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Special Issue "Novel Methodologies to Interpret Non-Destructive Testing and Structural Health Monitoring Data"

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

Deadline for manuscript submissions: closed (15 May 2018).

Special Issue Editor

Guest Editor
Prof. Dr. Thomas Schumacher

Civil and Environmental Engineering, Portland State University, Portland, OR 97201, USA
Website | E-Mail
Interests: novel sensing methodologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil infrastructure: Quantitative acoustic emission monitoring, carbon nanotube-based sensing composites, digital video-based monitoring; behavior and durability of concrete structures; sustainable structures; probabilistic approaches

Special Issue Information

Dear Colleagues,

The fields of non-destructive testing (NDT) and structural health monitoring (SHM) have emerged as important tools to inspect and maintain structures and mechanical systems. However, the issue of data interpretation remains a critical component in the whole process: In order for an owner or agency to make informed decisions regarding maintenance and repair of a system, reliable, as well as actionable, data are required.

In this Special Issue, we solicit review articles, original research papers, and short communications covering novel data analysis and interpretation methodologies for NDT and SHM of structures (e.g., buildings, bridges, off-shore platforms) and mechanical systems (e.g., aerospace, automobile, power generation). Methodologies of interest include, but are not limited to, novel analysis algorithms, statistical modeling, prediction and prognosis, advanced signal processing, data mining, and data fusion. An appropriate submission describes the instrumentation used, explains in detail the proposed methodology, discusses the efforts made to validate the methodology, and describes how the results are used as actionable data for informed decision-making. Submissions that discuss only numerical studies will not be considered.

Please do not hesitate to contact me should you have any questions about whether your work falls within the general scope of this Special Issue. I look forward to your contribution!

Dr. Thomas Schumacher
Guest Editor

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 papers will be 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 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.

Published Papers (18 papers)

