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Advance of Structural Health Monitoring in Civil Engineering
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Advance of Structural Health Monitoring in Civil Engineering

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 28114

Special Issue Editors

Dr. Chung Song

E-Mail Website
Guest Editor
Geotechnical Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
Interests: field instrumentation; advanced analysis based on multiphysics and multiscale approach
Prof. Dr. S. Sonny Kim

E-Mail Website
Co-Guest Editor
School of Environmental, Civil, Agricultural, and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA
Interests: tidal marsh soils; transportation geotechnics; nondestructive remote sensing and machine learning application in geomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dramatic advancements in structural health monitoring in Civil Engineering have been made over the last few decades. Structural health monitoring has also become much more popular because monitoring sensors have become more compatible with field applications, economically viable, and better understood among engineers. Well-executed health monitoring systems could provide warnings for potential failures or could enable substantial savings in budgets by optimizing the construction process in some cases. However, poorly executed monitoring systems could create confusion amongst engineers and have disastrous consequences.

The aim of "Advances in Structural Health Monitoring in Civil Engineering" is to provide up-to-date knowledge of structural health monitoring sensors and the usage of sensors. Articles that provide information such as case studies of structural health monitoring, innovative and fast data analysis methods, the pros and cons of different sensors, the evaluation of current practice, the assessment and usage of indirect (soft, non-contact) sensors such as cell phone signals and aerial photos, overarching sensing methods such as ubiquitous systems by merging conventional direct sensors and indirect sensors, and other relevant issues in the broad discipline of Civil Engineering are welcome.

This Special Issue will provide the current practice and new perspectives in structural health monitoring in Civil Engineering so this new area in Civil Engineering may advance to a new paradigm.

Dr. Chung Song
Prof. Dr. S. Sonny Kim
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • sensor
  • strain gauge
  • structural health monitoring
  • instrumentation in civil engineering
  • ubiquitous system

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Published Papers (13 papers)

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Research

13 pages, 4570 KiB  
Article
The Influence of the Hardness of the Tested Material and the Surface Preparation Method on the Results of Ultrasonic Testing
by Jakub Kowalczyk, Marian Jósko, Daniel Wieczorek, Kamil Sędłak and Michał Nowak
Appl. Sci. 2023, 13(17), 9904; https://doi.org/10.3390/app13179904 - 1 Sep 2023
Cited by 6 | Viewed by 1624
Abstract
Non-destructive ultrasonic testing can be used to assess the properties and condition of real machine elements during their operation, with limited (one-sided) access to these elements. A methodological question then arises concerning the influence of the material properties of such elements and the [...] Read more.
Non-destructive ultrasonic testing can be used to assess the properties and condition of real machine elements during their operation, with limited (one-sided) access to these elements. A methodological question then arises concerning the influence of the material properties of such elements and the condition of their surfaces on the result of ultrasonic testing. This paper attempts to estimate the influence of material hardness and surface roughness on the result of such testing study area testing machine or plant components of unknown exact thickness. Ultrasonic testing was carried out on specially prepared steel samples. These samples had varying surface roughness (Ra from 0.34 to 250.73 µm) of the reflection surface of the longitudinal ultrasonic wave (the so-called reflectors) and hardness (32 and 57 HRC). The ultrasonic measures were the attenuation of the wave, estimated by the decibel drop in the gain of its pulses, and the propagation velocity of the longitudinal ultrasonic wave. Ultrasonic transducers (probes) of varying frequencies (from 2 to 20 MHz), excited by a laboratory and industrial defectoscope were used as the source of such a wave. The results of our research provide a basis for the recommendation of two considered ultrasonic quantities for assessing the material properties of the tested element. This is of particular importance when testing machines or plant components of unknown exact thickness and unknown roughness of inaccessible surfaces, which are the reflectors of the longitudinal ultrasonic wave used for testing. It has been demonstrated that by using the ultrasonic echo technique, it is possible to evaluate the roughness and hardness of the tested elements. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>View of the underside of the sample, with thirteen different fields of varying roughness and the coordinates of the measurement points plotted.</p> Full article ">Figure 2
<p>View of example areas with different roughness; (<b>a</b>) 0.1 mm milling, (<b>b</b>) zoomed view of milled area, (<b>c</b>) processing with sandpaper 100, and (<b>d</b>) zoomed view of area roughened with sandpaper 100.</p> Full article ">Figure 2 Cont.
<p>View of example areas with different roughness; (<b>a</b>) 0.1 mm milling, (<b>b</b>) zoomed view of milled area, (<b>c</b>) processing with sandpaper 100, and (<b>d</b>) zoomed view of area roughened with sandpaper 100.</p> Full article ">Figure 3
<p>Roughness distribution on the surface of the sample with the coordinates plotted according to <a href="#applsci-13-09904-f001" class="html-fig">Figure 1</a>; (<b>a</b>) area with roughness less than 3 µm; (<b>b</b>) area with roughness greater than 50 µm.</p> Full article ">Figure 4
<p>The measurement setup—ultrasonic probe and sample with varying (regular and irregular) roughness of the reflecting surface; R<sub>a</sub>—surface roughness, λ—ultrasonic wavelength for 20 MHz wave frequency.</p> Full article ">Figure 5
<p>View of measuring system and sample on the test bench; (<b>a</b>) industrial defectoscope USM35XS, (<b>b</b>) laboratory defectoscope UMT15 in PC.</p> Full article ">Figure 6
<p>Schematic diagram for measuring and determining of the decibel gain drop based on adjacent impulses of ultrasonic wave reflection when measuring materials of different hardness; I—first echo of the ultrasonic longitudinal wave from the reflector (B); II—second echo of the ultrasonic longitudinal wave from the reflector (B); III—third echo of the ultrasonic longitudinal wave from the reflector (B); B—reflector; H<sub>I</sub>—the height of the first pulse on the flaw detector screen (in this case HI = 80%), H<sub>II</sub>—the height of the second pulse on the ultrasonic flaw detector screen; H<sub>III</sub>—the height of the third pulse on the ultrasonic flaw detector screen.</p> Full article ">Figure 7
<p>Values of measurement errors depending on the frequency of the ultrasonic probe.</p> Full article ">Figure 8
<p>The results of all ultrasonic measurements depending on the frequency of the ultrasonic probe and surface roughness.</p> Full article ">Figure 9
<p>Distribution of the average velocities of the longitudinal ultrasonic wave for the probes used and for specimens with different hardnesses.</p> Full article ">Figure 10
<p>Courses of the decibel gain difference of longitudinal ultrasonic wave pulses depending on the frequency of transducers generating it for tested hardnesses (57 and 32 HRC) of steel samples.</p> Full article ">
21 pages, 6439 KiB  
Article
Simulation Study on Axial Location Identification of Damage in Layered Pipeline Structures Based on Damage Index
by Ying Li, Lingzhi Qu and Baoxin Qi
Appl. Sci. 2023, 13(15), 8850; https://doi.org/10.3390/app13158850 - 31 Jul 2023
Viewed by 1220
Abstract
This study investigates the feasibility of identifying the axial position of circumferential defects in laminated pipeline structures based on damage indices. Wavelet packet decomposition is combined with damage indices, and the effects of dual defects with the same circumferential position but different axial [...] Read more.
