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Optical Sensors for Structural Health Monitoring

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 68165

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Special Issue Editors

Dr. Paulo Antunes

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Guest Editor

1. I3N & Physics Department of the Aveiro University, 3810-193 Aveiro, Portugal
2. Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Interests: optical fiber sensors; e-Health platforms; structural health monitoring; biosensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Humberto Varum

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Guest Editor

Faculty of Engineering of the University of Porto, Porto, 4099-002 Portugal
Interests: seismic engineering; structural analysis; seismic design; RC structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution and need for preservation and maintenance of existing structures, recent or historical, has fostered research in the area of structural monitoring, translated into the development of new techniques, equipment and sensors. Early detection of damage and accurate assessment of structural safety requires monitoring systems, the data of which can be used to calibrate numerical models for structural analysis and to assess their safety. Data are obtained under real time conditions, considering a group of parameters related to structural properties, such as stresses, accelerations, deformations and displacements. The analysis of the structural properties is particularly relevant when the structure is subjected to extreme events (earthquakes, wind, fire, explosions, among others) or by repeated loads (road/rail/air traffic, vibrations induced by equipment and machines), since they affect the structural integrity, and put at risk the users. In order to prevent the severe damage and eventual collapse of the structures, and consequent human, material and economic losses, the implementation of monitoring systems becomes a valuable tool for today's society.

Monitoring of structures is becoming increasingly important, not only as preventive actions, but also due to the actual economic and sustainability concerns, to ensure a safer and more comfortable built environment. Identifying structural damage and monitoring its evolution requires the development of sensing and structural monitoring techniques. Within these detection technologies arise the optical sensors, which have advantages such as immunity to electromagnetic interference, high sensitivity, reduced size and mass and minimal aesthetic invasion.

This Special Issue will focus on the current state-of-the-art of optical sensors for Structural Health Monitoring (SHM), covering recent technological improvements in new devices/sensors and emerging applications. Both original research papers and review articles describing the current state-of-the-art in this research field are welcome. Editors intend with this SI provide an overview of the present status and a future perspective of the aforementioned topics.

The manuscripts should cover, but are not limited to, the following topics:

Physical, chemical, and environmental optical sensors for SHM;
Interferometric and polarimetric sensors;
Nano- and micro-structured fiber sensors including fiber gratings and photonic crystal fibers;
Multiplexing and sensor networking;
Distributed sensing;
Advances in interrogation techniques for optical sensing;
Smart structures and sensors;
Bragg gratings, Fabry Perot cavities, and plasmonic and Mach Zehnder interferometers;
SHM case studies using optical technologies;
Low-cost, miniaturized, and selective and multiparameter optical devices;
Energy-efficient SHM integrated platforms;
Big data analysis for SHM;
SHM advanced signal processing techniques.

Dr. Paulo Antunes
Prof. Dr. Humberto Varum
Guest Editors

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Published Papers (14 papers)

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Research

16 pages, 4544 KiB

Open AccessArticle

Colorimetric Paper-Based Device for Hazardous Compounds Detection in Air and Water: A Proof of Concept

by Valeria De Matteis, Mariafrancesca Cascione, Gabriele Fella, Laura Mazzotta and Rosaria Rinaldi

Sensors 2020, 20(19), 5502; https://doi.org/10.3390/s20195502 - 25 Sep 2020

Cited by 12 | Viewed by 5593

Abstract

In the last decades, the increase in global industrialization and the consequent technological progress have damaged the quality of the environment. As a consequence, the high levels of hazardous compounds such as metals and gases released in the atmosphere and water, have raised several concerns about the health of living organisms. Today, many analytical techniques are available with the aim to detect pollutant chemical species. However, a lot of them are not affordable due to the expensive instrumentations, time-consuming processes and high reagents volumes. Last but not least, their use is exclusive to trained operators. Contrarily, colorimetric sensing devices, including paper-based devices, are easy to use, providing results in a short time, without particular specializations to interpret the results. In addition, the colorimetric response is suitable for fast detection, especially in resource-limited environments or underdeveloped countries. Among different chemical species, transition and heavy metals such as iron Fe(II) and copper Cu(II) as well as volatile compounds, such as ammonia (NH₃) and acetaldehyde (C₂H₄O) are widespread mainly in industrialized geographical areas. In this work, we developed a colorimetric paper-based analytical device (PAD) to detect different contaminants, including Fe²⁺ and Cu²⁺ ions in water, and NH₃ and C₂H₄O in air at low concentrations. This study is a “proof of concept” of a new paper sensor in which the intensity of the colorimetric response is proportional to the concentration of a detected pollutant species. The sensor model could be further implemented in other technologies, such as drones, individual protection devices or wearable apparatus to monitor the exposure to toxic species in both indoor and outdoor environments. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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21 pages, 29815 KiB

