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Special Issue "Bridge Structural Health Monitoring and Damage Identification"

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

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editors

Guest Editor
Dr. Yun Lai Zhou

The Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Website | E-Mail
Interests: Structural Health Monitoring; Damage identification; Bridge Optimisation; Model updating; Fracture mechanics
Guest Editor
Dr. Magd Abdel Wahab

Labo Soete, Ghent University, Gent, Belgium
Website | E-Mail
Interests: finite element analysis; computational mechanics; numerical analysis; fretting fatigue; fretting wear; fatigue of materials
Guest Editor
Dr. Eloi Figueiredo

Faculdade de Engenharia da Universidade Lusofona, Campo Grande 376, 1749-024 Lisbon, Portugal
Website | E-Mail
Interests: Structural Health Monitoring (SHM); maintenance of bridges; Civil Engineering
Guest Editor
Dr. Francisco Javier Cara Cañas

Laboratory of Statistics, ETS Ingenieros Industriales, Universidad Politécnica de Madrid, Spain
Website | E-Mail
Interests: Analisis Modal Operacion; Alalgoritmo EM

Special Issue Information

Dear Colleagues,

Bridge structural health monitoring (BSHM) has the potential to perform an essential role in monitoring aging bridges since it can identify early damage propagation, which may evolve into huge economic losses and catastrophic failures. Bridges, which involve complicated engineering, are frequently located in restricted areas, such as cliffs, rivers, and straits. For connecting two separated areas, various types of bridges have been developed and constructed, including long span suspension or cable-stayed bridges, and steel–concrete composite bridges. Regarding this situation, BSHM adopts various sensors, such as cameras, wireless sensors, and radar to better examine bridges from distinct perspectives. Hereinafter, non-conventional methodologies and techniques, such as data driven approaches, are investigated. However, the reliability and accuracy of BSHM is, to date, out of reach, since more sophisticated bridges are constructed, which requires further investigation and a deeper understanding of BSHM.

This Special Issue aims to explore BSHM via various sensing techniques and related approaches, especially those for real bridge applications. This shall include multidisciplinary studies, and, thus, welcomes investigations related to BSHM from mechanical engineering, civil engineering, numerical simulations, signal processing, and so on.

This Special Issue aims to publish high-quality investigations regarding BSHM and damage identification, as well as reviews summarizing advances over recent years. Original, high-quality contributions that are not published elsewhere are welcome for this Special Issue.

Potential topics include, but are not limited to, the following:

  • Bridge structural health monitoring
  • Damage identification including detection, localization and quantification methods
  • Machine learning in BSHM
  • Artificial intelligence BSHM
  • Big data processing and management
  • Advanced sensing systems in BSHM
  • Robotic inspecting system in BSHM
  • Embedded sensing system in BSHM
  • Long-term condition monitoring for bridges

Papers are published upon acceptance, regardless of the Special Issue publication date.

 

Dr. Yun Lai Zhou
Dr. Magd Abdel Wahab
Dr. Eloi Figueiredo
Dr. Francisco Javier Cara Cañas
Guest Editors

Manuscript Submission Information

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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 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bridge structural health monitoring
  • Damage identification
  • Machine learning
  • Artificial intelligence
  • Robotic inspection
  • big data processing

Published Papers (15 papers)

