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Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 35567

Special Issue Editors

Department of Civil Engineering, University of Seoul, Seoul 02504, Korea
Interests: civil engineering; deep learning; digital image processing; wireless sensor; system identification
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Architectural Engineering, Sejong University, Seoul 05006, Korea
Interests: structural health monitoring; nondestructive evaluation; smart structures; active sensing technologies; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As society gets older, civil structures, a main component of society, deteriorate and require maintenance. To assess the current condition of a structure in service, many researchers have been working on structural health monitoring (SHM) and nondestructive testing (NDT) techniques. In the past few decades, various techniques have been developed and applied to real structures, though they have not yet been fully validated for the purpose of comprehensive assessment of civil structures.

Thus, many researchers are still working hard to develop SHM and NDT techniques that can be used in practice with increased facilitation. The developed techniques are generally implemented to numerical models, laboratory-scale structures, and real-scale structures in steps. In this Special Issue, the recent efforts and advances made for the comprehensive SHM and NDT of civil structures will be discussed. The topics of interest for this Special Issue include but are not limited to the following:

  • Innovations in sensors for SHM and NDT;
  • System identification of civil structures using sensors and civionics;
  • Structural health diagnosis and prognosis;
  • Data fusion and analytics;
  • Robotic/UAV platform for structural inspection and preservation;
  • Artificial intelligence for SHM and NDE;
  • SHM-aided reliability analysis and evaluation of structures;
  • Nondestructive testing techniques;
  • Nondestructive evaluation (NDE) of characteristics of construction materials;
  • Multifunctional sensing materials.

Prof. Soojin Cho
Prof. Yun-Kyu An
Guest Editors

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Keywords

  • structural health monitoring
  • nondestructive testing
  • nondestructive evaluation
  • system identification
  • artificial intelligence
  • data fusion
  • robotics
  • UAV
  • civionics
  • reliability
  • sensors
  • sensing materials

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Related Special Issue

Published Papers (11 papers)

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Research

14 pages, 2079 KiB  
Article
Probability-Based Concrete Carbonation Prediction Using On-Site Data
by Hyunjun Jung, Seok-Been Im and Yun-Kyu An
Appl. Sci. 2020, 10(12), 4330; https://doi.org/10.3390/app10124330 - 24 Jun 2020
Cited by 10 | Viewed by 2982
Abstract
This study proposes a probability-based carbonation prediction approach for successful monitoring of deteriorating concrete structures. Over the last several decades, a number of researchers have studied the concrete carbonation prediction to estimate the long-term performance of carbonated concrete structures. Recently, probability-based durability analyses [...] Read more.
This study proposes a probability-based carbonation prediction approach for successful monitoring of deteriorating concrete structures. Over the last several decades, a number of researchers have studied the concrete carbonation prediction to estimate the long-term performance of carbonated concrete structures. Recently, probability-based durability analyses have been introduced to precisely estimate the carbonation of concrete structures. Since the carbonation of concrete structures, however, can be affected by material compositions as well as various environmental conditions, it is still a challenge to predict concrete carbonation in the field. In this study, the Fick’s first law and a Bayes’ theorem-based carbonation prediction approach is newly proposed using on-site data, which were obtained over 19 years. In particular, the effects of design parameters such as diffusion coefficient, concentration, absorption quantity of CO2, and the degree of hydration have been thoroughly considered in this study. The proposed probabilistic approach has shown a reliable prediction of concrete carbonation and remaining service life. Full article
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Figure 1
<p>CO<sub>2</sub> diffusion coefficient according to time.</p>
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<p>Probability distribution of CO<sub>2</sub> diffusion coefficient [<a href="#B22-applsci-10-04330" class="html-bibr">22</a>].</p>
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<p>Hydration rates of main constituents of Portland cement [<a href="#B22-applsci-10-04330" class="html-bibr">22</a>].</p>
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<p>Changes in posterior predicted values of carbonation depth according to changes in the number of Latin hypercube sampling (LHS) values.</p>
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<p>Updated prediction model considering different numbers of on-site data of the Gajwa IC viaduct.</p>
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<p>Sensitivity analysis for the optimized number of on-site data. (<b>a</b>) Gajwa IC viaduct; (<b>b</b>) Noryang bridge; (<b>c</b>) Geoje bridge.</p>
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<p>Changes in prior and posterior predicted values for carbonation depth of the subject bridge. (<b>a</b>) Gajwa IC viaduct; (<b>b</b>) Noryang bridge; (<b>c</b>) Geoje bridge.</p>
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<p>Changes in prior and posterior predicted values for reliability index of the subject bridge. (<b>a</b>) Gajwa IC viaduct; (<b>b</b>) Geoje bridge.</p>
Full article ">
13 pages, 6514 KiB  
Article
Adaptive Subset-Based Digital Image Correlation for Fatigue Crack Evaluation
by Myung Soo Kang and Yun-Kyu An
Appl. Sci. 2020, 10(10), 3574; https://doi.org/10.3390/app10103574 - 21 May 2020
Cited by 4 | Viewed by 2494
Abstract
This paper proposes a fatigue crack evaluation technique based on digital image correlation (DIC) with statistically optimized adaptive subsets. In conventional DIC analysis, a uniform subset size is typically utilized throughout the entire region of interest (ROI), which is determined by experts’ subjective [...] Read more.