This study investigates the feasibility of identifying the axial position of circumferential defects in laminated pipeline structures based on damage indices. Wavelet packet decomposition is combined with damage indices, and the effects of dual defects with the same circumferential position but different axial positions, as well as dual defects with different circumferential and axial positions, on damage indices are separately studied. Our aim was to determine the potential to use damage indices to identify the axial position of circumferential defects in laminated pipeline structures. ABAQUS finite element analysis software was used to establish models of laminated pipeline structures with single defects and dual defects (with the same circumferential position but different axial positions, and with different circumferential and axial positions). The laminated pipeline structure was composed of a steel pipe (structural layer), a rigid polyurethane foam (insulation layer), and a high-density polyethylene (anticorrosion layer). The received sensing signals were averaged, and subjected to 5-level wavelet packet decomposition, to calculate the damage index values, which were then organized into a damage index matrix. Based on the trend of changes in the damage index matrix, the effects of variations in the number and circumferential position of the defects on the identification of the axial position of the damage were analyzed. The results indicate that the trend in damage index changes is influenced by the number of defects, and the increase in the circumferential distance between the second and the piezoelectric element sensor. This study found that when 1.7λ≤PD≤3.4λ, Idouble defect 90°<Isingle defect<Idouble defect 0°; when 3.7λ≤PD≤4λ, Idouble defect 90°<0.3<Idouble defect 0°<Isingle defect. This article demonstrates that the identification of the axial position of damage in laminated pipeline structures can be achieved using the damage index values in the damage index matrix. Additionally, this damage identification method overcomes the limitation of the wavelet packet’s inability to identify dual defects with relatively small relative axial distances. This provides new ideas and methods for finite element analysis in identifying the axial position of damage in laminated pipeline structures. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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Figure 1

Figure 1
<p>Dispersion curve of the layered pipe structure.</p> Full article ">Figure 2
<p>Wavelet packet decomposition tree at level 5.</p> Full article ">Figure 3
<p>Schematic diagram of circumferential defects.</p> Full article ">Figure 4
<p>Schematic of double defects with the same circumferential position in the pipeline structure.</p> Full article ">Figure 5
<p>Defect layout diagram for dual defects in pipeline structures at different circumferential positions.</p> Full article ">Figure 6
<p>Two-dimensional damage index matrix for pipeline structures with dual defects at the same circumferential position <sup>4</sup>. <sup>4</sup> The axial distance of damage in the figure is the distance between the second defect and the piezoelectric element sensor, with the first defect always located at a position 750 mm away from the piezoelectric element sensor. The axial distance between the second defect and the piezoelectric element sensor was set as 750 mm, such that the position of the first defect overlapped with that of the second defect, meaning that there was only one defect in the pipeline structure.</p> Full article ">Figure 7
<p>Time-domain comparison plots for the operating conditions: (<b>a</b>) comparison of the piezoelectric time-domain signals between case 10 and case 11, (<b>b</b>) the partial detailed diagrams of case 10 and case 11, (<b>c</b>) comparison of the piezoelectric time-domain signals between case 10 and case 12, and (<b>d</b>) the partial detailed diagrams of case 10 and case 12.</p> Full article ">Figure 8
<p>The propagation mechanism of guided waves in pipeline structures with dual defects.</p> Full article ">Figure 9
<p>Sensor signal diagram.</p> Full article ">Figure 10
<p>The two-dimensional damage index matrix for dual defects in pipeline structures with different circumferential positions <sup>6</sup>. <sup>6</sup> The axial distance of the damage shown in the figure refers to the distance between the second defect and the piezoelectric element sensor. The first damage is always located at 750 mm from the piezoelectric element sensor.</p> Full article ">Figure 11
<p>Piezoelectric time domain comparison <sup>7</sup>: (<b>a</b>) piezoelectric time domain diagram of case 14, (<b>b</b>) piezoelectric time domain diagram of case 22, (<b>c</b>) piezoelectric time domain comparison between case 14 and case 22, (<b>d</b>) piezoelectric temporal domain local amplification diagram. <sup>7</sup> For case 14 and case 22, the reflection echo 2 of the first defect, and the reflection echo 1 of the second defect propagated the same distance in the pipeline, so the two wave packets completely overlapped at this time.</p> Full article ">Figure 11 Cont.