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Research

Open AccessArticle
Development of a Novel Methodology to Assess the Corrosion Threshold in Concrete Based on Simultaneous Monitoring of pH and Free Chloride Concentration
Sensors 2018, 18(9), 3101; https://doi.org/10.3390/s18093101
Received: 7 August 2018 / Revised: 8 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
Cited by 3 | PDF Full-text (2509 KB) | HTML Full-text | XML Full-text
Abstract
Both the free chloride concentration and the pH of the concrete pore solution are highly relevant parameters that control corrosion of the reinforcing steel. In this paper, we present a method to continuously monitor these two parameters in-situ. The approach is based on [...] Read more.
Both the free chloride concentration and the pH of the concrete pore solution are highly relevant parameters that control corrosion of the reinforcing steel. In this paper, we present a method to continuously monitor these two parameters in-situ. The approach is based on a recently developed electrode system that consists of several different potentiometric sensors as well as a data interpretation procedure. Instrumented mortar specimens containing different amounts of admixed chlorides were exposed to accelerated carbonation, and changes in free chloride concentration and pH were monitored simultaneously over time. The results revealed the stepwise decrease in pH as well as corresponding increases in free chlorides, resulting from the release of bound chlorides. For a pH drop of about 1 unit (from pH 13.5 down to pH 12.5), the free chloride concentration increased up to 1.5-fold. We continuously quantified the ratio Cl/OH that increased steeply with time, and was found to exceed a critical corrosion threshold long before carbonation can be detected with traditional indicator spray testing, even at admixed chloride contents in the order of allowable limits. These results can strongly influence the decision-making in engineering practice and it is expected to significantly improve condition assessments of reinforced concrete structures. Full article
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Open AccessArticle
A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings
Sensors 2018, 18(7), 2285; https://doi.org/10.3390/s18072285
Received: 15 May 2018 / Revised: 25 June 2018 / Accepted: 12 July 2018 / Published: 14 July 2018
Cited by 48 | PDF Full-text (10564 KB) | HTML Full-text | XML Full-text
Abstract
This article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography [...] Read more.
This article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement were introduced. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls as both flat intersections (2D) and spatial (3D) images. The presented group of algorithms has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified. Full article
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Open AccessArticle
Processing Ultrasonic Data by Coda Wave Interferometry to Monitor Load Tests of Concrete Beams
Sensors 2018, 18(6), 1971; https://doi.org/10.3390/s18061971
Received: 11 May 2018 / Revised: 5 June 2018 / Accepted: 12 June 2018 / Published: 19 June 2018
Cited by 4 | PDF Full-text (5064 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Ultrasonic transmission measurements have been used for decades to monitor concrete elements, mostly on a laboratory scale. Recently, coda wave interferometry (CWI), a technique adapted from seismology, was introduced to civil engineering experiments. It can be used to reveal subtle changes in concrete [...] Read more.
Ultrasonic transmission measurements have been used for decades to monitor concrete elements, mostly on a laboratory scale. Recently, coda wave interferometry (CWI), a technique adapted from seismology, was introduced to civil engineering experiments. It can be used to reveal subtle changes in concrete laboratory samples and even large structural elements without having a transducer directly at the place where the change is taking place. Here, several load tests until failure on large posttensioned concrete beams have been monitored using networks of embedded transducers. To detect subtle effects at the beginning of the experiments and cope with severe changes due to cracking close to failure, the coda wave interferometry procedures had to be modified to an adapted step-wise approach. Using this methodology, we were able to monitor stress distribution and localize large cracks by a relatively simple technique. Implementation of this approach on selected real structures might help to make decisions in infrastructure asset management. Full article
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Open AccessFeature PaperArticle
Nondestructive Evaluation of Concrete Bridge Decks with Automated Acoustic Scanning System and Ground Penetrating Radar
Sensors 2018, 18(6), 1955; https://doi.org/10.3390/s18061955
Received: 1 May 2018 / Revised: 9 June 2018 / Accepted: 12 June 2018 / Published: 16 June 2018
Cited by 4 | PDF Full-text (3740 KB) | HTML Full-text | XML Full-text
Abstract
Delamanintions and reinforcement corrosion are two common problems in concrete bridge decks. No single nondestructive testing method (NDT) is able to provide comprehensive characterization of these defects. In this work, two NDT methods, acoustic scanning and Ground Penetrating Radar (GPR), were used to [...] Read more.
Delamanintions and reinforcement corrosion are two common problems in concrete bridge decks. No single nondestructive testing method (NDT) is able to provide comprehensive characterization of these defects. In this work, two NDT methods, acoustic scanning and Ground Penetrating Radar (GPR), were used to image a straight concrete bridge deck and a curved intersection ramp bridge. An acoustic scanning system has been developed for rapid delamination mapping. The system consists of metal-ball excitation sources, air-coupled sensors, and a GPS positioning system. The acoustic scanning results are presented as a two-dimensional image that is based on the energy map in the frequency range of 0.5–5 kHz. The GPR scanning results are expressed as the GPR signal attenuation map to characterize concrete deterioration and reinforcement corrosion. Signal processing algorithms for both methods are discussed. Delamination maps from the acoustic scanning are compared with deterioration maps from the GPR scanning on both bridges. The results demonstrate that combining the acoustic and GPR scanning results will provide a complementary and comprehensive evaluation of concrete bridge decks. Full article
