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Bridge Monitoring Using Remote Sensors
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Bridge Monitoring Using Remote Sensors

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 51304

Special Issue Editors

Prof. Dr. Fabio Remondino

E-Mail Website
Guest Editor
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: photogrammetry; laser scanning; optical metrology; 3D; AI; quality control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bijan Samali

E-Mail Website
Guest Editor
Centre for Infrastructure Engineering, School of Engineering, Design and Built Environment, Western Sydney University, NSW, Australia
Interests: structural dynamics; earthquake engineering; wind engineering; smart materials for structural control applications; damage detection and health monitoring of bridges
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Rashidi

E-Mail Website
Guest Editor
Senior Lecturer, Centre for Infrastructure Engineering, Western Sydney University, Kingswood, NSW 2747, Australia
Interests: bridge engineering and asset management; digital twin development; unmanned aerial vehicle (UAV) based photogrammetry; terrestrial laser scanning (TLS); structural health monitoring (SHM), sustainability, and life cycle management.
Special Issues, Collections and Topics in MDPI journals
Dr. Masoud Mohammadi

E-Mail Website
Guest Editor
Senior Research Assistant, Centre for Infrastructure Engineering, Western Sydney University, Kingswood, NSW 2747, Australia
Interests: advanced manufacturing; civil/structural engineering; bridge engineering; structural seismic dampers; digital twin development; bridge health monitoring; bridge information model (BrIM)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last two decades, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructure has significantly increased. Advanced technologies such as laser scanning and photogrammetry have become suitable alternatives for labor-intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increased use of these methods in civil infrastructure asset management especially bridge monitoring.

This Special Issue invites contributions to showcase various applications of remote sensing in health monitoring, computer modeling, assessment, and management of bridges in different phases of fabrication, construction, operation, and maintenance. The potential topics of this Special Issue include, but are not limited to the application of remote sensing in the following areas:

  • 3D model reconstruction of bridges.
  • Quality inspection of bridge elements.
  • Structural and substructural assessment of bridges.
  • Optimization of scanning parameters and model updating.
  • Advanced structural health monitoring (SHM).
  • Development of bridge information model (BrIM).
  • Artificial intelligent (AI).
  • Progress tracking.

Dr. Fabio Remondino
Prof. Dr. Bijan Samali
Dr. Maria Rashidi
Dr. Masoud Mohammadi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

  • Remote sensors
  • Bridge
  • Digital twin
  • Quality inspection
  • Structural assessment
  • Artificial intelligent
  • Progress tracking
  • Bridge information model (BrIM)

Published Papers (15 papers)