This paper proposes a fatigue crack evaluation technique based on digital image correlation (DIC) with statistically optimized adaptive subsets. In conventional DIC analysis, a uniform subset size is typically utilized throughout the entire region of interest (ROI), which is determined by experts’ subjective judgement. The basic assumption of the conventional DIC analysis is that speckle patterns are uniformly distributed within the ROI of a target image. However, the speckle patterns on the ROI are often spatially biased, augmenting spatially different DIC errors. Thus, a subset size optimization with spatially different sizes, called adaptive subset sizes, is needed to improve the DIC accuracy. In this paper, the adaptive subset size optimization algorithm is newly proposed and experimentally validated using an aluminum plate with sprayed speckle patterns which are not spatially uniform. The validation test results show that the proposed algorithm accurately estimates the horizontal displacements of 200 μ m , 500 μ m and 1 mm without any DIC error within the ROI. On the other hand, the conventional subset size determination algorithm, which employs a uniform subset size, produces the maximum error of 33% in the designed specimen. In addition, a real fatigue crack-opening phenomenon, which is a local deformation within the ROI, is evaluated using the proposed algorithm. The fatigue crack-opening phenomenon as well as the corresponding displacement distribution nearby the fatigue crack tip are effectively visualized under the uniaxial tensile conditions of 0.2, 1.0, 1.4 and 1.7 mm , while the conventional algorithm shows local DIC errors, especially at crack opening areas. Full article
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<p>Overview of the automated size determination algorithm of adaptive subsets: <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>q</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>R</mi> <mi>q</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> are the pair reference images. <math display="inline"><semantics> <mrow> <msub> <mi>O</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the seed point on <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>q</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the reference subset centered at <math display="inline"><semantics> <mrow> <msub> <mi>O</mi> <mi>i</mi> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mi>j</mi> </msub> </mrow> </semantics></math> is the size parameter of <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> is <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mn>2</mn> <msub> <mi>M</mi> <mi>j</mi> </msub> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> × <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mn>2</mn> <msub> <mi>M</mi> <mi>j</mi> </msub> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msubsup> <mi>S</mi> <mi>i</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> is the matched subset of <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> </mrow> </semantics></math> centered at <math display="inline"><semantics> <mrow> <msubsup> <mi>O</mi> <mi>i</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>. <math display="inline"><semantics> <mi>D</mi> </semantics></math> is the matching distance between <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>S</mi> <mi>i</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>. <math display="inline"><semantics> <msup> <mi>D</mi> <mo>′</mo> </msup> </semantics></math> is the derivative of <math display="inline"><semantics> <mi>D</mi> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the converging size and <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the adaptive subset size. PDF is the probability density function.</p>
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<p>Experimental setup: (<b>a</b>) data acquisition system, (<b>b</b>) aluminum plate specimen and the target region of interest (ROI).</p>
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<p>Determination results of adaptive subset sizes: (<b>a</b>) the number of adaptive subset sizes according to the subset length and (<b>b</b>) spatial distribution of the adaptive subset sizes.</p>
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<p>Validation test results of the adaptive subset sizes: horizontal displacements of (<b>a</b>) 200 μm, (<b>b</b>) 500 μm and (<b>c</b>) 1 mm.</p>
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<p>Randomly selected two different seed points within the ROI of the aluminum specimen.</p>