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Open AccessArticle
A Temperature Drift Compensation Method for Pulsed Eddy Current Technology
Sensors 2018, 18(6), 1952; https://doi.org/10.3390/s18061952
Received: 21 May 2018 / Revised: 6 June 2018 / Accepted: 12 June 2018 / Published: 15 June 2018
PDF Full-text (1906 KB) | HTML Full-text | XML Full-text
Abstract
Pulsed eddy current (PEC) technology is another important non-contact nondestructive testing technology for defect detection. However, the temperature drift of the exciting coil has a considerable influence on the precision of PEC testing. The objective of this study is to investigate the temperature [...] Read more.
Pulsed eddy current (PEC) technology is another important non-contact nondestructive testing technology for defect detection. However, the temperature drift of the exciting coil has a considerable influence on the precision of PEC testing. The objective of this study is to investigate the temperature drift effect and reduce its impact. The temperature drift effect is analyzed theoretically and experimentally. The temperature drift effect on the peak-to-peak values of the output signal is investigated, and a temperature compensation method is proposed to reduce the effect of temperature variation. The results show that temperature drift has a negative impact on PEC testing and the temperature compensation method can effectively reduce the effect of temperature drift. Full article
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Open AccessArticle
Preload Monitoring of Bolted L-Shaped Lap Joints Using Virtual Time Reversal Method
Sensors 2018, 18(6), 1928; https://doi.org/10.3390/s18061928
Received: 1 May 2018 / Revised: 5 June 2018 / Accepted: 12 June 2018 / Published: 13 June 2018
Cited by 4 | PDF Full-text (6287 KB) | HTML Full-text | XML Full-text
Abstract
L-shaped bolt lap joints are commonly used in aerospace and civil structures. However, bolt joints are frequently subjected to loosening, and this has a significant effect on the safety and reliability of these structures. Therefore, bolt preload monitoring is very important, especially at [...] Read more.
L-shaped bolt lap joints are commonly used in aerospace and civil structures. However, bolt joints are frequently subjected to loosening, and this has a significant effect on the safety and reliability of these structures. Therefore, bolt preload monitoring is very important, especially at the early stage of loosening. In this paper, a virtual time reversal guided wave method is presented to monitor preload of bolted L-shaped lap joints accurately and simply. In this method, a referenced reemitting signal (RRS) is extracted from the bolted structure in fully tightened condition. Then the RRS is utilized as the excitation signal for the bolted structure in loosening states, and the normalized peak amplitude of refocused wave packet is used as the tightness index (TIA). The proposed method is experimentally validated by L-shaped bolt joints with single and multiple bolts. Moreover, the selections of guided wave frequency and tightness index are also discussed. The results demonstrate that the relationship between TIA and bolt preload is linear. The detection sensitivity is improved significantly compared with time reversal (TR) method, particularly when bolt loosening is at its embryo stage. The results also show that TR method is an effective method for detection of the number of loosening bolts. Full article
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Open AccessArticle
Development of A Low-Cost FPGA-Based Measurement System for Real-Time Processing of Acoustic Emission Data: Proof of Concept Using Control of Pulsed Laser Ablation in Liquids
Sensors 2018, 18(6), 1775; https://doi.org/10.3390/s18061775
Received: 15 April 2018 / Revised: 23 May 2018 / Accepted: 29 May 2018 / Published: 1 June 2018
Cited by 4 | PDF Full-text (364 KB) | HTML Full-text | XML Full-text
Abstract
Today, the demand for continuous monitoring of valuable or safety critical equipment is increasing in many industrial applications due to safety and economical requirements. Therefore, reliable in-situ measurement techniques are required for instance in Structural Health Monitoring (SHM) as well as process monitoring [...] Read more.
Today, the demand for continuous monitoring of valuable or safety critical equipment is increasing in many industrial applications due to safety and economical requirements. Therefore, reliable in-situ measurement techniques are required for instance in Structural Health Monitoring (SHM) as well as process monitoring and control. Here, current challenges are related to the processing of sensor data with a high data rate and low latency. In particular, measurement and analyses of Acoustic Emission (AE) are widely used for passive, in-situ inspection. Advantages of AE are related to its sensitivity to different micro-mechanical mechanisms on the material level. However, online processing of AE waveforms is computationally demanding. The related equipment is typically bulky, expensive, and not well suited for permanent installation. The contribution of this paper is the development of a Field Programmable Gate Array (FPGA)-based measurement system using ZedBoard devlopment kit with Zynq-7000 system on chip for embedded implementation of suitable online processing algorithms. This platform comprises a dual-core Advanced Reduced Instruction Set Computer Machine (ARM) architecture running a Linux operating system and FPGA fabric. A FPGA-based hardware implementation of the discrete wavelet transform is realized to accelerate processing the AE measurements. Key features of the system are low cost, small form factor, and low energy consumption, which makes it suitable to serve as field-deployed measurement and control device. For verification of the functionality, a novel automatically realized adjustment of the working distance during pulsed laser ablation in liquids is established as an example. A sample rate of 5 MHz is achieved at 16 bit resolution. Full article
