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Special Issue "Advance of Structural Health Monitoring in Civil Engineering"

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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 10946

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Dr. Chung Song

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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

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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
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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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Keywords

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

Published Papers (13 papers)

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Research

Open AccessArticle

The Influence of the Hardness of the Tested Material and the Surface Preparation Method on the Results of Ultrasonic Testing

and

Appl. Sci. 2023, 13(17), 9904; https://doi.org/10.3390/app13179904 (registering DOI) - 01 Sep 2023

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 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)

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Open AccessArticle

Simulation Study on Axial Location Identification of Damage in Layered Pipeline Structures Based on Damage Index

Ying Li

Lingzhi Qu

and

Baoxin Qi

Appl. Sci. 2023, 13(15), 8850; https://doi.org/10.3390/app13158850 - 31 Jul 2023

Viewed by 336

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 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

1.7 λ \leq P D \leq 3.4 λ

I_{double defect 90 °} < I_{single defect} < I_{double defect 0 °}

; when

3.7 λ \leq P D \leq 4 λ

I_{double defect 90 °} < 0.3 < I_{double defect 0 °} < I_{single 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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Open AccessArticle

Field Measurement Study on Dynamic Characteristics of the Shanghai World Financial Center

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Appl. Sci. 2023, 13(13), 7973; https://doi.org/10.3390/app13137973 - 07 Jul 2023

Viewed by 378

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 (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)

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Open AccessCommunication

Design of a Functionally Graded Material Phonon Crystal Plate and Its Application in a Bridge

Shuqin Li

Jing Song

and

Jingshun Ren

Appl. Sci. 2023, 13(13), 7677; https://doi.org/10.3390/app13137677 - 29 Jun 2023

Viewed by 384

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 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)

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Open AccessArticle

Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin

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Appl. Sci. 2023, 13(7), 4549; https://doi.org/10.3390/app13074549 - 03 Apr 2023

Viewed by 1165

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 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)

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Open AccessArticle

A Comparison of Surface Deformation Measurement Methods for Slopes

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Appl. Sci. 2023, 13(6), 3417; https://doi.org/10.3390/app13063417 - 08 Mar 2023

Viewed by 764

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 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)

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Open AccessArticle

Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning

Samira Ardani

Saeed Eftekhar Azam

and

Daniel G. Linzell

Appl. Sci. 2023, 13(3), 1935; https://doi.org/10.3390/app13031935 - 02 Feb 2023

Cited by 1 | Viewed by 945

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 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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Open AccessArticle

Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification

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Appl. Sci. 2023, 13(3), 1756; https://doi.org/10.3390/app13031756 - 30 Jan 2023

Cited by 1 | Viewed by 1276

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 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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Open AccessArticle

Design of a Structural Health Monitoring System and Performance Evaluation for a Jacket Offshore Platform in East China Sea

and

Appl. Sci. 2022, 12(23), 12021; https://doi.org/10.3390/app122312021 - 24 Nov 2022

Viewed by 1213

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, 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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Open AccessArticle

The Use of a Movable Vehicle in a Stationary Condition for Indirect Bridge Damage Detection Using Baseline-Free Methodology

Ibrahim Hashlamon

and

Ehsan Nikbakht

Appl. Sci. 2022, 12(22), 11625; https://doi.org/10.3390/app122211625 - 16 Nov 2022

Cited by 1 | Viewed by 761

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 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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Open AccessArticle

Enhancing Reliability Analysis with Multisource Data: Mitigating Adverse Selection Problems in Bridge Monitoring and Management

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Appl. Sci. 2022, 12(20), 10359; https://doi.org/10.3390/app122010359 - 14 Oct 2022

Viewed by 701

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 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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Open AccessArticle

Identification of Vehicle Loads on an Orthotropic Deck Steel Box Beam Bridge Based on Optimal Combined Strain Influence Lines

and

Appl. Sci. 2022, 12(19), 9848; https://doi.org/10.3390/app12199848 - 30 Sep 2022

Cited by 2 | Viewed by 856

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 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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Open AccessArticle

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

and

Appl. Sci. 2022, 12(18), 9244; https://doi.org/10.3390/app12189244 - 15 Sep 2022

Cited by 2 | Viewed by 1196

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 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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