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Research

Jump to: Review

Open AccessArticle Uniaxial Static Stress Estimation for Concrete Structures Using Digital Image Correlation
Sensors 2019, 19(2), 319; https://doi.org/10.3390/s19020319
Received: 28 November 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 15 January 2019
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Abstract
This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation [...] Read more.
This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation around the hole and, consequently, a displacement field. DIC measures this displacement field by comparing digital images taken before and after the hole-drilling. The stress level in the concrete structure is then determined by solving an optimization problem, designed to minimize the difference between the displacement fields from DIC and the one from a numerical model. Compared to the pointwise measurements by strain gauges, the full-field displacement obtained by DIC provides a larger amount of data, leading to a more accurate estimation. Our theoretical results were experimentally validated using concrete specimens, demonstrating the efficacy of the proposed method. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Robotic System for Inspection by Contact of Bridge Beams Using UAVs
Sensors 2019, 19(2), 305; https://doi.org/10.3390/s19020305
Received: 30 November 2018 / Revised: 9 January 2019 / Accepted: 10 January 2019 / Published: 14 January 2019
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Abstract
This paper presents a robotic system using Unmanned Aerial Vehicles (UAVs) for bridge-inspection tasks that require physical contact between the aerial platform and the bridge surfaces, such as beam-deflection analysis or measuring crack depth with an ultrasonic sensor. The proposed system takes advantage [...] Read more.
This paper presents a robotic system using Unmanned Aerial Vehicles (UAVs) for bridge-inspection tasks that require physical contact between the aerial platform and the bridge surfaces, such as beam-deflection analysis or measuring crack depth with an ultrasonic sensor. The proposed system takes advantage of the aerodynamic ceiling effect that arises when the multirotor gets close to the bridge surface. Moreover, this paper describes how a UAV can be used as a sensor that is able to fly and touch the bridge to take measurements during an inspection by contact. A practical application of the system involving the measurement of a bridge’s beam deflection using a laser tracking station is also presented. In order to validate our system, experiments on two different bridges involving the measurement of the deflection of their beams are shown. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Cable Interlayer Slip Damage Identification Based on the Derivatives of Eigenparameters
Sensors 2018, 18(12), 4456; https://doi.org/10.3390/s18124456
Received: 30 October 2018 / Revised: 13 December 2018 / Accepted: 13 December 2018 / Published: 16 December 2018
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Abstract
Cables are the main load-bearing structural components of long-span bridges, such as suspension bridges and cable-stayed bridges. When relative slip occurs among the wires in a cable, the local bending stiffness of the cable will significantly decrease, and the cable enters a local [...] Read more.
Cables are the main load-bearing structural components of long-span bridges, such as suspension bridges and cable-stayed bridges. When relative slip occurs among the wires in a cable, the local bending stiffness of the cable will significantly decrease, and the cable enters a local interlayer slip damage state. The decrease in the local bending stiffness caused by the local interlayer slip damage to the cable is symmetric or approximately symmetric for multiple elements at both the fixed end and the external load position. An eigenpair sensitivity identification method is introduced in this study to identify the interlayer slip damage to the cable. First, an eigenparameter sensitivity calculation formula is deduced. Second, the cable is discretized as a mass-spring-damping structural system considering stiffness and damping, and the magnitude of the cable interlayer slip damage is simulated based on the degree of stiffness reduction. The Tikhonov regularization method is introduced to solve the damage identification equation of the inverse problem, and artificial white noise is introduced to evaluate the robustness of the method to noise. Numerical examples of stayed cables are investigated to illustrate the efficiency and accuracy of the method proposed in this study. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Damage Localization of Beam Bridges Using Quasi-Static Strain Influence Lines Based on the BOTDA Technique
Sensors 2018, 18(12), 4446; https://doi.org/10.3390/s18124446
Received: 14 November 2018 / Revised: 13 December 2018 / Accepted: 14 December 2018 / Published: 15 December 2018
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Abstract
The diagnosis of damage in a bridge superstructure using quasi-static strain influence lines (ILs) is promising. However, it is challenging to accurately localize the damage in a bridge superstructure due to limited numbers of strain IL measurement points and inconsistencies between the loading [...] Read more.
The diagnosis of damage in a bridge superstructure using quasi-static strain influence lines (ILs) is promising. However, it is challenging to accurately localize the damage in a bridge superstructure due to limited numbers of strain IL measurement points and inconsistencies between the loading conditions before and after damage. To address the above issues, the Brillouin optical time domain analysis (BOTDA) technique is first applied to bridge damage localization using quasi-static strain ILs, and the number of strain IL measurement points is substantially increased. Additionally, a damage localization index based on quasi-static strain ILs that is independent of differences in the loading conditions before and after damage is proposed to localize damage in the superstructure of a beam bridge. Finally, the effectiveness of the proposed method is demonstrated through both numerical analysis and measured data from a quasi-static test of a model bridge. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Drive-By Bridge Frequency Identification under Operational Roadway Speeds Employing Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR)
Sensors 2018, 18(12), 4207; https://doi.org/10.3390/s18124207
Received: 2 November 2018 / Revised: 22 November 2018 / Accepted: 24 November 2018 / Published: 30 November 2018
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Abstract
Recently, drive-by bridge inspection has attracted increasing attention in the bridge monitoring field. A number of studies have given confidence in the feasibility of the approach to detect, quantify, and localize damages. However, the speed of the inspection truck represents a major obstacle [...] Read more.
