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

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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 29333

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Special Issue Editors

Dr. Soojin Cho

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Guest Editor

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

Prof. Dr. Yun-Kyu An

E-Mail Website
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

Related Special Issue

Artificial Intelligence Technologies for Structural Health Monitoring in Applied Sciences (10 articles)

Published Papers (11 papers)

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Research

14 pages, 2079 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app10124330 - 24 Jun 2020

Cited by 8 | Viewed by 2382

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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13 pages, 6514 KiB

Open AccessFeature PaperArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app10103574 - 21 May 2020

Cited by 4 | Viewed by 2102

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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10 pages, 4269 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app10103495 - 18 May 2020

Cited by 6 | Viewed by 2921

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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17 pages, 3052 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app10093132 - 30 Apr 2020

Cited by 18 | Viewed by 2525

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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12 pages, 3782 KiB

Open AccessArticle

Sonic and Impact Test for Structural Assessment of Historical Masonry

by Alessandro Grazzini

Appl. Sci. 2019, 9(23), 5148; https://0-doi-org.brum.beds.ac.uk/10.3390/app9235148 - 28 Nov 2019

Cited by 16 | Viewed by 2605

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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14 pages, 3043 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app9224865 - 13 Nov 2019

Cited by 17 | Viewed by 3202

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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11 pages, 3089 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app9204469 - 22 Oct 2019

Cited by 7 | Viewed by 2459

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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14 pages, 6170 KiB

Open AccessFeature PaperArticle

Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization

by Byunghyun Kim and Soojin Cho

Appl. Sci. 2019, 9(20), 4444; https://0-doi-org.brum.beds.ac.uk/10.3390/app9204444 - 19 Oct 2019

Cited by 1 | Viewed by 2266

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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12 pages, 3769 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app9163252 - 09 Aug 2019

Cited by 3 | Viewed by 1919

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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16 pages, 2604 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app9153193 - 05 Aug 2019

Cited by 5 | Viewed by 3203

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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14 pages, 7993 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app9152956 - 24 Jul 2019

Cited by 16 | Viewed by 2917

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 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

(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering)

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