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<p>Determination of subset sizes using subset intensity gradient (SSSIG) at the randomly selected two different seed points: (<b>a</b>) seed point 1 and (<b>b</b>) seed point 2.</p>
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<p>Validation test results of SSSIG: the subset size of 7 × 7 pixels with a horizontal displacement of (<b>a</b>) 200 μm, (<b>b</b>) 500 μm and (<b>c</b>) 1 mm. The subset size of 9 × 9 pixels with a horizontal displacement of (<b>d</b>) 200 μm, (<b>e</b>) 500 μm and (<b>f</b>) 1 mm.</p>
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<p>Experimental setup: (<b>a</b>) data acquisition system and (<b>b</b>) the ROI on the fatigue crack specimen.</p>
Full article ">Figure 9
<p>Determination results of adaptive subset sizes: (<b>a</b>) the number of adaptive subset sizes according to the subset length and (<b>b</b>) spatial distribution of the adaptive subset sizes.</p>
Full article ">Figure 10
<p>Digital image correlation (DIC) analysis results using the proposed algorithm under the uniaxial tensile loads of: (<b>a</b>) 0.2, (<b>b</b>) 1.0, (<b>c</b>) 1.4 and (<b>d</b>) 1.7 mm.</p>
Full article ">Figure 11
<p>Randomly selected two seed points in the ROI of the fatigue crack specimen.</p>
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<p>Determination of subset sizes using SSSIG at the randomly selected two different seed points: (<b>a</b>) seed point 1 and (<b>b</b>) seed point 2.</p>
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<p>DIC analysis results using SSSIG. The subset size of 13 × 13 pixels with uniaxial tensile conditions of: (<b>a</b>) 0.2, (<b>b</b>) 1.0, (<b>c</b>) 1.4 and (<b>d</b>) 1.7 mm; the subset size of 19 × 19 pixels with uniaxial tensile conditions of (<b>e</b>) 0.2, (<b>f</b>) 1.0, (<b>g</b>) 1.4 and (<b>h</b>) 1.7 mm.</p>
Full article ">Figure 13 Cont.
<p>DIC analysis results using SSSIG. The subset size of 13 × 13 pixels with uniaxial tensile conditions of: (<b>a</b>) 0.2, (<b>b</b>) 1.0, (<b>c</b>) 1.4 and (<b>d</b>) 1.7 mm; the subset size of 19 × 19 pixels with uniaxial tensile conditions of (<b>e</b>) 0.2, (<b>f</b>) 1.0, (<b>g</b>) 1.4 and (<b>h</b>) 1.7 mm.</p>
Full article ">
10 pages, 4269 KiB  
Article
Effect of High-Speed Train-Induced Wind on Trackside UAV Thrust Near Railway Bridge
by Hyuk-Jin Yoon, Su-Hwan Yun, Dae-Hyun Kim, Jae Hee Kim, Bong-Kwan Cho, Gi-Gun Lee, Soon-Eung Park and Young-Chul Kim
Appl. Sci. 2020, 10(10), 3495; https://doi.org/10.3390/app10103495 - 18 May 2020
Cited by 7 | Viewed by 3479
Abstract
Imaging devices attached to unmanned aerial vehicles (UAVs) are used for crack measurements of railway bridges constructed for high-speed trains. This research aims to investigate track-side wind induced by high-speed trains and its effect on UAV thrust near the railway bridge. Furthermore, the [...] Read more.
Imaging devices attached to unmanned aerial vehicles (UAVs) are used for crack measurements of railway bridges constructed for high-speed trains. This research aims to investigate track-side wind induced by high-speed trains and its effect on UAV thrust near the railway bridge. Furthermore, the characteristics of train-induced wind in three axial directions along a track, wind velocity, and the effect of train-induced wind on the UAV thrust were analyzed. This was achieved by installing 3-axis ultrasonic anemometers and a UAV thrust measurement system on top of a PSC box girder bridge. The changes in the train-induced wind velocity were monitored along the train travel, width, and height directions. The train-induced wind was measured at distances of 0.8, 1.3, 2.3, and 2.8 m away from the train’s body to analyze wind velocity based on distance. It was found that the maximum wind velocity decreased linearly as the distance from the train’s body increased. The UAV thrust increased by up to 20% and 60%, owing to train-induced wind when the leading and trailing power cars of a high-speed train passed, respectively. Thus, it is necessary to conduct further research to develop robust control and a variable pitch-propeller that can control thrust. Full article
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<p>Schematics of the train-induced wind and unmanned aerial vehicle (UAV) thrust measurement systems installed on top of the Wolsan bridge.</p>
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<p>Installation locations of the 3-axis ultrasonic anemometers and UAV thrust measurement system. (<b>a</b>) Distances between the 3-axis ultrasonic anemometers and the train. (<b>b</b>) Installed train-induced wind and UAV thrust measurement systems.</p>
Full article ">Figure 2 Cont.
