Read more.
This article describes research that investigated the ability of a carbon nanotube (CNT) sensor to detect and monitor fatigue crack initiation and propagation in metal structures. The sensor consists of a nonwoven carrier fabric with a thin film of CNT that is bonded to the surface of a structure using an epoxy adhesive. The carrier fabric enables the sensor to be easily applied over large areas with complex geometries. Furthermore, the distributed nature of the sensor improves the probability of detecting crack initiation and enables monitoring of crack propagation over time. Piezoresistivity of the sensor enables strains to be monitored in real time and the sensor, which is designed to fragment as fatigue cracks propagate, directly measures crack growth through permanent changes in resistance. The following laboratory tests were conducted to evaluate the performance of the sensor: (1) continuous crack propagation monitoring, (2) potential false positive evaluation under near-threshold crack propagation conditions, and (3) crack re-initiation detection at a crack-stop hole, which is a commonly used technique to arrest fatigue cracks. Real-time sensor measurements and post-mortem fractography show that a distinguishable resistance change of the sensor occurs due to fatigue crack propagation that can be quantitatively related to crack length. The sensor does not show false positive responses when the crack does not propagate, which is a drawback of many other fatigue sensors. The sensor is also shown to be remarkably sensitive to detecting crack re-initiation. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Electromagnetic Sensors for Underwater Scour Monitoring
Sensors 2020, 20(15), 4096; https://doi.org/10.3390/s20154096 - 23 Jul 2020
Cited by 2
Abstract
Scour jeopardises the safety of many civil engineering structures with foundations in riverbeds and it is the leading cause for the collapse of bridges worldwide. Current approaches for bridge scour risk management rely mainly on visual inspections, which provide unreliable estimates of scour [...] Read more.
Scour jeopardises the safety of many civil engineering structures with foundations in riverbeds and it is the leading cause for the collapse of bridges worldwide. Current approaches for bridge scour risk management rely mainly on visual inspections, which provide unreliable estimates of scour and of its effects, also considering the difficulties in visually monitoring the riverbed erosion around submerged foundations. Thus, there is a need to introduce systems capable of continuously monitoring the evolution of scour at bridge foundations, even during extreme flood events. This paper illustrates the development and deployment of a scour monitoring system consisting of smart probes equipped with electromagnetic sensors. This is the first application of this type of sensing probes to a real case-study for continuous scour monitoring. Designed to observe changes in the permittivity of the medium around bridge foundations, the sensors allow for detection of scour depths and the assessment of whether the scour hole has been refilled. The monitoring system was installed on the A76 200 Bridge in New Cumnock (S-W Scotland) and has provided a continuous recording of the scour for nearly two years. The scour data registered after a peak flood event (validated against actual measurements of scour during a bridge inspection) show the potential of the technology in providing continuous scour measures, even during extreme flood events, thus avoiding the deployment of divers for underwater examination. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Surface Treatment of Carbon Nanotubes Using Modified Tapioca Starch for Improved Force Detection Consistency in Smart Cementitious Materials
Sensors 2020, 20(14), 3985; https://doi.org/10.3390/s20143985 - 17 Jul 2020
Abstract
The remarkable mechanical properties and piezo-responses of carbon nanotubes (CNT) makes this group of nanomaterials an ideal candidate for use in smart cementitious materials to monitor forces and the corresponding structural health conditions of civil structures. However, the inconsistency in measurements is the [...] Read more.