Open AccessArticle

Detection and Measurement of Matrix Discontinuities in UHPFRC by Means of Distributed Fiber Optics Sensing

by Bartłomiej Sawicki, Antoine Bassil, Eugen Brühwiler, Xavier Chapeleau and Dominique Leduc

Sensors 2020, 20(14), 3883; https://doi.org/10.3390/s20143883 - 12 Jul 2020

Cited by 9 | Viewed by 5110

Abstract

Following the significant improvement in their properties during the last decade, Distributed Fiber Optics sensing (DFOs) techniques are nowadays implemented for industrial use in the context of Structural Health Monitoring (SHM). While these techniques have formed an undeniable asset for the health monitoring of concrete structures, their performance should be validated for novel structural materials including Ultra High Performance Fiber Reinforced Cementitious composites (UHPFRC). In this study, a full scale UHPFRC beam was instrumented with DFOs, Digital Image Correlation (DIC) and extensometers. The performances of these three measurement techniques in terms of strain measurement as well as crack detection and localization are compared. A method for the measurement of opening and closing of localized fictitious cracks in UHPFRC using the Optical Backscattering Reflectometry (OBR) technique is verified. Moreover, the use of correct combination of DFO sensors allows precise detection of microcracks as well as monitoring of fictitious cracks’ opening. The recommendations regarding use of various SHM methods for UHPFRC structures are given. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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24 pages, 19209 KiB

Open AccessArticle

Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

by Diyar Khalis Bilal, Mustafa Unel, Mehmet Yildiz and Bahattin Koc

Sensors 2020, 20(12), 3405; https://doi.org/10.3390/s20123405 - 16 Jun 2020

Cited by 4 | Viewed by 3213

Abstract

This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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15 pages, 4478 KiB

Open AccessArticle

Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging

by Dongyan Zhang, Qian Wang, Fenfang Lin, Xun Yin, Chunyan Gu and Hongbo Qiao

Sensors 2020, 20(8), 2260; https://doi.org/10.3390/s20082260 - 16 Apr 2020

Cited by 30 | Viewed by 4056

Abstract

Fusarium head blight (FHB) is a major disease threatening worldwide wheat production. FHB is a short cycle disease and is highly destructive under conducive environments. To provide technical support for the rapid detection of the FHB disease, we proposed to develop a new Fusarium disease index (FDI) based on the spectral data of 374–1050 nm. This study was conducted through the analysis of reflectance spectral data of healthy and diseased wheat ears at the flowering and filling stages by hyperspectral imaging technology and the random forest method. The characteristic wavelengths selected were 570 nm and 678 nm for the late flowering stage, 565 nm and 661 nm for the early filling stage, 560 nm and 663 nm for the combined stage (combining both flowering and filling stages) by random forest. FDI at each stage was derived from the wavebands of each corresponding stage. Compared with other 16 existing spectral indices, FDI demonstrated a stronger ability to determine the severity of the FHB disease. Its determination coefficients (R²) values exceeded 0.90 and the RMSEs were less than 0.08 in the models for each stage. Furthermore, the model for the combined stage performed better when used at single growth stage, but its effect was weaker than that of the models for the two individual growth stages. Therefore, using FDI can provide a new tool to detect the FHB disease at different growth stages in wheat. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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15 pages, 5666 KiB

Open AccessArticle

Combining SDAE Network with Improved DTW Algorithm for Similarity Measure of Ultra-Weak FBG Vibration Responses in Underground Structures

