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Special Issue "Optical Fiber Sensor Technology 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: 20 January 2024 | Viewed by 4015

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Special Issue Editors

Prof. Dr. Weimin Chen

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Guest Editor

School of Optoelectronic Engineering, Chongqing University, Chongqing, China
Interests: optical fiber sensing; precision measurement; structural health monitoring; semiconductor lighting

Prof. Dr. Zhi Zhou

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Guest Editor

College of Civil Engineering and Architecture, Hainan University, Haikou, China
Interests: optical fiber sensor; measurement; structural health monitoring

Dr. Ying Huang

E-Mail Website
Guest Editor

Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: advanced high-performance and smart materials; artificial intelligence and big data; corrosion protection and mitigation; structural health monitoring; railroad damage and defect assessment; smart cities and autonomous systems; energy networks and pipeline
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaohua Lei
E-Mail Website
Guest Editor

College of Optoelectronic Engineering, Chongqing University, Chongqing, China
Interests: optical fiber sensor; signal processing

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is regarded as an extra safety measure for a variety of complex structures and anisotropic heterogeneous materials, which requires effective sensors. Fiber optic sensors (FOS) can be good candidates to apply in SHM due to their inherent unique advantages of small size, light weight, resistance to electromagnetic interference and corrosion resistance, long-term durability, and ability of multiplexing and embedding into host structures. However, the application of OFS in SHM still faces many challenges, especially for anisotropic heterogeneous materials. These challenges demand innovative research and new engineering applications of FOS technology for a wider application of FOS. This Special Issue, therefore, seeks original research and review articles on recent advances, technologies, solutions, and applications in the field of FOS technology for SHM.

Potential topics include, but are not limited to, the following themes:

Sensing technology: principles/algorithms, interrogation/demodulation, multi/mass multiplex, quasi-distributed/ultra-long distributed sensing, interference immunity/compensate;
Special sensors: strain/stress, deformation/displacement, vibration/acceleration, crack/corrosion, delamination/damage, pavement/compound/truss;
Applications: civil engineering, aerospace, aviation, navigation, aerospace, transportation, underground/underwater structures, power/nuclear facilities, heavy machinery;
Key techniques: fabrication, installation, calibration, accuracy, durability.

Prof. Dr. Weimin Chen
Prof. Dr. Zhi Zhou
Dr. Ying Huang
Dr. Xiaohua Lei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

optical fiber sensor technology
structural health monitoring
anisotropic heterogeneous
strain transfer
sensing technology
applications
key techniques
distributed sensing

Published Papers (6 papers)

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Research

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Open AccessArticle

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm

and

Sensors 2023, 23(17), 7379; https://doi.org/10.3390/s23177379 - 24 Aug 2023

Viewed by 293

Abstract

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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Open AccessArticle

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

and

Sensors 2023, 23(16), 7059; https://doi.org/10.3390/s23167059 - 09 Aug 2023

Viewed by 490

Abstract

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time–frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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Open AccessArticle

A Novel Smart CFRP Cable Based on Optical Electrical Co-Sensing for Full-Process Prestress Monitoring of Structures

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Sensors 2023, 23(11), 5261; https://doi.org/10.3390/s23115261 - 01 Jun 2023

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Abstract

Carbon-fiber-reinforced polymer (CFRP) is a type of composite material with many superior performances, such as high tensile strength, light weight, corrosion resistance, good fatigue, and creep performance. As a result, CFRP cables have great potential to replace steel cables in prestressed concrete structures. However, the technology to monitor the stress state in real-time throughout the entire life cycle is very important in the application of CFRP cables. Therefore, an optical–electrical co-sensing CFRP cable (OECSCFRP cable) was designed and manufactured in this paper. Firstly, a brief description is outlined for the production technology of the CFRP-DOFS bar, CFRP-CCFPI bar, and CFRP cable anchorage technology. Subsequently, the sensing and mechanical properties of the OECS-CFRP cable were characterized by serious experiments. Finally, the OECS-CFRP cable was used for the prestress monitoring of an unbonded prestressed RC beam to verify the feasibility of the actual structure. The results show that the main static performance indexes of DOFS and CCFPI meet the requirements of civil engineering. In the loading test of the prestressed beam, the OECS-CFRP cable can effectively monitor the cable force and the midspan defection of the beam so as to obtain the stiffness degradation of the prestressed beam under different loads. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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Open AccessArticle