<p>Piezoelectric time domain comparison <sup>7</sup>: (<b>a</b>) piezoelectric time domain diagram of case 14, (<b>b</b>) piezoelectric time domain diagram of case 22, (<b>c</b>) piezoelectric time domain comparison between case 14 and case 22, (<b>d</b>) piezoelectric temporal domain local amplification diagram. <sup>7</sup> For case 14 and case 22, the reflection echo 2 of the first defect, and the reflection echo 1 of the second defect propagated the same distance in the pipeline, so the two wave packets completely overlapped at this time.</p> Full article ">Figure 12
<p>The comparison of the damage index matrices for pipeline structures <sup>8</sup>. <sup>8</sup> The axial distance of the damage in the figure refers to the axial distance between the second defect and the piezoelectric element sensor. As the single-layered pipeline structure had only one defect, the axial distance of the damage refers to the axial distance between the damage and the piezoelectric element sensor.</p> Full article ">Figure 13
<p>Radial schematic diagram of the layered pipeline structure.</p> Full article ">Figure 14
<p>Experimental system for layered pipeline structures.</p> Full article ">
18 pages, 29351 KiB  
Article
Field Measurement Study on Dynamic Characteristics of the Shanghai World Financial Center
by Xu Wang, Hu Kong, Guoliang Zhang and Peng Zhao
Appl. Sci. 2023, 13(13), 7973; https://doi.org/10.3390/app13137973 - 7 Jul 2023
Viewed by 1729
Abstract
It is of great practical importance to study the vibration response characteristics of super high-rise buildings under an earthquake action to provide a basis for seismic design and later maintenance of structures in coastal areas. During this study, the Shanghai World Financial Center [...] Read more.
It is of great practical importance to study the vibration response characteristics of super high-rise buildings under an earthquake action to provide a basis for seismic design and later maintenance of structures in coastal areas. During this study, the Shanghai World Financial Center (SWFC)’s health monitoring system was utilized to monitor earthquakes of magnitude 6.4 in Taiwan, 6.0 in Japan, 7.2 in the East China Sea, and 4.4 in Jiangsu, in real-time. Through the improved Envelope Random Decrement Technique (E-RDT), the dynamic properties of super high-rise buildings were examined under different earthquake effects in terms of the acceleration power spectrum, natural frequency, damping ratio, and mode shape. The results demonstrated that (1) the vibration responses of the structure in X (East–West) and Y (North–South) directions under four earthquakes were consistent, and with increasing floor height, the discreteness of the amplitude and acceleration signals of vibration responses increased. (2) The first two natural frequencies of the structure in X and Y directions decreased with the increase in amplitude, but the damping ratio increased with the increase in amplitude. The minimum values of the first two natural frequencies are 0.1498 Hz and 0.4312 Hz, respectively, and the maximum values of the first two damping ratios are 0.0086 and 0.0068, respectively. (3) Under different earthquake excitations, the SWFC’s mode shape’s estimates were similar, and their change trends in the X and Y directions were nonlinear as the number of floors increased. The structure was not seriously damaged by the four earthquakes. This study can provide helpful information for the seismic design of super high-rise buildings based on its findings. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Elevation view of the SWFC (Map Data © 2022 Google; image by Xu Wang).</p> Full article ">Figure 2
<p>Floor plan of the SWFC: (<b>a</b>) upper floor plan (<b>b</b>) lower floor plan.</p> Full article ">Figure 3
<p>Accelerometer layout on the SWFC.</p> Full article ">Figure 4
<p>Time−history response on F101 of the SWFC.</p> Full article ">Figure 5
<p>Acceleration trajectory on F101 of the SWFC.</p> Full article ">Figure 5 Cont.
<p>Acceleration trajectory on F101 of the SWFC.</p> Full article ">Figure 6
<p>The peak value of the SWFC acceleration response varies with the floor.</p> Full article ">Figure 7
<p>Power spectra of the time−history responses of the SWFC.</p> Full article ">Figure 7 Cont.
<p>Power spectra of the time−history responses of the SWFC.</p> Full article ">Figure 8
<p>SWFC natural frequency under earthquake.</p> Full article ">Figure 8 Cont.
<p>SWFC natural frequency under earthquake.</p> Full article ">Figure 9
<p>Normalized natural frequency.</p> Full article ">Figure 9 Cont.
<p>Normalized natural frequency.</p> Full article ">Figure 10
<p>SWFC damping ratio under earthquake.</p> Full article ">Figure 10 Cont.
<p>SWFC damping ratio under earthquake.</p> Full article ">Figure 11
<p>Normalized damping ratio.</p> Full article ">Figure 12
<p>SWFC mode shapes under four earthquakes.</p> Full article ">
13 pages, 5818 KiB  
Communication
Design of a Functionally Graded Material Phonon Crystal Plate and Its Application in a Bridge
by Shuqin Li, Jing Song and Jingshun Ren
Appl. Sci. 2023, 13(13), 7677; https://doi.org/10.3390/app13137677 - 29 Jun 2023
Viewed by 1278
Abstract
In order to alleviate the structural vibrations induced by traffic loads, in this paper, a phonon crystal plate with functionally graded materials is designed based on local resonance theory. The vibration damping performance of the phonon crystal plate is studied via finite element [...] Read more.
In order to alleviate the structural vibrations induced by traffic loads, in this paper, a phonon crystal plate with functionally graded materials is designed based on local resonance theory. The vibration damping performance of the phonon crystal plate is studied via finite element numerical simulation and the band gap is verified via vibration transmission response analysis. Finally, the engineering application mode is simulated to make it have practical engineering application value. The results show that the phonon crystal plate has two complete bandgaps within 0~150 Hz, the initial bandgap frequency is 0.00 Hz, the cut-off frequency is 128.32 Hz, and the internal ratio of 0~100 Hz is 94.13%, which can effectively reduce the structural vibration caused by traffic loads. Finally, stress analysis of the phonon crystal plate is carried out. The results show that phonon crystals of functionally graded materials can reduce stress concentration through adjusting the band gap. The phonon crystal plate designed in this paper can effectively suppress the structural vibration caused by traffic loads, provides a new method for the vibration reduction of traffic infrastructure, and can be applied to the vibration reduction of bridges and their auxiliary facilities. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Structure diagram of phonon crystal plate.</p> Full article ">Figure 2
<p>Irreducible Brillouin zone of square lattice.</p> Full article ">Figure 3
<p>Phonon crystal cell meshing.</p> Full article ">Figure 4
<p>Band structure of phonon crystal.</p> Full article ">Figure 5
<p>Vibration mode diagram.</p> Full article ">Figure 6
<p>Direction of vibration transmission displacement excitation.</p> Full article ">Figure 7
<p>Vibration transmission response curve.</p> Full article ">Figure 8
<p>Vibration mode diagram of phonon crystal plate.</p> Full article ">Figure 9
<p>Phonon crystal plate structure model.</p> Full article ">Figure 10
<p>Stress characteristic analysis curve.</p> Full article ">Figure 11
<p>Stress distribution map.</p> Full article ">Figure 12
<p>Micro box girder structure.</p> Full article ">Figure 13
<p>Vibration transmission curve of box girder.</p> Full article ">Figure 14
<p>Vibration modes of box girder.</p> Full article ">Figure 14 Cont.