Recently, drive-by bridge inspection has attracted increasing attention in the bridge monitoring field. A number of studies have given confidence in the feasibility of the approach to detect, quantify, and localize damages. However, the speed of the inspection truck represents a major obstacle to the success of this method. High speeds are essential to induce a significant amount of kinetic energy to stimulate the bridge modes of vibration. On the other hand, low speeds are necessary to collect more data and to attenuate the vibration of the vehicle due to the roughness of the road and, hence, magnify the bridge influence on the vehicle responses. This article introduces Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR) as a new technique, which possesses the ability to extract bridge dynamic properties from the responses of a vehicle that passes over the bridge at high speed. Stochastic Resonance (SR) is a phenomenon where feeble information such as weak signals can be amplified through the assistance of background noise. In this study, bridge vibrations that are present in the vehicle responses when it passes over the bridge are the feeble information while the noise counts for the effect of the road roughness on the vehicle vibration. UPSR is one of the SR models that has been chosen in this study for its suitability to extract the bridge vibration. The main contributions of this article are: (1) introducing a Frequency Independent-Stochastic Resonance model known as the FI-UPSR and (2) implementing this model to extract the bridge vibration from the responses of a fast passing vehicle. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm
Sensors 2018, 18(12), 4131; https://doi.org/10.3390/s18124131
Received: 29 October 2018 / Revised: 20 November 2018 / Accepted: 21 November 2018 / Published: 26 November 2018
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Abstract
Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. [...] Read more.
Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete Bridges
Sensors 2018, 18(11), 4070; https://doi.org/10.3390/s18114070
Received: 29 October 2018 / Revised: 15 November 2018 / Accepted: 16 November 2018 / Published: 21 November 2018
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Abstract
Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often [...] Read more.
Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often cover subtle changes in the vibration signals caused by damage to the system. The deflection signals of prestressed concrete (PC) bridges are regarded as the superposition of different effects, including concrete shrinkage, creep, prestress loss, material deterioration, temperature effects, and live load effects. According to multiscale analysis theory of the long-term deflection signal, in this paper, an integrated machine learning algorithm that combines a Butterworth filter, ensemble empirical mode decomposition (EEMD), principle component analysis (PCA), and fast independent component analysis (FastICA) is proposed for separating the individual deflection components from a measured single channel deflection signal. The proposed algorithm consists of four stages: (1) the live load effect, which is a high-frequency signal, is separated from the raw signal by a Butterworth filter; (2) the EEMD algorithm is used to extract the intrinsic mode function (IMF) components; (3) these IMFs are utilized as input in the PCA model and some uncorrelated and dominant basis components are extracted; and (4) FastICA is applied to derive the independent deflection component. The simulated results show that each individual deflection component can be successfully separated when the noise level is under 10%. Verified by a practical application, the algorithm is feasible for extracting the structural deflection (including concrete shrinkage, creep, and prestress loss) only caused by structural damage or material deterioration. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network
Sensors 2018, 18(10), 3371; https://doi.org/10.3390/s18103371
Received: 12 September 2018 / Revised: 3 October 2018 / Accepted: 6 October 2018 / Published: 9 October 2018
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Abstract
Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-based structural damage detection. In addition to the number [...] Read more.
Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-based structural damage detection. In addition to the number of hidden neurons, the type of transfer function used in the hidden layer cannot be neglected for the ANN design. Neural network learning can be further presented in the framework of Bayesian statistics, but the issues of selection for the hidden layer transfer function with respect to the Bayesian neural network has not yet been reported in the literature. In addition, most of the research works in the literature for addressing the predictive distribution of neural network output is only for a single target variable, while multiple target variables are rarely involved. In the present paper, for the purpose of probabilistic structural damage detection, Bayesian neural networks with multiple target variables are optimally designed, and the selection of the number of neurons, and the transfer function in the hidden layer, are carried out simultaneously to achieve a neural network architecture with suitable complexity. Furthermore, the nonlinear network function can be approximately linear by assuming the posterior distribution of network parameters is a sufficiently narrow Gaussian, and then the input-dependent covariance matrix of the predictive distribution of network output can be obtained with the Gaussian assumption for the situation of multiple target variables. Structural damage detection is conducted for a steel truss bridge model to verify the proposed method through a set of numerical case studies. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Performance of Rayleigh-Based Distributed Optical Fiber Sensors Bonded to Reinforcing Bars in Bending
Sensors 2018, 18(9), 3125; https://doi.org/10.3390/s18093125
Received: 16 August 2018 / Revised: 5 September 2018 / Accepted: 12 September 2018 / Published: 16 September 2018
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Abstract
Distributed Optical Fiber Sensors (DOFSs), thanks to their multiple sensing points, are ideal tools for the detection of deformations and cracking in reinforced concrete (RC) structures, crucial as a means to ensure the safety of infrastructures. Yet, beyond a certain point of most [...] Read more.
Distributed Optical Fiber Sensors (DOFSs), thanks to their multiple sensing points, are ideal tools for the detection of deformations and cracking in reinforced concrete (RC) structures, crucial as a means to ensure the safety of infrastructures. Yet, beyond a certain point of most DOFS-monitored experimental tests, researchers have come across unrealistic readings of strain which prevent the extraction of further reliable data. The present paper outlines the results obtained through an experimental test aimed at inducing such anomalies to isolate and identify the physical cause of their origin. The understanding of such a phenomenon would enable DOFS to become a truly performant strain sensing technique. The test consists of gradually bending seven steel reinforcement bars with a bonded DOFS under different conditions such as different load types, bonding adhesives, bar sections and more. The results show the bonding adhesives having an influence on the DOFS performance but not on the rise of anomalies while the reasons triggering the latter are narrowed down from six to two, reaching a strain threshold and a change in structure’s deformative behavior. Further planned research will allow identification of the cause behind the rise of strain-reading anomalies. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Damage Detection in Active Suspension Bridges: An Experimental Investigation
Sensors 2018, 18(9), 3002; https://doi.org/10.3390/s18093002
Received: 8 August 2018 / Revised: 2 September 2018 / Accepted: 4 September 2018 / Published: 7 September 2018