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Open AccessArticle
Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges
Sensors 2018, 18(5), 1488; https://doi.org/10.3390/s18051488
Received: 25 March 2018 / Revised: 3 May 2018 / Accepted: 7 May 2018 / Published: 9 May 2018
Cited by 1 | PDF Full-text (3093 KB) | HTML Full-text | XML Full-text
Abstract
Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential [...] Read more.
Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential factors to vertical deflection such as concrete creep and shrinkage. However, it is not an easy task because the vertical deflection of a railway bridge generally involves several sources of uncertainty. This paper proposes a probabilistic method that employs a Gaussian process to construct a model to predict the vertical deflection of a railway bridge based on actual vision-based measurement and temperature. To deal with the sources of uncertainty which may cause prediction errors, a Gaussian process is modeled with multiple kernels and hyperparameters. Once the hyperparameters are identified through the Gaussian process regression using training data, the proposed method provides a 95% prediction interval as well as a predictive mean about the vertical deflection of the bridge. The proposed method is applied to an arch bridge under operation for high-speed trains in South Korea. The analysis results obtained from the proposed method show good agreement with the actual measurement data on the vertical deflection of the example bridge, and the prediction results can be utilized for decision-making on railway bridge maintenance. Full article
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Open AccessArticle
Non-Contact Smartphone-Based Monitoring of Thermally Stressed Structures
Sensors 2018, 18(4), 1250; https://doi.org/10.3390/s18041250
Received: 22 February 2018 / Revised: 21 March 2018 / Accepted: 12 April 2018 / Published: 18 April 2018
Cited by 2 | PDF Full-text (38333 KB) | HTML Full-text | XML Full-text
Abstract
The in-situ measurement of thermal stress in beams or continuous welded rails may prevent structural anomalies such as buckling. This study proposed a non-contact monitoring/inspection approach based on the use of a smartphone and a computer vision algorithm to estimate the vibrating characteristics [...] Read more.
The in-situ measurement of thermal stress in beams or continuous welded rails may prevent structural anomalies such as buckling. This study proposed a non-contact monitoring/inspection approach based on the use of a smartphone and a computer vision algorithm to estimate the vibrating characteristics of beams subjected to thermal stress. It is hypothesized that the vibration of a beam can be captured using a smartphone operating at frame rates higher than conventional 30 Hz, and the first few natural frequencies of the beam can be extracted using a computer vision algorithm. In this study, the first mode of vibration was considered and compared to the information obtained with a conventional accelerometer attached to the two structures investigated, namely a thin beam and a thick beam. The results show excellent agreement between the conventional contact method and the non-contact sensing approach proposed here. In the future, these findings may be used to develop a monitoring/inspection smartphone application to assess the axial stress of slender structures, to predict the neutral temperature of continuous welded rails, or to prevent thermal buckling. Full article
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Open AccessArticle
Image Registration-Based Bolt Loosening Detection of Steel Joints
Sensors 2018, 18(4), 1000; https://doi.org/10.3390/s18041000
Received: 14 January 2018 / Revised: 5 March 2018 / Accepted: 12 March 2018 / Published: 28 March 2018
Cited by 14 | PDF Full-text (92592 KB) | HTML Full-text | XML Full-text
Abstract
Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require [...] Read more.
Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts. Full article
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Open AccessArticle
Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning
Sensors 2018, 18(3), 833; https://doi.org/10.3390/s18030833
Received: 12 January 2018 / Revised: 3 March 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
Cited by 1 | PDF Full-text (2970 KB) | HTML Full-text | XML Full-text
Abstract
Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in [...] Read more.
Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load. Full article
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Open AccessArticle
Visualization of Concrete Slump Flow Using the Kinect Sensor
Sensors 2018, 18(3), 771; https://doi.org/10.3390/s18030771
Received: 7 February 2018 / Revised: 27 February 2018 / Accepted: 27 February 2018 / Published: 3 March 2018
Cited by 1 | PDF Full-text (7918 KB) | HTML Full-text | XML Full-text
Abstract
Workability is regarded as one of the important parameters of high-performance concrete and monitoring it is essential in concrete quality management at construction sites. The conventional workability test methods are basically based on length and time measured by a ruler and a stopwatch [...] Read more.