<p>Vibration modes of box girder.</p> Full article ">
23 pages, 8249 KiB  
Article
Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
by Weidong Wang, Xinyue Xu, Jun Peng, Wenbo Hu and Dingze Wu
Appl. Sci. 2023, 13(7), 4549; https://doi.org/10.3390/app13074549 - 3 Apr 2023
Cited by 8 | Viewed by 2726
Abstract
The automated detection of distress such as cracks or potholes is a key basis for assessing the condition of pavements and deciding on their maintenance. A fine-grained pavement distress-detection algorithm based on integrated data using a digital twin is proposed to solve the [...] Read more.
The automated detection of distress such as cracks or potholes is a key basis for assessing the condition of pavements and deciding on their maintenance. A fine-grained pavement distress-detection algorithm based on integrated data using a digital twin is proposed to solve the challenges of the insufficiency of high-quality negative samples in specific scenarios An asphalt pavement background model is created based on UAV-captured images, and a lightweight physical engine is used to randomly render 5 types of distress and 3 specific scenarios to the background model, generating a digital twin model that can provide virtual distress data. The virtual data are combined with real data in different virtual-to-real ratios (0:1 to 5:1) to form an integrated dataset and used to fully train deep object detection networks for fine-grained detection. The results show that the YOLOv5 network with the virtual-to-real ratio of 3:1 achieves the best average precision for 5 types of distress (asphalt pavement MAP: 75.40%), with a 2-fold and 1.5-fold improvement compared to models developed without virtual data and with traditional data augmentation, respectively, and achieves over 40% recall in shadow, occlusion and blur. The proposed approach could provide a more reliable and refined automated method for pavement analysis in complex scenarios. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Total architecture of refined detection algorithm for asphalt pavement distress based on integrated data using digital twin.</p> Full article ">Figure 2
<p>Flow of integrated dataset generation for asphalt pavement distress based on digital twin.</p> Full article ">Figure 3
<p>Schematic diagram of UAV-based image acquisition of asphalt pavement.</p> Full article ">Figure 4
<p>Schematic diagram of 5 types of virtual pavement distress. (<b>a</b>) Transverse crack. (<b>b</b>) Longitudinal crack. (<b>c</b>) Cross crack. (<b>d</b>) Alligator crack. (<b>e</b>) Pothole.</p> Full article ">Figure 5
<p>Schematic diagram of 3 types of virtual pavement distress under complex scenarios. (<b>a</b>) Shadow. (<b>b</b>) Occlusion. (<b>c</b>) Blur.</p> Full article ">Figure 6
<p>Images annotation process.(<b>a</b>) Annotation by Labelimg. (<b>b</b>) Convert the XML file to a TXT file. (<b>c</b>) Training results visualization.</p> Full article ">Figure 7
<p>Structure of YOLOv5 deep object detection network.</p> Full article ">Figure 8
<p>Schematic diagram of the predicted and labeled regions.</p> Full article ">Figure 9
<p>MAP and Loss-epoch curves of the YOLOv5 model.</p> Full article ">Figure 10
<p>Performance comparison of different algorithms.</p> Full article ">Figure 11
<p>MAP for the integrated dataset of six virtual-to-real ratios.</p> Full article ">Figure 12
<p>Precision-recall curves of five types of defects based on integrated datasets with different virtual-to-real ratios. Detection results of YOLOv5 network for (<b>a</b>) transverse cracks (<b>b</b>) longitudinal cracks (<b>c</b>) cross cracks (<b>d</b>) alligator cracks (<b>e</b>) potholes.</p> Full article ">Figure 13
<p>Recall of YOLOv5 network trained on the integrated dataset with the best virtual-to-real ratio of 3:1 for three adverse-detection scenarios.</p> Full article ">Figure 14
<p>Test results of YOLOv5 network trained on two datasets.</p> Full article ">Figure 15
<p>Schematic diagram of 5 types of concrete pavement distress. (<b>a</b>) Transverse crack. (<b>b</b>) Longitudinal crack. (<b>c</b>) Cross crack. (<b>d</b>) Alligator crack. (<b>e</b>) Pothole.</p> Full article ">Figure 16
<p>MAP for the integrated dataset of six virtual-to-real ratios on concrete pavement.</p> Full article ">
15 pages, 4319 KiB  
Article
A Comparison of Surface Deformation Measurement Methods for Slopes
by Chung R. Song, Richard L. Wood, Binyam Bekele, Nikolas Glennie, Alex Silvey and Mitra Nasimi
Appl. Sci. 2023, 13(6), 3417; https://doi.org/10.3390/app13063417 - 8 Mar 2023
Cited by 4 | Viewed by 1988
Abstract
This study aimed to promote an efficient and reliable collection of deformation data for earthen slopes by comparing the Total Station (TS), Distributed Strain Sensing (DSS), and Uncrewed Aerial System (UAS)-based deformation measurement methods. The TS-based method was a two-person task with a [...] Read more.