The remarkable mechanical properties and piezo-responses of carbon nanotubes (CNT) makes this group of nanomaterials an ideal candidate for use in smart cementitious materials to monitor forces and the corresponding structural health conditions of civil structures. However, the inconsistency in measurements is the major challenge of CNT-enabled smart cementitious materials to be widely applied for force detection. In this study, the modified tapioca starch co-polymer is introduced to surface treat the CNTs for a better dispersion of CNTs; thus, to reduce the inconsistency of force measurements of the CNTs modified smart cementitious materials. Cement mortar with bare (unmodified) CNTs (direct mixing method) and surfactant surface treated CNTs using sodium dodecyl benzenesulfonate (NaDDBS) were used as the control. The experimental results showed that when compared with samples made from bare CNTs, the samples made by modified tapioca starch co-polymer coated CNTs (CCNTs) showed higher dynamic load induced piezo-responses with significantly improved consistency and less hysteresis in the cementitious materials. When compared with the samples prepared with the surfactant method, the samples made by the developed CCNTs showed slightly increased force detection sensitivity with significantly improved consistency in piezo-response and only minor hysteresis, indicating enhanced dispersion effectiveness. The new CNT surface coating method can be scaled up easily to cater the potential industry needs for future wide application of smart cementitious materials. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Application of an Automated Digital Image-Processing Method for Quantitative Assessment of Cracking Patterns in a Lime Cement Matrix
Sensors 2020, 20(14), 3859; https://doi.org/10.3390/s20143859 - 10 Jul 2020
Abstract
The paper presents an original approach to the localization and analysis of the cracking patterns of cement composites. The lime cement matrix modified with microsilica was evaluated under a two-phase thermal load. For quantitative detection and analysis of thermal cracks, an image-processing method [...] Read more.
The paper presents an original approach to the localization and analysis of the cracking patterns of cement composites. The lime cement matrix modified with microsilica was evaluated under a two-phase thermal load. For quantitative detection and analysis of thermal cracks, an image-processing method was applied. For this purpose, an original image double-segmentation method was developed using machine-learning algorithms. Among other things, the fractal analysis was used to describe the morphology and the thermal evolution of the cracking patterns. The basic mechanical characteristics were examined and the results indicated a very high correlation between tensile strength and all cracking patterns’ parameters. This allows high-quality estimation of the mechanical properties of the lime cement matrix to be carried out on the basis of measurement and evaluation of morphology of the thermal cracking patterns. Knowledge in this field contributes to the development of non-destructive testing methods in cement composites technology, in terms of localization of and tracking the cracking patterns. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Thermally Stable Wireless Patch Antenna Sensor for Strain and Crack Sensing
by Dan Li and Yang Wang
Sensors 2020, 20(14), 3835; https://doi.org/10.3390/s20143835 - 09 Jul 2020
Abstract
Strain and crack are critical indicators of structural safety. As a novel sensing device, a patch antenna sensor can be utilized to wirelessly estimate structural strain and surface crack growth through resonance frequency shift. The main challenges for the sensor are other effects [...] Read more.
Strain and crack are critical indicators of structural safety. As a novel sensing device, a patch antenna sensor can be utilized to wirelessly estimate structural strain and surface crack growth through resonance frequency shift. The main challenges for the sensor are other effects such as temperature fluctuation that can generate unwanted resonance frequency shift and result in large noise in the measurement. Another challenge for existing designs of patch antenna sensor is the limited interrogation distance. In this research, thermally stable patch antenna sensors are investigated for more reliable measurement. Fabricated on a substrate material with a steady dielectric constant, a new passive (battery-free) patch antenna sensor is designed to improve reliability under temperature fluctuations. In addition, another newly designed dual-mode patch antenna sensor is proposed to achieve a longer interrogation distance. Extensive experiments are conducted to characterize the patch antenna sensor performance, including thermal stability, tensile strain sensing, and emulated crack sensing. The two new patch antenna sensors are demonstrated to be effective in wireless strain and crack measurements and have potential applications in structural health monitoring (SHM). Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Probabilistic Updating of Structural Models for Damage Assessment Using Approximate Bayesian Computation
Sensors 2020, 20(11), 3197; https://doi.org/10.3390/s20113197 - 04 Jun 2020
Cited by 1
Abstract
A novel probabilistic approach for model updating based on approximate Bayesian computation with subset simulation (ABC-SubSim) is proposed for damage assessment of structures using modal data. The ABC-SubSim is a likelihood-free Bayesian approach in which the explicit expression of likelihood function is avoided [...] Read more.