Workability is regarded as one of the important parameters of high-performance concrete and monitoring it is essential in concrete quality management at construction sites. The conventional workability test methods are basically based on length and time measured by a ruler and a stopwatch and, as such, inevitably involves human error. In this paper, we propose a 4D slump test method based on digital measurement and data processing as a novel concrete workability test. After acquiring the dynamically changing 3D surface of fresh concrete using a 3D depth sensor during the slump flow test, the stream images are processed with the proposed 4D slump processing algorithm and the results are compressed into a single 4D slump image. This image basically represents the dynamically spreading cross-section of fresh concrete along the time axis. From the 4D slump image, it is possible to determine the slump flow diameter, slump flow time, and slump height at any location simultaneously. The proposed 4D slump test will be able to activate research related to concrete flow simulation and concrete rheology by providing spatiotemporal measurement data of concrete flow. Full article
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Open AccessArticle
Fatigue Reliability Assessment for Orthotropic Steel Decks Based on Long-Term Strain Monitoring
Sensors 2018, 18(1), 181; https://doi.org/10.3390/s18010181
Received: 7 December 2017 / Revised: 3 January 2018 / Accepted: 6 January 2018 / Published: 10 January 2018
Cited by 2 | PDF Full-text (2685 KB) | HTML Full-text | XML Full-text
Abstract
A time-dependent fatigue reliability assessment approach is proposed for welded details of orthotropic steel decks (OSDs) using long-term strain monitoring data. The fatigue reliability limit function of the welded details is established based on the Eurocode specifications. Depending on the distribution characteristics of [...] Read more.
A time-dependent fatigue reliability assessment approach is proposed for welded details of orthotropic steel decks (OSDs) using long-term strain monitoring data. The fatigue reliability limit function of the welded details is established based on the Eurocode specifications. Depending on the distribution characteristics of the measured daily equivalent stress range, either the lognormal distribution or Gaussian mixture model (GMM) is selected to quantify its uncertainty. Subsequently, the fatigue reliability can be calculated using either an explicit formula or the Monte Carlo method. This proposed approach is applied for the fatigue reliability evaluation of two rib-to-deck and two rib-to-rib welded fatigue details of an in-service suspension bridge. The results show that the reliability indices decrease significantly with bridge’s service life. Except for a rib-to-deck detail, all other three welded details cannot meet the target fatigue reliability during this bridge’s 100-year service life. The proposed approach can help bridge owners and operators make informed decisions regarding maintenance and repair of potential fatigue cracks. Full article
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Open AccessArticle
Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors
Sensors 2018, 18(1), 152; https://doi.org/10.3390/s18010152
Received: 15 November 2017 / Revised: 28 December 2017 / Accepted: 5 January 2018 / Published: 8 January 2018
Cited by 7 | PDF Full-text (7434 KB) | HTML Full-text | XML Full-text
Abstract
A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline [...] Read more.
A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario. Full article
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Open AccessArticle
Structural Health Monitoring of Above-Ground Storage Tank Floors by Ultrasonic Guided Wave Excitation on the Tank Wall
Sensors 2017, 17(11), 2542; https://doi.org/10.3390/s17112542
Received: 18 September 2017 / Revised: 23 October 2017 / Accepted: 31 October 2017 / Published: 4 November 2017
Cited by 6 | PDF Full-text (4687 KB) | HTML Full-text | XML Full-text
Abstract
There is an increasing interest in using ultrasonic guided waves to assess the structural degradation of above-ground storage tank floors. This is a non-invasive and economically viable means of assessing structural degradation. Above-ground storage tank floors are ageing assets which need to be [...] Read more.
There is an increasing interest in using ultrasonic guided waves to assess the structural degradation of above-ground storage tank floors. This is a non-invasive and economically viable means of assessing structural degradation. Above-ground storage tank floors are ageing assets which need to be inspected periodically to avoid structural failure. At present, normal-stress type transducers are bonded to the tank annular chime to generate a force field in the thickness direction of the floor and excite fundamental symmetric and asymmetric Lamb modes. However, the majority of above-ground storage tanks in use have no annular chime due to a simplified design and/or have a degraded chime due to corrosion. This means that transducers cannot be mounted on the chime to assess structural health according to the present technology, and the market share of structural health monitoring of above-ground storage tank floors using ultrasonic guided wave is thus limited. Therefore, the present study investigates the potential of using the tank wall to bond the transducer instead of the tank annular chime. Both normal and shear type transducers were investigated numerically, and results were validated using a 4.1 m diameter above-ground storage tank. The study results show shear mode type transducers bonded to the tank wall can be used to assess the structural health of the above-ground tank floors using an ultrasonic guided wave. It is also shown that for the cases studied there is a 7.4 dB signal-to-noise ratio improvement at 45 kHz for the guided wave excitation on the tank wall using shear mode transducers. Full article
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Open AccessArticle
Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges
Sensors 2017, 17(10), 2317; https://doi.org/10.3390/s17102317
Received: 2 July 2017 / Revised: 3 October 2017 / Accepted: 9 October 2017 / Published: 11 October 2017