by Sheng Li, Xiang Zuo, Zhengying Li, Honghai Wang and Lizhi Sun

Sensors 2020, 20(8), 2179; https://doi.org/10.3390/s20082179 - 12 Apr 2020

Cited by 7 | Viewed by 3403

Abstract

Quantifying structural status and locating structural anomalies are critical to tracking and safeguarding the safety of long-distance underground structures. Given the dynamic and distributed monitoring capabilities of an ultra-weak fiber Bragg grating (FBG) array, this paper proposes a method combining the stacked denoising autoencoder (SDAE) network and the improved dynamic time wrapping (DTW) algorithm to quantify the similarity of vibration responses. To obtain the dimensionality reduction features that were conducive to distance measurement, the silhouette coefficient was adopted to evaluate the training efficacy of the SDAE network under different hyperparameter settings. To measure the distance based on the improved DTW algorithm, the one nearest neighbor (1-NN) classifier was utilized to search the best constraint bandwidth. Moreover, the study proposed that the performance of different distance metrics used to quantify similarity can be evaluated through the 1-NN classifier. Based on two one-dimensional time-series datasets from the University of California, Riverside (UCR) archives, the detailed implementation process for similarity measure was illustrated. In terms of feature extraction and distance measure of UCR datasets, the proposed integrated approach of similarity measure showed improved performance over other existing algorithms. Finally, the field-vibration responses of the track bed in the subway detected by the ultra-weak FBG array were collected to determine the similarity characteristics of structural vibration among different monitoring zones. The quantitative results indicated that the proposed method can effectively quantify and distinguish the vibration similarity related to the physical location of structures. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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11 pages, 2175 KiB

Open AccessEditor’s ChoiceArticle

Embedded Fiber Sensors to Monitor Temperature and Strain of Polymeric Parts Fabricated by Additive Manufacturing and Reinforced with NiTi Wires

by Micael Nascimento, Patrick Inácio, Tiago Paixão, Edgar Camacho, Susana Novais, Telmo G. Santos, Francisco Manuel Braz Fernandes and João L. Pinto

Sensors 2020, 20(4), 1122; https://doi.org/10.3390/s20041122 - 19 Feb 2020

Cited by 21 | Viewed by 4105

Abstract

This paper focuses on three main issues regarding Material Extrusion (MEX) Additive Manufacturing (AM) of thermoplastic composites reinforced by pre-functionalized continuous Nickel–Titanium (NiTi) wires: (i) Evaluation of the effect of the MEX process on the properties of the pre-functionalized NiTi, (ii) evaluation of the mechanical and thermal behavior of the composite material during usage, (iii) the inspection of the parts by Non-Destructive Testing (NDT). For this purpose, an optical fiber sensing network, based on fiber Bragg grating and a cascaded optical fiber sensor, was successfully embedded during the 3D printing of a polylactic acid (PLA) matrix reinforced by NiTi wires. Thermal and mechanical perturbations were successfully registered as a consequence of thermal and mechanical stimuli. During a heating/cooling cycle, a maximum contraction of ≈100 µm was detected by the cascaded sensor in the PLA material at the end of the heating step (induced by Joule effect) of NiTi wires and a thermal perturbation associated with the structural transformation of austenite to R-phase was observed during the natural cooling step, near 33.0 °C. Regarding tensile cycling tests, higher increases in temperature arose when the applied force ranged between 0.7 and 1.1 kN, reaching a maximum temperature variation of 9.5 ± 0.1 °C. During the unload step, a slope change in the temperature behavior was detected, which is associated with the material transformation of the NiTi wire (martensite to austenite). The embedded optical sensing methodology presented here proved to be an effective and precise tool to identify structural transformations regarding the specific application as a Non-Destructive Testing for AM. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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25 pages, 9183 KiB

Open AccessArticle

Determination of the Real Cracking Moment of Two Reinforced Concrete Beams through the Use of Embedded Fiber Optic Sensors

by Julián García Díaz, Nieves Navarro Cano and Edelmiro Rúa Álvarez

Sensors 2020, 20(3), 937; https://doi.org/10.3390/s20030937 - 10 Feb 2020

Cited by 6 | Viewed by 5684

Abstract

This article investigates the possibility of applying weldable optic fiber sensors to the corrugated rebar in reinforced concrete structures to detect cracks and measure the deformation of the steel. Arrays have initially been designed comprised of two weldable optic fiber sensors, and one temperature sensor to compensate its effect in measuring deformations. A series of tests were performed on the structures to evaluate functioning of the sensors, and the results obtained from the deformation measures shown by the sensors have been stored using specific software. Two reinforced concrete beams simply resting on the support have been designed to perform the tests, and they have been monitored in the zones with maximum flexion moment. Different loading steps have been applied to the beams at the center of the span, using a loading cylinder, and the measurement of the load applied has been determined using a loading cell. The analysis of the deformation measurements of the corrugated rebar obtained by the optic fiber sensors has allowed us to determine the moment at which the concrete has cracked due to the effect of the loads applied and the deformation it has suffered by the effect of the different loading steps applied to the beams. This means that this method of measuring deformations in the corrugated rebar by weldable optic fiber sensors provides very precise results. Future lines of research will concentrate on determining an expression that indicates the real cracking moment of the concrete. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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15 pages, 5819 KiB