<p>Installation locations of the 3-axis ultrasonic anemometers and UAV thrust measurement system. (<b>a</b>) Distances between the 3-axis ultrasonic anemometers and the train. (<b>b</b>) Installed train-induced wind and UAV thrust measurement systems.</p>
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<p>Two types of high-speed trains used in the experiment (<b>a</b>) KTX; (<b>b</b>) SRT.</p>
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<p>Train-induced wind velocity over time measured at a 0.8 m distance from the train surface.</p>
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<p>Train-induced wind velocity over time measured at 0.8 m from the train surface in each direction. (<b>a</b>) Train travel direction; (<b>b</b>) train width direction; (<b>c</b>) height direction.</p>
Full article ">Figure 5 Cont.
<p>Train-induced wind velocity over time measured at 0.8 m from the train surface in each direction. (<b>a</b>) Train travel direction; (<b>b</b>) train width direction; (<b>c</b>) height direction.</p>
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<p>Maximum train-induced wind velocity according to the distance from the train surface during the passage of KTX trains.</p>
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<p>Train-induced wind velocity measured at 0.8, 1.3, 2.3, and 2.8 m distances from the train surface in each direction. (<b>a</b>) Train travel direction; (<b>b</b>) train width direction; (<b>c</b>) height direction.</p>
Full article ">Figure 7 Cont.
<p>Train-induced wind velocity measured at 0.8, 1.3, 2.3, and 2.8 m distances from the train surface in each direction. (<b>a</b>) Train travel direction; (<b>b</b>) train width direction; (<b>c</b>) height direction.</p>
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<p>UAV thrust measured at the trackside on top of the Wolsan bridge during the passage of a KTX train.</p>
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17 pages, 3052 KiB  
Article
A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study
by Hoofar Shokravi, Hooman Shokravi, Norhisham Bakhary, Seyed Saeid Rahimian Koloor and Michal Petrů
Appl. Sci. 2020, 10(9), 3132; https://doi.org/10.3390/app10093132 - 30 Apr 2020
Cited by 22 | Viewed by 3141
Abstract
Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of [...] Read more.
Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC. Full article
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<p>Diagram of the stochastic subspace identification (SSI)-DATA algorithm.</p>
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<p>The flowchart to calculate principle component (PC), unweighted principle component (UPC), and canonical variate analysis (CVA) weight matrices.</p>
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<p>The lamped mass model of the numerical case study.</p>
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<p>Principle angles in function of the block rows number (j) for subspace algorithms using CVA.</p>
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<p>The mode shapes and natural frequencies of the simulation mass–spring–damper system without considering damping effects.</p>
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<p>The variance accounted for (VAF) values for (<b>a</b>) CVA, (<b>b</b>) UPC, and (<b>c</b>) PC algorithms.</p>
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<p>The poles of noise-free simulation model in complex plane.</p>
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<p>The estimated poles of the numerical model for: (<b>a</b>) 1st and 2nd and (<b>b</b>) 3rd and 4th orders in PC algorithm. (<b>c</b>) 1st and 2nd and (<b>d</b>) 3rd and 4th orders in UPC algorithm. (<b>e</b>) 1st and 2nd and (<b>f</b>) 3rd and 4th orders in CVA algorithm.</p>
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<p>The ambient testing of the Alamosa Canyon Bridge. (a) Crossing of vehicles over the bridge. (b) Passing vehicle from the adjacent bridge [<a href="#B46-applsci-10-03132" class="html-bibr">46</a>].</p>
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<p>The estimated poles of the numerical model for: (<b>a</b>) 1st and 2nd and (<b>b</b>) 3rd and 4th orders in PC algorithm. (<b>c</b>) 1st and 2nd and (<b>d</b>) 3rd and 4th orders in UPC algorithm. (<b>e</b>) 1st and 2nd and (<b>f</b>) 3rd and 4th orders in CVA algorithm.</p>
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12 pages, 3782 KiB  
Article
Sonic and Impact Test for Structural Assessment of Historical Masonry
by Alessandro Grazzini
Appl. Sci. 2019, 9(23), 5148; https://doi.org/10.3390/app9235148 - 28 Nov 2019
Cited by 17 | Viewed by 3198
Abstract
Diagnostics is a very important tool of knowledge in the field of historical buildings. In particular, non-destructive techniques allow to deepen the study of the mechanical characteristics of the historical walls without compromising the artistic value of the monumental building. A case study [...] Read more.