This study aimed to promote an efficient and reliable collection of deformation data for earthen slopes by comparing the Total Station (TS), Distributed Strain Sensing (DSS), and Uncrewed Aerial System (UAS)-based deformation measurement methods. The TS-based method was a two-person task with a longstanding “tried and true” reputation, and it provided acceptable results. However, it included a major portion of manual work in the field, potentially consuming extended time to obtain high-resolution data. The DSS-based method was a fiber optic cable-based one-person work, and it showed substantially faster and easier measurement. This method possessed the capability of collecting unattended measurements. The method also required anchor posts to measure deformation in segmented sections; some anchor posts became loose from shrinkage cracks and resulted in invalid measurements, particularly for soils of high plasticity. The UAS-based method was an aerial photogrammetric method. It provided an extremely high-resolution deformation profile but required a manual survey for an elevation check at reference points, although the surveying took a short amount of time by utilizing a Global Navigational Satellite Survey (GNSS) technique. This method required one operator and an assistant. From a comparison of the characteristics of the three different methods, it was found that each technique has its pros and cons, and the combination of different methods may greatly enhance the accuracy and convenience of the measurement. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Time domain reflectometry (TDR) (Revised plot based on Oleg [<a href="#B13-applsci-13-03417" class="html-bibr">13</a>]).</p> Full article ">Figure 2
<p>Cross section of fiber optic cable for physical strain and temperature measurement.</p> Full article ">Figure 3
<p>Example view of a point cloud as collected from a UAS platform.</p> Full article ">Figure 4
<p>Schematic Layer Profiles in Nebraska (Note: Figures are not in scale). (<b>a</b>) Typical soil profile in North, West and Mid-Nebraska. (<b>b</b>) Typical soil profile in East Nebraska.</p> Full article ">Figure 5
<p>Scene of I-180 Slope. (<b>a</b>) Crack on I-180 Slope. (<b>b</b>) DSS System Installed on I-180 Slope.</p> Full article ">Figure 6
<p>Arrangement of DSS Cable and Posts.</p> Full article ">Figure 7
<p>Initial Cracks Appeared on Highway 84.</p> Full article ">Figure 8
<p>Cross Section and Pictue of Verdigre Slope. (<b>a</b>) Retrofitting design of the slope. (<b>b</b>) Anchors for DSS and UAS reference points.</p> Full article ">Figure 9
<p>Deformation profiles of I-180 slope obtained from different methods. (<b>a</b>) Downward deformation monitored by DSS. Note: (100 mm = 2.5% downward strain) (<b>b</b>) Deformation (m) contour measured by TS (Difference between before and after the failure. Arrows indicate the direction of the movement). (<b>c</b>) Deformation profile from the UAS-based method (Unit of the color bar is in cm).</p> Full article ">Figure 10
<p>Instrumentation on Highway 84 slope. (<b>a</b>) Alignment of DSS line (Large solid circle: PVC Pole, Black Line: DSS line). (<b>b</b>) Measured Strain by DSS.</p> Full article ">Figure 11
<p>UAS-based deformation profile of Highway 84 (From Spring 2019 to Spring 2021, Unit of the color bar is in m).</p> Full article ">
18 pages, 5309 KiB  
Article
Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
by Samira Ardani, Saeed Eftekhar Azam and Daniel G. Linzell
Appl. Sci. 2023, 13(3), 1935; https://doi.org/10.3390/app13031935 - 2 Feb 2023
Cited by 8 | Viewed by 2264
Abstract
This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is [...] Read more.
This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and mapping from source to target domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior and health, provide a promising tool for damage identification in structural systems. Previous investigations show that damage intensity and location are highly correlated with POM variations for structures under unknown loads. To train damage identification algorithms based on POMs and ML, one generally needs to use multiple simulations to generate damage scenarios. The developed process is applied to a simply supported truss span in a multi-span railway bridge. TL is first used to obtain relationships between POMs for two modeled bridges: one being a source model (i.e., labeled) and the other being the target modeled bridge (i.e., unlabeled). This technique is then implemented to develop POMs for a damaged, unknown target using TL that links source and target POMs. It is shown that the trained knowledge from one bridge was effectively generalized to other, somewhat similar, bridges in the population. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>(<b>a</b>) Isometric view, truss span; (<b>b</b>) plan and elevation views and strain transducer locations.</p> Full article ">Figure 2
<p>Healthy average first POMs for each model.</p> Full article ">Figure 3
<p>TL process for training, cross validation, and hyperparameter tuning.</p> Full article ">Figure 4
<p>Confusion matrix, <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>0</mn> </msub> </mrow> </semantics></math> for DI = 80%: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>3</mn> </msub> <mo>,</mo> <mo> </mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>5</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Confusion matrix for model <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </mrow> </semantics></math> as target at DIs (<b>a</b>) 40%, (<b>b</b>) 60%, (<b>c</b>) 80%, and (<b>d</b>) 100%.</p> Full article ">Figure 6
<p>Confusion matrix at DL 18 (see <a href="#applsci-13-01935-f001" class="html-fig">Figure 1</a>b): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>3</mn> </msub> <mo>,</mo> <mo> </mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>5</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Confusion matrix for model <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </mrow> </semantics></math> as target at DLs (<b>a</b>) 3, (<b>b</b>) 8, (<b>c</b>) 13, and (<b>d</b>) 18 (see <a href="#applsci-13-01935-f001" class="html-fig">Figure 1</a>b).</p> Full article ">Figure 8
<p>Confusion matrix associated with each model for unknown DLs and DIs and loading event. Source model <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and target model (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>3</mn> </msub> <mo>,</mo> <mo> </mo> </mrow> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>M</mi> <mn>5</mn> </msub> </mrow> </semantics></math>.</p> Full article ">
22 pages, 12069 KiB  
Article
Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
by Xin Duan, Xi Chu, Weizhu Zhu, Zhixiang Zhou, Rui Luo and Junhao Meng
Appl. Sci. 2023, 13(3), 1756; https://doi.org/10.3390/app13031756 - 30 Jan 2023
Cited by 8 | Viewed by 2421
Abstract
Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature [...] Read more.
Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature points of a structure were extracted via image scale-invariant feature transform. Then, the mathematical model was analyzed respecting the relative position change of the feature points before and after deformation, and a calculation theory was proposed for the structure’s full-field displacement vector (FFDV). Next, a test beam was constructed to obtain the FFDV calculation results for the beam under different damage conditions. Validation results showed that the maximum length error of the FFDV was 0.48 mm, while the maximum angle error was 0.82°. The FFDV monitoring results for the test beam showed that the rotation angle of the displacement vector at the damage location presented abnormal characteristics. Additionally, a damage identification index was proposed for the rotation-angle change rate. Based on the validation test, the index was proven to be sensitive to the damage location. Finally, a structural damage identification program was proposed based on the FFDV monitoring results. The obtained results will help to expand structural health monitoring data and fundamentally solve damage identification issues arising from sparse monitoring data. This study is the first to implement structural full-field displacement monitoring under natural texture conditions. The proposed method exhibits outstanding economic benefits, efficiency, and visualization advantages compared with the conventional single-point monitoring method. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>Expression of feature points in two images.</p> Full article ">Figure 2
<p>Registration of feature points.</p> Full article ">Figure 3
<p>Mathematical model of the relative position change of the feature points.</p> Full article ">Figure 4
<p>Illustration of the test beam with dimensions (units: mm).</p> Full article ">Figure 5
<p>Photograph of the test beam.</p> Full article ">Figure 6
<p>Position of measuring camera.</p> Full article ">Figure 7
<p>Layout of dial indicators.</p> Full article ">Figure 8
<p>Layout diagram of test beam’s code marks.</p> Full article ">Figure 9
<p>Three-dimensional laser scanning validation test.</p> Full article ">Figure 10
<p>Location of damaged members.</p> Full article ">Figure 11
<p>Photographs of damaged test beam and member.</p> Full article ">Figure 12
<p>Diagram of test beam’s loading point.</p> Full article ">Figure 13
<p>Loading details of static load test.</p> Full article ">Figure 14
<p>Layout position of chessboard used in current study.</p> Full article ">Figure 15
<p>Feature points of test beam.</p> Full article ">Figure 16
<p>Distribution of feature points on test beam’s surface.</p> Full article ">Figure 17
<p>Calibration of test beam’s image monitoring resolution.</p> Full article ">Figure 18
<p>Structure surface FFDV under D01 condition.</p> Full article ">Figure 19
<p>Structure surface FFDV under D02 condition.</p> Full article ">Figure 20
<p>Structure surface FFDV under D03 condition.</p> Full article ">Figure 21
<p>Location coordinate extraction of code marks.</p> Full article ">Figure 22
<p>Code-mark displacement calculation results.</p> Full article ">Figure 23
<p>Validation of monitoring results.</p> Full article ">Figure 24
<p>Steps of structural full-field displacement monitoring.</p> Full article ">Figure 25
<p>Displacement vector distribution of nos. 13–14 damaged member under condition D01.</p> Full article ">Figure 26
<p>Displacement vector distribution of nos. 13–14 damaged member under condition D02.</p> Full article ">Figure 27
<p>Displacement vector distribution of nos. 13–14 damaged member under condition D03.</p> Full article ">Figure 28
<p>Relative displacement of nos. 13–14 damaged member under load condition D03.</p> Full article ">Figure 29
<p>FFDV extraction results for test beams above and below steel plates.</p> Full article ">Figure 30
<p>Schematic diagram of displacement vector’s rotation-angle change rate.</p> Full article ">Figure 31
<p>Upper chord steel plate edge’s rotation-angle distribution under conditions D01–D03.</p> Full article ">Figure 32
<p>Rotation-angle change rate curves under two damage conditions.</p> Full article ">Figure 33
<p>Schematic diagram of new damaged members.</p> Full article ">Figure 34
<p>Photograph of member repair and damage.</p> Full article ">Figure 35
<p>Diagram of FFDV and rotation-angle change rate chromatography.</p> Full article ">Figure 36
<p>Damage identification program.</p> Full article ">
19 pages, 4799 KiB  
Article
Design of a Structural Health Monitoring System and Performance Evaluation for a Jacket Offshore Platform in East China Sea
by Hailin Ye, Chuwei Jiang, Feng Zu and Suzhen Li
Appl. Sci. 2022, 12(23), 12021; https://doi.org/10.3390/app122312021 - 24 Nov 2022
Cited by 5 | Viewed by 3154
Abstract
Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, [...] Read more.
Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem. First, the sensor subsystem is determined to include the structure information, component information, and vibration information monitoring of the offshore platform. Based on the monitoring target, three sensor types including incline sensor, acceleration sensor, and strain sensor are initially selected. Second, the assessment subsystem is determined to include safety monitoring and early warning evaluation using static measurements, overall performance evaluation based on frequency variation, and damage identification based on strain modal using strain monitoring. Overall performance evaluation based on frequency variation and damage identification based on Strain modal are illustrated. Finally, an offshore platform in the East China Sea is selected to establish a finite-element model to discuss the application and feasibility of the SHM system, the frequency variation due to scouring, corrosion, the growth of marine organisms, and temperature variation was investigated, and the overall performance of the platform was also evaluated. This work can provide a reference for installation and implementation of SHM system for offshore platform. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>Schematic design of the offshore platform structural health monitoring system.</p> Full article ">Figure 2
<p>Schematic diagram of pile foundation load.</p> Full article ">Figure 3
<p>Stress model of a jacket leg.</p> Full article ">Figure 4
<p>Physical dimensions of jacket offshore platform: (<b>a</b>) jacket and observation platform; (<b>b</b>) wind tower. (unit: m).</p> Full article ">Figure 5
<p>Sensor’s placement on the jacket offshore platform.</p> Full article ">Figure 6
<p>Finite-element model of the offshore platform.</p> Full article ">Figure 7
<p>Pile–soil interaction model [<a href="#B30-applsci-12-12021" class="html-bibr">30</a>].</p> Full article ">Figure 8
<p>Structural natural frequency change under Condition A.</p> Full article ">Figure 9
<p>Structural natural frequency change under Condition B.</p> Full article ">Figure 10
<p>First order strain difference diagram of 50% bar damage.</p> Full article ">Figure 10 Cont.
<p>First order strain difference diagram of 50% bar damage.</p> Full article ">Figure 11
<p>The diagram of time-varying effect on frequency under scouring. (<b>a</b>) Frequency change rate under scouring. (<b>b</b>) Frequency variation under scouring.</p> Full article ">Figure 12
<p>The diagram of time-varying effect on frequency under corrosion. (<b>a</b>) Frequency change rate under corrosion. (<b>b</b>) Frequency variation under corrosion.</p> Full article ">Figure 13
<p>The diagram of time-varying effect on frequency under marine life growth. (<b>a</b>) Frequency change rate under marine life growth. (<b>b</b>) Frequency variation under marine life growth.</p> Full article ">Figure 14
<p>The diagram of overall natural frequency variation of the offshore platform over a whole year.</p> Full article ">
13 pages, 2088 KiB  
Article
The Use of a Movable Vehicle in a Stationary Condition for Indirect Bridge Damage Detection Using Baseline-Free Methodology
by Ibrahim Hashlamon and Ehsan Nikbakht
Appl. Sci. 2022, 12(22), 11625; https://doi.org/10.3390/app122211625 - 16 Nov 2022
Cited by 2 | Viewed by 1705
Abstract
The use of an instrumented scanning vehicle has become the center of focus for bridge health monitoring (BHM) due to its cost efficiency, mobility, and practicality. However, indirect BHM still faces challenges such as the effects of road roughness on vehicle response, which [...] Read more.