Guided Wave Ultrasonic Testing for Crack Detection in Polyethylene Pipes: Laboratory Experiments and Numerical Modeling

Jay Shah

Said El-Hawwat

and

Hao Wang

Sensors 2023, 23(11), 5131; https://doi.org/10.3390/s23115131 - 27 May 2023

Viewed by 1185

Abstract

The use of guided wave-based Ultrasonic Testing (UT) for monitoring Polyethylene (PE) pipes is mostly restricted to detecting defects in welded zones, despite its diversified success in monitoring metallic pipes. PE’s viscoelastic behavior and semi-crystalline structure make it prone to crack formation under extreme loads and environmental factors, which is a leading cause of pipeline failure. This state-of-the-art study aims to demonstrate the potential of UT for detecting cracks in non-welded regions of natural gas PE pipes. Laboratory experiments were conducted using a UT system consisting of low-cost piezoceramic transducers assembled in a pitch-catch configuration. The amplitude of the transmitted wave was analyzed to study wave interaction with cracks of different geometries. The frequency of the inspecting signal was optimized through wave dispersion and attenuation analysis, guiding the selection of third- and fourth- order longitudinal modes for the study. The findings revealed that cracks with lengths equal to or greater than the wavelength of the interacting mode were more easily detectable, while smaller crack lengths required greater crack depths for detection. However, there were potential limitations in the proposed technique related to crack orientation. These insights were validated using a finite element-based numerical model, confirming the potential of UT for detecting cracks in PE pipes. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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Open AccessArticle

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN–Adaptive Threshold Wavelet Method

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Sensors 2023, 23(9), 4268; https://doi.org/10.3390/s23094268 - 25 Apr 2023

Viewed by 673

Abstract

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), combined with adaptive threshold wavelet de-noising is proposed. The adaptive threshold wavelet filtering method composed of the mean and variance of wavelet coefficients of each layer is used to de-noise the BDS displacement monitoring data. CEEMDAN was used to decompose the displacement response data of the bridge to obtain the intrinsic mode function (IMF). Correlation coefficients were used to distinguish the noisy component from the effective component, and the adaptive threshold wavelet de-noising occurred on the noisy component. Finally, all IMF were restructured. The simulation experiment and the BDS displacement monitoring data of Nanmao Bridge were verified. The results demonstrated that the proposed method could effectively suppress random noise and multipath noise, and effectively obtain the real response of bridge displacement. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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Review

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Open AccessReview

Fiber Optic-Based Durability Monitoring in Smart Concrete: A State-of-Art Review

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Sensors 2023, 23(18), 7810; https://doi.org/10.3390/s23187810 - 11 Sep 2023

Viewed by 333

Abstract

Concrete is the most commonly used construction material nowadays. With emerging cutting-edge technologies such as nanomaterials (graphene, carbon nanotubes, etc.), advanced sensing (fiber optics, computer tomography, etc.), and artificial intelligence, concrete can now achieve self-sensing, self-healing, and ultrahigh performance. The concept and functions of smart concrete have thus been partially realized. However, due to the wider application location (coastal areas, cold regions, offshore, and deep ocean scenarios) and changing climate (temperature increase, more CO₂ emissions, higher moisture, etc.), durability monitoring (pH, ion penetration, carbonation, corrosion, etc.) becomes an essential component for smart concrete. Fiber optic sensors (FOS) have been widely explored in recent years for concrete durability monitoring due to their advantages of high sensitivity, immunity to harsh environments, small size, and superior sensitivity. The purpose of this review is to summarize FOS development and its application in concrete durability monitoring in recent years. The objectives of this study are to (1) introduce the working principle of FOS, including fiber Bragg grating (FBG), long-period fiber grating (LPFG), surface plasmon resonance (SPR), fluorescence-based sensors, and distributed fiber optic sensors (DFOS); (2) compare the sensitivity, resolution, and application scenarios of each sensor; and (3) discuss the advantages and disadvantages of FOS in concrete durability monitoring. This review is expected to promote technical development and provide potential research paths in the future for FOS in durability monitoring in smart concrete. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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