Diagnostics is a very important tool of knowledge in the field of historical buildings. In particular, non-destructive techniques allow to deepen the study of the mechanical characteristics of the historical walls without compromising the artistic value of the monumental building. A case study of the use of sonic and impact tests was described, performed using the same instrumented hammer, for the characterization of the masonry walls at the Sanctuary of Santa Maria delle Grazie at Varoni, one of the churches damaged in the 2016 Amatrice earthquake. Sonic tests showed the presence of a discontinous masonry texture, as well as confirming the ineffectiveness of the strengthening work made by injections of lime mortar. The impact test allowed us to obtain the elastic modulus of the omogeneous stones of the masonry. The results obtained from the non-destructive techniques were confirmed by the flat jacks test carried out on the building, confirming the great potential of the non-destructive diagnostics suitable for analyzing important structural parameters without affecting the preservation of historical masonry structures. Full article
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<p>(<b>a</b>) Shoring works on the facade of the Sanctuary of Santa Maria delle Grazie at Varoni after 2016 Central Italy earthquake; (<b>b</b>) Interior of the single nave of the church.</p>
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<p>(<b>a</b>) Signs of the execution of mortar injections on the walls of the church; (<b>b</b>) Second iron tie carried out in pairs with the one existing at the triumphal arch.</p>
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<p>(<b>a</b>) Electric instrumented hammer during the sonic test; (<b>b</b>) Monoaxial accelerometer applied in the other surface of the analysed masonry wall.</p>
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<p>(<b>a</b>) Endoscopy test performed in the same area of the sonic test; (<b>b</b>) Double flat jacks test.</p>
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<p>(<b>a</b>) Endoscopy surveys; (<b>b</b>) Initial section of compact masonry; (<b>c</b>) First discontinuity; (<b>d</b>) Central discontinuity between two walls.</p>
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<p>(<b>a</b>) Example of a force-time diagram; (<b>b</b>) force-time diagram of the impact test at the Sanctuary of Santa Maria delle Grazie.</p>
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<p>Impact test on single big sandstone constituting the masonry texture.</p>
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<p>Movable mass in contact with the test surface.</p>
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14 pages, 3043 KiB  
Article
Wind Load Characteristics of Wind Barriers Induced by High-Speed Trains Based on Field Measurements
by Yunfeng Zou, Zhengyi Fu, Xuhui He, Chenzhi Cai, Jia Zhou and Shuai Zhou
Appl. Sci. 2019, 9(22), 4865; https://doi.org/10.3390/app9224865 - 13 Nov 2019
Cited by 21 | Viewed by 3965
Abstract
This paper focuses on field measurements and analyses of train-generated wind loads on wind barriers (3.0 m height and porosity 0%) with respect to different running speeds of the CRH380A EMU vehicle. Multi-resolution analysis was conducted to identify the pressure distribution in different [...] Read more.
This paper focuses on field measurements and analyses of train-generated wind loads on wind barriers (3.0 m height and porosity 0%) with respect to different running speeds of the CRH380A EMU vehicle. Multi-resolution analysis was conducted to identify the pressure distribution in different frequency bands. Results showed that the wind pressure on the wind barrier caused by train-induced wind had two significant impacts with opposite directions, which were the “head wave” and “tail wave”. The peak wind pressure on the wind barrier was approximately proportional to the square of the speed of the train, and the peak wind pressure decreased rapidly along the wind barrier height from the bottom of the wind barrier. The maximum wind pressure occurred at the rail surface height. Furthermore, results of the multi-resolution analysis illustrated that the energy of the frequency band from 0 to 2.44 Hz accounted for 94% of the total energy. This indicated that the low-frequency range component of the wind pressure dominated the design of the wind barrier. The frequency of pulse excitation of train-induced wind loads may overlap with the natural frequency of barriers, and may lead to fatigue failure due to cyclic loads generated by the repeated passage of high-speed trains. In addition, the speed of the train had a negligible effect on the energy distribution of the wind pressure in the frequency domain, while the extreme pressure increased slightly with the increase of the speed of the train. Full article