The use of an instrumented scanning vehicle has become the center of focus for bridge health monitoring (BHM) due to its cost efficiency, mobility, and practicality. However, indirect BHM still faces challenges such as the effects of road roughness on vehicle response, which can be avoided when the vehicle is in a stationary condition. This paper proposes a baseline-free method to detect bridge damage using a stationary vehicle. The proposed method is implemented in three steps. First, the contact-point response (CPR) of the stationary vehicle is computed. Secondly, the CPR is decomposed into intrinsic mode functions (IMFs) using the variational mode decomposition (VMD) method. Finally, instantaneous amplitude (IA) of a high frequency IMF is computed. The peak represents the existence and location of the damage. A finite element model of a bridge with damage is created. The results show that the method can identify the damage location under different circumstances, such as a vehicle with and without damping, different speeds of the moving vehicle, different sizes of damage, and multiple damage. A higher speed was found to provide better visibility of damages. In addition, smaller damage was less visible than wider damage. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>Schematic representation the proposed damage detection method.</p> Full article ">Figure 2
<p>A stationary vehicle parked in the middle of a bridge while another moving vehicle passes over the bridge.</p> Full article ">Figure 3
<p>A typical profile of road surface roughness.</p> Full article ">Figure 4
<p>Dynamic response of the stationary vehicle, its contact-point, and bridge acceleration response at mid-span.</p> Full article ">Figure 5
<p>IA of the contact-point response of a stationary vehicle parked at mid-span.</p> Full article ">Figure 6
<p>IA of the contact-point response of a stationary vehicle parked far from the location of the damage.</p> Full article ">Figure 7
<p>IA of the contact-point response of a stationary vehicle parked at the location of the damage.</p> Full article ">Figure 8
<p>IA of the IMF for the healthy and damaged bridge.</p> Full article ">Figure 9
<p>IA of the IMF of contact-point response of the damped stationary vehicle.</p> Full article ">Figure 10
<p>IA of the IMF of contact-point response of the stationary vehicle on a bridge with multiple damage.</p> Full article ">Figure 11
<p>IA of IMF of contact-point response of a stationary vehicle on a bridge with 0.5 m damage size.</p> Full article ">Figure 12
<p>IA of IMF of the contact-point response of a stationary vehicle with 5 m/s speed of the moving vehicle.</p> Full article ">
17 pages, 5514 KiB  
Article
Enhancing Reliability Analysis with Multisource Data: Mitigating Adverse Selection Problems in Bridge Monitoring and Management
by Ananta Sinha, Mi G. Chorzepa, Jidong J. Yang, S. Sonny Kim and Stephan Durham
Appl. Sci. 2022, 12(20), 10359; https://doi.org/10.3390/app122010359 - 14 Oct 2022
Cited by 1 | Viewed by 1608
Abstract
Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one [...] Read more.
Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>WIM Systems Including (<b>a</b>) Weight Sensors Installed on Roadways and (<b>b</b>) a Data Acquisition System.</p> Full article ">Figure 2
<p>Flow chart showing the WIM data driven reliability assessment.</p> Full article ">Figure 3
<p>SHAP value plot of attributes from NBI Database using (<b>a</b>) XGBoost and (<b>b</b>) CatBoost algorithms.</p> Full article ">Figure 3 Cont.
<p>SHAP value plot of attributes from NBI Database using (<b>a</b>) XGBoost and (<b>b</b>) CatBoost algorithms.</p> Full article ">Figure 4
<p>Map showing locations of old and reconstructed bridges in Georgia.</p> Full article ">Figure 5
<p>WIM live load histogram fitted with normal distribution and NBI OR distribution.</p> Full article ">Figure 6
<p>Load and capacity curve of bridges (Site ID: 0210378).</p> Full article ">Figure 7
<p>Bridge reliability in percentage for routes (<b>a</b>) I-75, (<b>b</b>) I-95, (<b>c</b>) I-16, and (<b>d</b>) I-20.</p> Full article ">Figure 8
<p>Operating rating vs. year built/reconstructed (Note: 1 kip = 4.4482 kN).</p> Full article ">Figure 9
<p>Histogram Showing the Number of Low Reliability (RI ≤ 2.0) Bridges by Age Groups in Routes (<b>a</b>) I-75, (<b>b</b>) I-95, (<b>c</b>) I-16, and (<b>d</b>) I-20.</p> Full article ">Figure 10
<p>Reliability indices of bridges in the (<b>a</b>) Atlanta and (<b>b</b>) Savanah areas.</p> Full article ">Figure 11
<p>Selected bridge with a low RI: (<b>a</b>) side view and (<b>b</b>) substructure view in 2021.</p> Full article ">
19 pages, 6076 KiB  
Article
Identification of Vehicle Loads on an Orthotropic Deck Steel Box Beam Bridge Based on Optimal Combined Strain Influence Lines
by Cheng-Yao Li, Chao Wang, Qing-Xiang Yang and Tian-Yu Qi
Appl. Sci. 2022, 12(19), 9848; https://doi.org/10.3390/app12199848 - 30 Sep 2022
Cited by 8 | Viewed by 1795
Abstract
Vehicles are critical living loads to bridge structure; thus, identifying vehicle loads is very important for structural health monitoring and safety evaluations. This paper proposed a load identification method based on an optimal combined strain influence line. Firstly, two types of strain gauges [...] Read more.