Cited by 10 | PDF Full-text (6405 KB) | HTML Full-text | XML Full-text
Abstract
The displacement responses of a civil engineering structure can provide important information regarding structural behaviors that help in assessing safety and serviceability. A displacement measurement using conventional devices, such as the linear variable differential transformer (LVDT), is challenging owing to issues related to [...] Read more.
The displacement responses of a civil engineering structure can provide important information regarding structural behaviors that help in assessing safety and serviceability. A displacement measurement using conventional devices, such as the linear variable differential transformer (LVDT), is challenging owing to issues related to inconvenient sensor installation that often requires additional temporary structures. A promising alternative is offered by computer vision, which typically provides a low-cost and non-contact displacement measurement that converts the movement of an object, mostly an attached marker, in the captured images into structural displacement. However, there is limited research on addressing light-induced measurement error caused by the inevitable sunlight in field-testing conditions. This study presents a computer vision-based displacement measurement approach tailored to a field-testing environment with enhanced robustness to strong sunlight. An image-processing algorithm with an adaptive region-of-interest (ROI) is proposed to reliably determine a marker’s location even when the marker is indistinct due to unfavorable light. The performance of the proposed system is experimentally validated in both laboratory-scale and field experiments. Full article
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Open AccessArticle
An Architecture for On-Line Measurement of the Tip Clearance and Time of Arrival of a Bladed Disk of an Aircraft Engine
Sensors 2017, 17(10), 2162; https://doi.org/10.3390/s17102162
Received: 21 July 2017 / Revised: 12 September 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
Cited by 6 | PDF Full-text (6891 KB) | HTML Full-text | XML Full-text
Abstract
Safety and performance of the turbo-engine in an aircraft is directly affected by the health of its blades. In recent years, several improvements to the sensors have taken place to monitor the blades in a non-intrusive way. The parameters that are usually measured [...] Read more.
Safety and performance of the turbo-engine in an aircraft is directly affected by the health of its blades. In recent years, several improvements to the sensors have taken place to monitor the blades in a non-intrusive way. The parameters that are usually measured are the distance between the blade tip and the casing, and the passing time at a given point. Simultaneously, several techniques have been developed that allow for the inference—from those parameters and under certain conditions—of the amplitude and frequency of the blade vibration. These measurements are carried out on engines set on a rig, before being installed in an airplane. In order to incorporate these methods during the regular operation of the engine, signal processing that allows for the monitoring of those parameters at all times should be developed. This article introduces an architecture, based on a trifurcated optic sensor and a hardware processor, that fulfills this need. The proposed architecture is scalable and allows several sensors to be simultaneously monitored at different points around a bladed disk. Furthermore, the results obtained by the electronic system will be compared with the results obtained by the validation of the optic sensor. Full article
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Open AccessArticle
Sparse Representation Based Frequency Detection and Uncertainty Reduction in Blade Tip Timing Measurement for Multi-Mode Blade Vibration Monitoring
Sensors 2017, 17(8), 1745; https://doi.org/10.3390/s17081745
Received: 21 June 2017 / Revised: 26 July 2017 / Accepted: 26 July 2017 / Published: 30 July 2017
Cited by 8 | PDF Full-text (7303 KB) | HTML Full-text | XML Full-text
Abstract
The accurate monitoring of blade vibration under operating conditions is essential in turbo-machinery testing. Blade tip timing (BTT) is a promising non-contact technique for the measurement of blade vibrations. However, the BTT sampling data are inherently under-sampled and contaminated with several measurement uncertainties. [...] Read more.
The accurate monitoring of blade vibration under operating conditions is essential in turbo-machinery testing. Blade tip timing (BTT) is a promising non-contact technique for the measurement of blade vibrations. However, the BTT sampling data are inherently under-sampled and contaminated with several measurement uncertainties. How to recover frequency spectra of blade vibrations though processing these under-sampled biased signals is a bottleneck problem. A novel method of BTT signal processing for alleviating measurement uncertainties in recovery of multi-mode blade vibration frequency spectrum is proposed in this paper. The method can be divided into four phases. First, a single measurement vector model is built by exploiting that the blade vibration signals are sparse in frequency spectra. Secondly, the uniqueness of the nonnegative sparse solution is studied to achieve the vibration frequency spectrum. Thirdly, typical sources of BTT measurement uncertainties are quantitatively analyzed. Finally, an improved vibration frequency spectra recovery method is proposed to get a guaranteed level of sparse solution when measurement results are biased. Simulations and experiments are performed to prove the feasibility of the proposed method. The most outstanding advantage is that this method can prevent the recovered multi-mode vibration spectra from being affected by BTT measurement uncertainties without increasing the probe number. Full article
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Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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