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<p>Schematic drawing of the Xijiang Bridge (unit: mm).</p>
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<p>Photograph of the field test site.</p>
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<p>Locations of the wind barrier and test points (unit: mm).</p>
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<p>Remote control data acquisition system.</p>
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<p>Schematic diagram of the wavelet transform (WT) decomposition process.</p>
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<p>Measured wind pressure with respect to different test points (train speed = 275 km/h).</p>
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<p>Peak wind pressure with respect to different heights (train speed = 275 km/h).</p>
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<p>The peak wind pressures of point 9 under different train speeds.</p>
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<p>Wind pressure components of different decomposition layers.</p>
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<p>Extreme pressures of different decomposition layers.</p>
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<p>Comparison of extreme pressures of different decomposition layers under different running speeds.</p>
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<p>The power spectrum of wind pressure at test point 9.</p>
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<p>Percentage of low-frequency energy in total energy.</p>
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11 pages, 3089 KiB  
Article
Automated Real-Time Assessment of Stay-Cable Serviceability Using Smart Sensors
by Seunghoo Jeong, Young-Joo Lee, Do Hyoung Shin and Sung-Han Sim
Appl. Sci. 2019, 9(20), 4469; https://doi.org/10.3390/app9204469 - 22 Oct 2019
Cited by 7 | Viewed by 2872
Abstract
The number of cable-stayed bridges being built worldwide has been increasing owing to the increasing demand for long-span bridges. As the stay-cable is one of critical load-carrying members of cable-stayed bridges, its maintenance has become a significant issue. The stay-cable has an inherently [...] Read more.
The number of cable-stayed bridges being built worldwide has been increasing owing to the increasing demand for long-span bridges. As the stay-cable is one of critical load-carrying members of cable-stayed bridges, its maintenance has become a significant issue. The stay-cable has an inherently low damping ratio with high flexibility, which makes it vulnerable to vibrations owing to wind, rain, and traffic. Excessive vibration of the stay-cable can cause long-term fatigue problems in the stay-cable as well as the cable-stayed bridge. Therefore, civil engineers are required to carry out maintenance measures on stay-cables as a high priority. For the maintenance of the stay-cables, an automated real-time serviceability assessment system using wireless smart sensors was developed in this study. When the displacement of the cable in the mid-span exceeds either the upper or the lower bound provided in most bridge design codes, it is considered as a serviceability failure. The system developed in this study features embedded on-board processing, including the measurement of acceleration, estimation of displacement from measured acceleration, serviceability assessment, and monitoring through wireless communication. A series of laboratory tests were carried out to verify the performance of the developed system. Full article
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<p>Displacement estimation scheme using overlapping time window.</p>
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<p>Design of wireless automated real-time cable serviceability assessment system.</p>
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<p>Flowchart for automated real-time cable serviceability assessment.</p>
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<p>Laboratory setup for monitoring serviceability.</p>
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<p>Automated real-time serviceability assessment results for 30 s.</p>
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<p>Wireless communication results of serviceability assessment for serviceability threshold of 1 mm.</p>
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14 pages, 6170 KiB  
Article
Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
by Byunghyun Kim and Soojin Cho
Appl. Sci. 2019, 9(20), 4444; https://doi.org/10.3390/app9204444 - 19 Oct 2019
Cited by 1 | Viewed by 2722
Abstract
In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult [...] Read more.