Open AccessArticle

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

by Sheng Li, Xiang Zuo, Zhengying Li and Honghai Wang

Sensors 2020, 20(3), 911; https://doi.org/10.3390/s20030911 - 8 Feb 2020

Cited by 30 | Viewed by 4729

Abstract

Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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13 pages, 1926 KiB

Open AccessArticle

Mechanical Properties of Optical Fiber Strain Sensing Cables under γ-Ray Irradiation and Large Strain Influence

by Arianna Piccolo, Sylvie Delepine-Lesoille, Etienne Friedrich, Shasime Aziri, Yann Lecieux and Dominique Leduc

Sensors 2020, 20(3), 696; https://doi.org/10.3390/s20030696 - 27 Jan 2020

Cited by 4 | Viewed by 4259

Abstract

Optical fiber strain sensing cables are widely used in structural health monitoring; however, the impact of a harsh environment on them is not assessed despite the huge importance of the stable performances of the monitoring systems. This paper analyzes (i) the impact of the different constituent layers on the behavior of a strain sensing cable whose constitutive materials are metal and polyamide, (ii) the radiation influence on the optical fiber strain sensing cable response (500 kGy of

γ

-rays), and (iii) the behavior of the cable under high axial strain (up to 1%, 10,000

μ ε

). Radiation impact on strain sensitivity is negligible for practical application, i.e., the coefficient changes by 4% at the max. The influence of the composition of the cable is also assessed: the sensitivity differences remain under 15%, a standard variation range when different cable compositions and structures are considered. The elasto-plastic behavior is at the end evaluated, highlighting the residual strain (about 1600

μ ε

after imposing 10,000

μ ε

) of the cable (especially for metallic parts). Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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16 pages, 6887 KiB

Open AccessArticle

External Corrosion Detection of Oil Pipelines Using Fiber Optics

by Nader Vahdati, Xueting Wang, Oleg Shiryayev, Paul Rostron and Fook Fah Yap

Sensors 2020, 20(3), 684; https://doi.org/10.3390/s20030684 - 26 Jan 2020

Cited by 27 | Viewed by 7464

Abstract

Oil flowlines, the first “pipeline” system connected to the wellhead, are pipelines that are 5 to 30.5 cm (two to twelve inches) in diameter, most susceptible to corrosion, and very difficult to inspect. Herein, an external corrosion detection sensor for oil and gas pipelines, consisting of a semicircular plastic strip, a flat dog-bone-shaped sacrificial metal plate made out of the same pipeline material, and an optical fiber with Fiber Bragg Grating (FBG) sensors, is described. In the actual application, multiple FBG optical fibers are attached to an oil and gas pipeline using straps or strips or very large hose clamps, and, every few meters, our proposed corrosion detection sensor will be glued to the FBG sensors. When the plastic parts are attached to the sacrificial metals, the plastic parts will be deformed and stressed; thus, placing the FBG sensors in tension. When corrosion is severe at any given pipeline location, the sacrificial metal at that location will corrode till failure and the tension strain is relieved at that FBG Sensor location, and therefore, a signal is detected at the interrogator. Herein, the external corrosion detection sensor and its design equations are described, and experimental results, verifying our theory, are presented. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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14 pages, 591 KiB

Open AccessEditor’s ChoiceArticle

Distributed Optical Fiber-Based Approach for Soil–Structure Interaction

by Nissrine Boujia, Franziska Schmidt, Christophe Chevalier, Dominique Siegert and Damien Pham Van Bang

Sensors 2020, 20(1), 321; https://doi.org/10.3390/s20010321 - 6 Jan 2020

Cited by 10 | Viewed by 4846

Abstract

Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the innovative optical frequency domain reflectometry (OFDR) was used to evaluate the effect of scour by performing distributed strain measurements along a rod under static lateral loads. An analytical analysis based on the Winkler model of the soil was carefully established and used to evaluate the accuracy of the fiber optic sensors and helped interpret the measurements results. Dynamic tests were also performed and results from static and dynamic tests were compared using an equivalent cantilever model. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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18 pages, 6802 KiB