Vehicles are critical living loads to bridge structure; thus, identifying vehicle loads is very important for structural health monitoring and safety evaluations. This paper proposed a load identification method based on an optimal combined strain influence line. Firstly, two types of strain gauges were arranged at the lower edge of a deck to monitor the strain response when vehicles cross the deck. One type of sensor was installed at the lower edge of the deck between U-ribs to detect axle information, including the number of axles, wheelbase, and vehicle speed. The other type of sensor was set on the lower edge of U-ribs to identify the axle’s weight. Secondly, structural responses under the vehicle load with known weights across the bridge was used to identify the strain influence line by using least square method. Because the local mechanical characteristic of the deck was very prominent under the wheel load, the strain influence line was short and susceptible to the transverse position of the vehicle. An index of variation coefficient is proposed as the object function, and an optimal combined strain influence line was developed using a genetic algorithm to decrease the influence of the transverse position of the load. Finally, the unknown vehicle load can be identified based on a calibrated combined strain influence line. A numerical simulation and an experimental test were carried out to validate the effectiveness and anti-noise performance of the proposed method. The identified results showed that the proposed algorithm has good accuracy and anti-noise performance. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>The entire layout of FAD sensors and axle weight sensors.</p> Full article ">Figure 2
<p>The strain response measured by FAD3 and FAD3′.</p> Full article ">Figure 3
<p>The basic principle of GA.</p> Full article ">Figure 4
<p>The half-span cross-section of an actual orthotropic deck steel box girder.</p> Full article ">Figure 5
<p>The entire finite element model.</p> Full article ">Figure 6
<p>The strain response at the position of FAD sensors.</p> Full article ">Figure 7
<p>The identified strain influence lines of part of sensors under vehicle loads at different transverse positions:(<b>a</b>) Z1 and (<b>b</b>) Z2.</p> Full article ">Figure 8
<p>Iterative optimization curve.</p> Full article ">Figure 9
<p>The optimal combination strain influence line under vehicle loads at different transverse positions.</p> Full article ">Figure 10
<p>Model and layout of strain measurement points: (<b>a</b>) plan view and (<b>b</b>) elevation view.</p> Full article ">Figure 11
<p>The entire experimental model.</p> Full article ">Figure 12
<p>Part of the strain response when the vehicle passes on the model from the transverse position of x = −1.5 cm: (<b>a</b>) signal collected by FAD sensors and (<b>b</b>) signal collected by axle weight sensors.</p> Full article ">Figure 13
<p>Convergence curve of the optimal solution.</p> Full article ">Figure 14
<p>The identified combined strain influence line under vehicle loads at different transverse position.</p> Full article ">Figure 15
<p>The averaged combined strain influence line.</p> Full article ">
20 pages, 16616 KiB  
Article
A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection
by Xingjun Gao, Chuansheng Huang, Shuai Teng and Gongfa Chen
Appl. Sci. 2022, 12(18), 9244; https://doi.org/10.3390/app12189244 - 15 Sep 2022
Cited by 15 | Viewed by 3068
Abstract
Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on [...] Read more.
Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on a DCNN to achieve anomaly crack detection. In the proposed method, the training set for the network only requires a small number of normal (non-crack) images but can achieve high detection accuracy. Moreover, the trained model has strong robustness in the condition of uneven illumination and evident crack difference. The proposed method is applied to the images of walls, bridges and pavements, and the results show that the detection accuracy comes up to 99.48%, 92.31% and 97.57%, respectively. In addition, the features of the neural network can be visualized to describe its working principle. This method has great potential in practical engineering applications. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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<p>Diagram of DSVDD data division.</p> Full article ">Figure 2
<p>The network architecture of improved VGG-16. The two-dimensional array denotes the convolution or pool kernel size, and the three-dimensional array denotes the size of the output image and the channels.</p> Full article ">Figure 3
<p>Flow chart of the proposed method.</p> Full article ">Figure 4
<p>Examples of images contained in the wall dataset: (<b>a</b>) non-crack images; (<b>b</b>) crack images.</p> Full article ">Figure 5
<p>Examples of the training images contained in the wall testing group dataset: (<b>a</b>) original images; (<b>b</b>) expanded images.</p> Full article ">Figure 6
<p>Results of random confetti noise.</p> Full article ">Figure 7
<p>The training process of the wall image classification network: (<b>a</b>) testing group; (<b>b</b>) control group.</p> Full article ">Figure 8
<p>Confusion matrix of wall testing set images: (<b>a</b>) testing group; (<b>b</b>) control group.</p> Full article ">Figure 9
<p>Example images contained in the bridge dataset: (<b>a</b>) non-crack images; (<b>b</b>) crack images.</p> Full article ">Figure 10
<p>Examples of the training images contained in the bridge dataset: (<b>a</b>) original images; (<b>b</b>) expanded images.</p> Full article ">Figure 11
<p>The results of the bridge image classification training and calibration process: (<b>a</b>) training process; (<b>b</b>) calibration set prediction results; (<b>c</b>) ROC curve; (<b>d</b>) overall diagram of scores-positive rate; (<b>e</b>) detailed diagram of a scores-positive rate. The red star represents the thermal threshold.</p> Full article ">Figure 11 Cont.
<p>The results of the bridge image classification training and calibration process: (<b>a</b>) training process; (<b>b</b>) calibration set prediction results; (<b>c</b>) ROC curve; (<b>d</b>) overall diagram of scores-positive rate; (<b>e</b>) detailed diagram of a scores-positive rate. The red star represents the thermal threshold.</p> Full article ">Figure 12
<p>Confusion matrix of bridge testing set images.</p> Full article ">Figure 13
<p>Examples of images contained in the pavement dataset: (<b>a</b>) non-crack images; (<b>b</b>) crack images.</p> Full article ">Figure 14
<p>Examples of the training images contained in the pavement dataset: (<b>a</b>) original images; (<b>b</b>) expanded images.</p> Full article ">Figure 15
<p>The results of the pavement image classification training and calibration process: (<b>a</b>) training process; (<b>b</b>) calibration set prediction results; (<b>c</b>) ROC curve; (<b>d</b>) overall diagram of scores-positive rate; (<b>e</b>) detailed diagram of a scores-positive rate. The red star represents the thermal threshold.</p> Full article ">Figure 16
<p>Confusion matrix of pavement testing set images.</p> Full article ">
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