In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view. Full article
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<p>Overall framework of the proposed solution for hyperspectral super-resolution when spatial information of an Red-Green-Blue (RGB) image and a hyperspectral image (HSI) is mismatched.</p>
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<p>Example of pixel mismatch environment created by image processing: (<b>a</b>) Original RGB image reconstructed from an HSI from the Harvard dataset [<a href="#B34-applsci-09-04444" class="html-bibr">34</a>] with camera sensitivity for a Nikon D700, (<b>b</b>) mismatched image where 50 pixels on the upper and left sides are cut off; (<b>c</b>) color mismatched RGB image reconstructed using a Nokia N900.</p>
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<p>RGB camera sensitivity for RGB image reconstruction: camera sensitivity for (<b>a</b>) a Nokia N900 and (<b>b</b>) a Nikon D700.</p>
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<p>Example of a per-pixel RMSE (root–mean–square error) image from the Harvard dataset [<a href="#B34-applsci-09-04444" class="html-bibr">34</a>]: (<b>a</b>) Image where translation is applied by cutting off and (<b>b</b>) an image in which the histogram is not matched with that of an RGB image.</p>
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<p>Example of pixel mismatch representation using (<b>a</b>) cutting off (<math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>n</mi> </msub> </mrow> </semantics></math> indicates the number of pixels cut off from the left and upper sides) and (<b>b</b>) shear transformation.</p>
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<p>Example of per-pixel RMSE images and reconstructed RGB images of high-resolution HSIs: (<b>a</b>) Per-pixel RMSE image where a shear transformation with <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>x</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.3 is applied, (<b>b</b>) a per-pixel RMSE image where translation with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>n</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 80 is applied, (<b>c</b>) RGB image an where shear transformation with <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>x</mi> </msub> <mo>=</mo> </mrow> </semantics></math> 0.3 is applied, and (<b>d</b>) RGB image where translation with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>n</mi> </msub> <mo>=</mo> <mo> </mo> </mrow> </semantics></math>80 is applied.</p>
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12 pages, 3769 KiB  
Article
Model-Free Method for Damage Localization of Grid Structure
by Qiuwei Yang, Chaojun Wang, Na Li, Shuai Luo and Wei Wang
Appl. Sci. 2019, 9(16), 3252; https://doi.org/10.3390/app9163252 - 9 Aug 2019
Cited by 4 | Viewed by 2244
Abstract
A model-free damage identification method for grid structures based on displacement difference is proposed. The inherent relationship between the displacement difference and the position of structural damage was deduced in detail by the Sherman–Morrison–Woodbury formula, and the basic principle of damage localization of [...] Read more.
A model-free damage identification method for grid structures based on displacement difference is proposed. The inherent relationship between the displacement difference and the position of structural damage was deduced in detail by the Sherman–Morrison–Woodbury formula, and the basic principle of damage localization of the grid structure was obtained. That is, except for the tensile and compressive deformations of the damaged elements, the deformations of other elements were small, and only rigid body displacements occurred before and after the structural damage. According to this rule, a method for identifying the position of the damage was proposed for the space grid structure by using the rate of change of length for each element. Taking a space grid structure with a large number of elements as an example, the elastic modulus reduction method was used to simulate the damage to the elements, and the static and dynamic test parameters were simulated respectively to obtain the difference in displacement before and after the structural damage. The rate of change of length of each element was calculated based on the obtained displacement difference, and data noise was added to the simulation. The results indicated that the element with the larger length change rate in the structure was the most likely to be damaged, and the damaged element can be accurately evaluated even in the presence of noise in data. Full article
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<p>Grid structure with 11 elements.</p>
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<p>Force of element 3 (Fr is resultant force).</p>
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<p>(<b>a</b>). X-Y axis elevation structure and force; (<b>b</b>). Standard view of the space grid structure of element.</p>
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<p>Rate of change of element length obtained from the static data (element 5 had damage with no noise).</p>
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<p>Rate of change of element length obtained from the static data (elements 35 and 40 were damaged with no noise).</p>
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<p>Length change rate of element obtained from the static data (element 5 was damaged with an added 5% noise).</p>
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<p>Length change rate of element obtained from the static data (elements 35 and 40 were damaged with an added 5% noise).</p>
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<p>Length change rate of element obtained from the dynamic data (element 5 was damaged with no noise).</p>
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<p>Length change rate of element obtained from the dynamic data (elements 35 and 40 were damaged with no noise).</p>
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<p>Length change rate of element obtained from the dynamic data (element 5 was damaged adding 5% noise).</p>
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<p>Length change rate of element obtained from the dynamic data (elements 35 and 40 were damaged adding 5% noise).</p>
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<p>Length change rate of element obtained from the static data with an added 5% noise (element 44 has 20%, 15%, and 10% stiffness reductions).</p>
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<p>Length change rate of element obtained from the static data with an added 5% noise (element 44 has 7.5% and 5% stiffness reductions).</p>
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16 pages, 2604 KiB  
Article
An EKF-Based Method and Experimental Study for Small Leakage Detection and Location in Natural Gas Pipelines
by Qingmin Hou and Weihang Zhu
Appl. Sci. 2019, 9(15), 3193; https://doi.org/10.3390/app9153193 - 5 Aug 2019
Cited by 8 | Viewed by 3786
Abstract
Small leaks in natural gas pipelines are hard to detect, and there are few studies on this problem in the literature. In this paper, a method based on the extended Kalman filter (EKF) is proposed to detect and locate small leaks in natural [...] Read more.