Open AccessArticle

Rotating Stall Induced Non-Synchronous Blade Vibration Analysis for an Unshrouded Industrial Centrifugal Compressor

by Xinwei Zhao, Qiang Zhou, Shuhua Yang and Hongkun Li

Sensors 2019, 19(22), 4995; https://doi.org/10.3390/s19224995 - 16 Nov 2019

Cited by 22 | Viewed by 7258

Abstract

Rotating stall limits the operating range and stability of the centrifugal compressor and has a significant impact on the lifetime of the impeller blade. This paper investigates the relationship between stall pressure wave and its induced non-synchronous blade vibration, which will be meaningful for stall resonance avoidance at the early design phase. A rotating disc under a time-space varying load condition is first modeled to understand the physics behind stall-induced vibration. Then, experimental work is conducted to verify the model and reveal the mechanism of stall cells evolution process within flow passage and how blade vibrates when suffering such aerodynamic load. The casing mounted pressure sensors are used to capture the low-frequency pressure wave. Strain gauges and tip timing sensors are utilized to monitor the blade vibration. Based on circumferentially distributed pressure sensors and stall parameters identification method, a five stall cells mode is found in this compressor test rig and successfully correlates with the blade non-synchronous vibration. Furthermore, with the help of tip timing measurement, all blades vibration is also evaluated under different operating mass flow rate. Analysis results verify that the proposed model can show the blade forced vibration under stall flow condition. The overall approach presented in this paper is also important for stall vibration and resonance free design with effective experimental verification. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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12 pages, 2656 KiB

Open AccessArticle

Improved Visual Inspection through 3D Image Reconstruction of Defects Based on the Photometric Stereo Technique

by Sanao Huang, Ke Xu, Ming Li and Mingren Wu

Sensors 2019, 19(22), 4970; https://doi.org/10.3390/s19224970 - 14 Nov 2019

Cited by 19 | Viewed by 3504

Abstract

Visual inspections of nuclear power plant (NPP) reactors are important for understanding current NPP conditions. Unfortunately, the existing visual inspection methods only provide limited two-dimensional (2D) information due to a loss of depth information, which can lead to errors identifying defects. However, the high cost of developing new equipment can be avoided by using advanced data processing technology with existing equipment. In this study, a three-dimensional (3D) photometric stereo (PS) reconstruction technique is introduced to recover the lost depth information in NPP images. The system uses conventional inspection equipment, equipped with a camera and four light-emitting diodes (LEDs). The 3D data of the object surface are obtained by capturing images under multiple light sources oriented in different directions. The proposed method estimates the light directions and intensities for various image pixels in order to reduce the limitation of light calibration, which results in improved performance. This novel technique is employed to test specimens with various defects under laboratory conditions, revealing promising results. This study provides a new visual inspection method for NPP reactors. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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16 pages, 3672 KiB

Open AccessArticle

Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures Based on Crack Image Analysis

by Yuan-Sen Yang, Chia-Hao Chang and Chiun-lin Wu

Sensors 2019, 19(19), 4304; https://doi.org/10.3390/s19194304 - 4 Oct 2019

Cited by 5 | Viewed by 2857

Abstract

Image analysis techniques have been applied to measure the displacements, strain field, and crack distribution of structures in the laboratory environment, and present strong potential for use in structural health monitoring applications. Compared with accelerometers, image analysis is good at monitoring area-based responses, such as crack patterns at critical regions of reinforced concrete (RC) structures. While the quantitative relationship between cracks and structural damage depends on many factors, cracks need to be detected and quantified in an automatic manner for further investigation into structural health monitoring. This work proposes a damage-indexing method by integrating an image-based crack measurement method and a crack quantification method. The image-based crack measurement method identifies cracks locations, opening widths, and orientations. Fractal dimension analysis gives the flexural cracks and shear cracks an overall damage index ranging between 0 and 1. According to the orientations of the cracks analyzed by image analysis, the cracks can be classified as either shear or flexural, and the overall damage index can be separated into shear and flexural damage indices. These damage indices not only quantify the damage of an RC structure, but also the contents of shear and flexural failures. While the engineering significance of the damage indices is structure dependent, when the damage indexing method is used for structural health monitoring, the damage indices safety thresholds can further be defined based on the structure type under consideration. Finally, this paper demonstrates this method by using the results of two experiments on RC tubular containment vessel structures. Full article

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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