A novel probabilistic approach for model updating based on approximate Bayesian computation with subset simulation (ABC-SubSim) is proposed for damage assessment of structures using modal data. The ABC-SubSim is a likelihood-free Bayesian approach in which the explicit expression of likelihood function is avoided and the posterior samples of model parameters are obtained using the technique of subset simulation. The novel contributions of this paper are on three fronts: one is the introduction of some new stopping criteria to find an appropriate tolerance level for the metric used in the ABC-SubSim; the second one is the employment of a hybrid optimization scheme to find finer optimal values for the model parameters; and the last one is the adoption of an iterative approach to determine the optimal weighting factors related to the residuals of modal frequency and mode shape in the metric. The effectiveness of this approach is demonstrated using three illustrative examples. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks
Sensors 2020, 20(7), 2021; https://doi.org/10.3390/s20072021 - 03 Apr 2020
Abstract
Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively [...] Read more.
Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection
Sensors 2020, 20(6), 1790; https://doi.org/10.3390/s20061790 - 24 Mar 2020
Cited by 2
Abstract
Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb [...] Read more.
Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
Corrosion-Induced Mass Loss Measurement under Strain Conditions through Gr/AgNW-Based, Fe-C Coated LPFG Sensors
Sensors 2020, 20(6), 1598; https://doi.org/10.3390/s20061598 - 13 Mar 2020
Cited by 1
Abstract
In this study, graphene/silver nanowire (Gr/AgNW)-based, Fe-C coated long period fiber gratings (LPFG) sensors were tested up to 72 hours in 3.5 w.t% NaCl solution for corrosion-induced mass loss measurement under four strain levels: 0, 500, 1000 and 1500 µε. The crack and [...] Read more.
In this study, graphene/silver nanowire (Gr/AgNW)-based, Fe-C coated long period fiber gratings (LPFG) sensors were tested up to 72 hours in 3.5 w.t% NaCl solution for corrosion-induced mass loss measurement under four strain levels: 0, 500, 1000 and 1500 µε. The crack and interfacial bonding behaviors of laminate Fe-C and Gr/AgNW layer structures were characterized using Scanning Electron Microscopy (SEM) and electrical resistance measurement. Both optical transmission spectra and electrical impedance spectroscopy (EIS) data were simultaneously measured from each sensor. Under increasing strains, transverse cracks appeared first and were followed by longitudinal cracks on the laminate layer structures. The spacing of transverse cracks and the length of longitudinal cracks were determined by the bond strength at the weak Fe-C and Gr/AgNW interface. During corrosion tests, the shift in resonant wavelength of the Fe-C coated LPFG sensors resulted from the effects of the Fe-C layer thinning and the NaCl solution penetration through cracks on the evanescent field surrounding the LPFG sensors. Compared with the zero-strained sensor, the strain-induced cracks on the laminate layer structures initially increased and then decreased the shift in resonant wavelength in two main stages of the Fe-C corrosion process. In each corrosion stage, the Fe-C mass loss was linearly related to the shift in resonant wavelength under zero strain and with the applied strain taken into account in general cases. The general correlation equation was validated at 700 and 1200 µε to a maximum error of 2.5% in comparison with 46.5% from the zero-strain correlation equation. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Open AccessArticle
A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure
Sensors 2020, 20(4), 1059; https://doi.org/10.3390/s20041059 - 15 Feb 2020
Cited by 4
Abstract
Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and [...] Read more.
Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)-based frequency-domain signals, the TF signals exhibited more significant damage-sensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, high-dimensional datasets. The developed TF-1D CNN damage identification framework was demonstrated to have practical value in future applications. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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Review

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Open AccessReview
Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms
Sensors 2020, 20(14), 3954; https://doi.org/10.3390/s20143954 - 16 Jul 2020
Cited by 1
Abstract
The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for [...] Read more.
The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
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