Small leaks in natural gas pipelines are hard to detect, and there are few studies on this problem in the literature. In this paper, a method based on the extended Kalman filter (EKF) is proposed to detect and locate small leaks in natural gas pipelines. First, the method of a characteristic line is used to establish a discrete model of transient pipeline flow. At the same time, according to the basic idea of EKF, a leakage rate is distributed to each segment of the discrete model to obtain a model with virtual multi-point leakage. As such, the virtual leakage rate becomes a component of the state variables in the model. Secondly, system noise and measurement noise are considered, and the optimal hydraulic factors such as leakage rate are estimated using EKF. Finally, by using the idea of an equivalent pipeline, the actual leakage rate is calculated and the location of leakage on the pipeline is assessed. Simulation and experimental results show that this method can consistently predict the leakage rate and location and is sensitive to small leakages in a natural gas pipeline. Full article
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<p>A real leakage in a continuous pipeline model.</p>
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<p>Virtual multi-point leakages in a discrete pipeline model.</p>
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<p>Discretization for virtual multi-point leakage in a pipeline.</p>
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<p>Pressure simulation data.</p>
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<p>Estimated leak rate using simulation data. EKF: extended Kalman filter.</p>
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<p>Estimated leak location using simulation data.</p>
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<p>The system diagram of the gas pipeline leak detection experiment testbed.</p>
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<p>Photos of the gas pipeline leak detection testbed.</p>
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<p>Estimated leakage rate using experimental data.</p>
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<p>Estimated leak position using experimental data.</p>
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14 pages, 7993 KiB  
Article
Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
by Shiuh-Chuan Her and Shin-Chieh Chung
Appl. Sci. 2019, 9(15), 2956; https://doi.org/10.3390/app9152956 - 24 Jul 2019
Cited by 21 | Viewed by 3497
Abstract
An optical fiber sensing system integrating a fiber Bragg grating (FBG) sensor, a long-period fiber grating (LPFG) optical filter and a photodetector is presented to monitor the dynamic response of a structure subjected to base excitation and impact loading. The FBG sensor is [...] Read more.
An optical fiber sensing system integrating a fiber Bragg grating (FBG) sensor, a long-period fiber grating (LPFG) optical filter and a photodetector is presented to monitor the dynamic response of a structure subjected to base excitation and impact loading. The FBG sensor is attached to a test specimen and connected to an LPFG filter. As the light reflected from the FBG sensor is transmitted through the long-period fiber grating filter, the intensity of the light is modulated by the wavelength, which is affected by the strain of the FBG. By measuring the intensity of the light using a photodetector, the wavelength reflected from the FBG sensor can be demodulated, thus leading to the determination of the strain in the structure. To demonstrate its effectiveness, the proposed sensing system was employed to measure the dynamic strain of a beam subjected to mechanical testing. The mechanical tests comprised three load scenarios: base excitation by a shaker at resonant frequency, impact loading by a hammer and shock test on a drop table. To monitor the dynamic strain during the test and validate the accuracy of the measurement of the FBG sensor, strain gauge was used as reference. Experimental results show good correlation between the measurements of FBG sensor and strain gauge. The present work provides a fast response and easy-to-implement optical fiber sensing system for structural health monitoring based on real-time dynamic strain measurements. Full article
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<p>Transmission and reflectance spectrum from fiber Bragg grating.</p>
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<p>Transmission spectrum of LPFG filter.</p>
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<p>Schematic diagram of the integrated optical fiber sensing system.</p>
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<p>Wavelength of FBG sensor.</p>
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<p>Schematic diagram of FBG-LPFG spectra.</p>
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<p>Relation between the strain and normalized light intensity.</p>
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<p>Base excitation test on a cantilever beam. (<b>a</b>) schematic diagram; (<b>b</b>) experimental setup.</p>
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<p>Vibrational response of a cantilever beam subjected to single frequency base excitation.</p>
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<p>Vibrational response measured by Mach-Zehnder interferometric optical sensor [<a href="#B43-applsci-09-02956" class="html-bibr">43</a>].</p>
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<p>Vibrational response of a cantilever beam subjected to dual frequencies base excitation.</p>
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<p>Cantilever beam impacted by a hammer. (<b>a</b>) schematic diagram; (<b>b</b>) experimental setup.</p>
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<p>Transient response of a cantilever beam subjected to impact loading.</p>
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<p>Frequency responses of the cantilever beam (x: frequency; y: amplitude).</p>
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<p>Free-fall drop table.</p>
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<p>Four half-spheres served as the impact target.</p>
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<p>Acceleration of the drop table.</p>
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<p>Transient response of a beam subjected to the drop test.</p>
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