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Abstract
This paper considers a Hilbert marginal spectrum-based approach to health monitoring of active suspension bridge hangers. The paper proposes to takes advantage of the presence of active cables and use them as an excitation mean of the bridge, while they are used for [...] Read more.
This paper considers a Hilbert marginal spectrum-based approach to health monitoring of active suspension bridge hangers. The paper proposes to takes advantage of the presence of active cables and use them as an excitation mean of the bridge, while they are used for active damping. The Hilbert–Huang transform is used to calculate the Hilbert marginal spectrum and establish a damage index for each hanger of the suspension bridge. The paper aims to investigate the method experimentally, through a series of damage scenarios, on a laboratory suspension bridge mock-up equipped with four active cables; each active cable is made of a displacement actuator collocated with a force sensor. Different locations and levels of damage severity are implemented. For the first time, the investigation demonstrates experimentally the effectiveness of the technique, as well as its limitations, to detect and locate the damage in hangers of a suspension bridge. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Effects of the Ground Resolution and Thresholding on Crack Width Measurements
Sensors 2018, 18(8), 2644; https://doi.org/10.3390/s18082644
Received: 19 June 2018 / Revised: 29 July 2018 / Accepted: 9 August 2018 / Published: 12 August 2018
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Abstract
When diagnosing the condition of a structure, it is necessary to measure the widths of any existing cracks in the structure. To ensure safety when relying on images of cracks, the selected imaging parameters and processing technology must be well understood. In this [...] Read more.
When diagnosing the condition of a structure, it is necessary to measure the widths of any existing cracks in the structure. To ensure safety when relying on images of cracks, the selected imaging parameters and processing technology must be well understood. In this study, the effects of the ground sample distance and threshold values on the crack width measurement error are analyzed from a theoretical perspective. Here, the main source of such errors is assumed to be due to the mixed pixel phenomena in the left and right boundary pixels. Thus, a mathematical model is proposed in which the intensity changes in these pixels are computed via an equation. In addition, the relationship between the error and error probability distribution is represented with an equation based on the threshold values and mean error. Upon analysis, it was found that the threshold value that minimizes the error is at the mid-point between the background and foreground, and the probabilistic nature of the error indicates that it is theoretically possible to predict both the error and its probability distribution. The proposed model was validated using artificial images. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Improved ABC Algorithm Optimizing the Bridge Sensor Placement
Sensors 2018, 18(7), 2240; https://doi.org/10.3390/s18072240
Received: 29 May 2018 / Revised: 5 July 2018 / Accepted: 6 July 2018 / Published: 11 July 2018
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Abstract
Inspired by sensor coverage density and matching & preserving strategy, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm which is designed to optimize bridge sensor placement. We use dynamic random coverage coding method to initialize colony to ensure the diversity and [...] Read more.
Inspired by sensor coverage density and matching & preserving strategy, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm which is designed to optimize bridge sensor placement. We use dynamic random coverage coding method to initialize colony to ensure the diversity and effectiveness. In addition, we randomly select the factors with lower trust value to search and evolve after food source being matched in order that the relatively high trust point factor is retained in the exploitation of food sources, which reduces the blindness of searching and improves the efficiency of convergence and the accuracy of the algorithm. According to the analysis of the modal data of the Ha-Qi long span railway bridge, the results show that IABC algorithm has faster convergence rate and better global search ability when solving the optimal placement problem of bridge sensor. The final analysis results also indicate that the IABC’s solution accuracy is 76.45% higher than that of the ABC algorithm, and the solution stability is improved by 86.23%. The final sensor placement mostly covers the sensitive monitoring points of the bridge structure and, in this way, the IABC algorithm is suitable for solving the optimal placement problem of large bridge and other structures. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Sensitivity Analysis of Geometrical Parameters on the Aerodynamic Performance of Closed-Box Girder Bridges
Sensors 2018, 18(7), 2053; https://doi.org/10.3390/s18072053
Received: 24 April 2018 / Revised: 12 June 2018 / Accepted: 14 June 2018 / Published: 27 June 2018
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Abstract
In this study, the influence of two critical geometrical parameters (i.e., angles of wind fairing, α; and lower inclined web, β) in the aerodynamic performance of closed-box girder bridges was systematically investigated through conducting a theoretical analysis and wind tunnel testing using laser [...] Read more.
In this study, the influence of two critical geometrical parameters (i.e., angles of wind fairing, α; and lower inclined web, β) in the aerodynamic performance of closed-box girder bridges was systematically investigated through conducting a theoretical analysis and wind tunnel testing using laser displacement sensors. The results show that, for a particular inclined web angle β, a closed-box girder with a sharper wind fairing angle of α = 50° has better flutter and vortex-induced vibration (VIV) performance than that with α = 60°, while an inclined web angle of β = 14° produces the best VIV performance. In addition, the results from particle image velocimetry (PIV) tests indicate that a wind fairing angle of α = 50° produces a better flutter performance by inducing a single vortex structure and a balanced distribution of the strength of vorticity in both upper and lower parts of the wake region. Furthermore, two-dimensional three-degrees-of-freedom (2D-3DOF) analysis results demonstrate that the absolute values of Part A (with a reference of flutter derivative A2*) and Part D (with a reference of A1*H3*) generally decrease with the increase of β, while the change of the participation level of heaving degrees of freedom (DOF) in torsion-dominated coupled flutter initially increases, reaches its peak, and then decreases with the increase of β. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Open AccessArticle Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes
Sensors 2018, 18(6), 1879; https://doi.org/10.3390/s18061879
Received: 28 May 2018 / Revised: 4 June 2018 / Accepted: 6 June 2018 / Published: 8 June 2018
Cited by 6 | PDF Full-text (2450 KB) | HTML Full-text | XML Full-text
Abstract
This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging [...] Read more.
This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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Review

Jump to: Research

Open AccessReview Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation
Sensors 2019, 19(3), 463; https://doi.org/10.3390/s19030463
Received: 30 November 2018 / Revised: 27 December 2018 / Accepted: 30 December 2018 / Published: 23 January 2019
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Abstract
The detection of damage in bridges subjected to moving loads has attracted increasing attention in the field of structural health monitoring. Processing the dynamic responses induced by moving loads to characterize damage is the key to identifying damage in bridges. On this topic, [...] Read more.
The detection of damage in bridges subjected to moving loads has attracted increasing attention in the field of structural health monitoring. Processing the dynamic responses induced by moving loads to characterize damage is the key to identifying damage in bridges. On this topic, various methods of processing dynamic responses to moving loads have been developed in recent decades, with respective strengths and weaknesses. These methods appear in different applications and literatures and their features have not been comprehensively surveyed to form a profile of this special area. To address this issue, this study presents a comprehensive survey of methods for identifying damage by processing dynamic responses of cracked bridges subjected to moving loads. First, methods utilizing the Fourier transform to process dynamic responses to moving loads for damage detection in bridges are examined. Second, methods using wavelet transform to process the dynamic responses to moving loads for damage characterization are examined. Third, methods of employing the Hilbert-Huang transform to process the dynamic responses to moving loads for damage identification are examined. Fourth, methods of dynamic response-driven heuristic interrogation of damage in bridges subjected to moving loads are examined. Finally, we recommend future research directions for advancing the development of damage identification relying on processing dynamic responses to moving loads. This study provides a profile of the state-of-the-art and state-of-the-use of damage identification in bridges based on dynamic responses to moving loads, with the primary aim of helping researchers find crucial points for further exploration of theories, methods, and technologies for damage detection in bridges subjected to moving loads. Full article
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
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