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Research

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23 pages, 3478 KiB  
Article
Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study
by Azadeh Noori Hoshyar, Maria Rashidi, Yang Yu and Bijan Samali
Remote Sens. 2023, 15(8), 1984; https://doi.org/10.3390/rs15081984 - 09 Apr 2023
Cited by 5 | Viewed by 2027
Abstract
Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have [...] Read more.
Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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34 pages, 24958 KiB  
Article
Accuracy Analysis and Appropriate Strategy for Determining Dynamic and Quasi-Static Bridge Structural Response Using Simultaneous Measurements with Two Real Aperture Ground-Based Radars
by Milan Talich, Jan Havrlant, Lubomír Soukup, Tomáš Plachý, Michal Polák, Filip Antoš, Pavel Ryjáček and Vojtěch Stančík
Remote Sens. 2023, 15(3), 837; https://doi.org/10.3390/rs15030837 - 02 Feb 2023
Cited by 3 | Viewed by 1482
Abstract
Over the past 10 years, ground-based radar interferometry has become a frequently used technology for determining dynamic deflections of bridge structures induced by vehicle passages. When measuring with only one radar device, the so-called Interpretation Error (EI) considerably rises. When [...] Read more.
Over the past 10 years, ground-based radar interferometry has become a frequently used technology for determining dynamic deflections of bridge structures induced by vehicle passages. When measuring with only one radar device, the so-called Interpretation Error (EI) considerably rises. When using two radars, it is possible to simultaneously determine, for example, vertical and longitudinal displacements and to eliminate the Interpretation Error. The aim of the article is to establish a suitable strategy for determining dynamic and quasi-static response of bridge structures based on the accuracy analysis of measurement by two radars. The necessary theory for displacements determination by means of two radar devices is presented. This is followed by an analysis of errors when measuring with only one radar. For the first time in the literature, mathematical formulas are derived here for determining the accuracy of the resulting displacements by simultaneous measurement with two radars. The practical examples of bridge structures displacements determination by measuring with two radar devices in the field are presented. The key contribution of the paper is the possibility to estimate and plan in advance the achievable accuracy of the resulting displacements for the given radar configurations in relation to the bridge structure. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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23 pages, 6648 KiB  
Article
Automatic Inspection of Bridge Bolts Using Unmanned Aerial Vision and Adaptive Scale Unification-Based Deep Learning
by Shang Jiang, Jian Zhang, Weiguo Wang and Yingjun Wang
Remote Sens. 2023, 15(2), 328; https://doi.org/10.3390/rs15020328 - 05 Jan 2023
Cited by 8 | Viewed by 3227
Abstract
Bolted connections are essential components that require regular inspection to ensure bridge safety. Existing methods mainly rely on traditional artificial vision-based inspection, which is inefficient due to the many bolts of bridges. A vision-based method using deep learning and unmanned aerial vision is [...] Read more.
Bolted connections are essential components that require regular inspection to ensure bridge safety. Existing methods mainly rely on traditional artificial vision-based inspection, which is inefficient due to the many bolts of bridges. A vision-based method using deep learning and unmanned aerial vision is proposed to automatically analyze the bridge bolts’ condition. The contributions are as follows: (1) Addressing the problems that motion blur often exists in videos captured by unmanned ariel systems (UASs) with high moving speed, and that bolt damage is hard to accurately detect due to the few pixels a single bolt occupies, a bolt image preprocessing method, including image deblurring based on inverse filtering with camera motion model and adaptive scaling based on super-resolution, is proposed to eliminate the motion blur of bolt images and segment them into subimages with uniform bolt size. (2) Addressing the problem that directly applying an object detection network for both bolt detection and classification may lead to the wrong identification of bolt damage, a two-stage detection method is proposed to divide bolt inspection into bolt object segmentation and damage classification. The proposed method was verified on an in-service bridge to detect bolts and classify them into normal bolts, corrosion bolts, and loose bolts. The results show that the proposed method can effectively eliminate the inherent defects of data acquired by UAS and accurately classify the bolt defects, verifying the practicability and high precision of the proposed method. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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20 pages, 7932 KiB  
Article
Comparison between Supervised and Unsupervised Learning for Autonomous Delamination Detection Using Impact Echo
by Faezeh Jafari and Sattar Dorafshan
Remote Sens. 2022, 14(24), 6307; https://doi.org/10.3390/rs14246307 - 13 Dec 2022
Cited by 5 | Viewed by 1727
Abstract
Impact echo (IE) is a non-destructive evaluation method commonly used to detect subsurface delamination in reinforced concrete bridge decks. Existing analysis methods are based on frequency domain which can lead to inaccurate assessments of reinforced concrete bridge decks since they do not consider [...] Read more.
Impact echo (IE) is a non-destructive evaluation method commonly used to detect subsurface delamination in reinforced concrete bridge decks. Existing analysis methods are based on frequency domain which can lead to inaccurate assessments of reinforced concrete bridge decks since they do not consider features of the IE signals in the time domain. The authors propose a new method for IE classification by combining features in the time and the frequency domains. The features used in this study included normalized peak values, energy, power, time of peaks, and signal lengths that were extracted from IE signals after they are preprocessed. We used a dataset containing IE data collected from four in-service bridges, annotated using chain dragging. A support vector machine (SVM) classifier was constructed using combined features to classify IE signals. A 1DCNN with unfiltered IE signals and a two-dimensional CNN using wavelet scalograms (2D representations of unfiltered IE signals) were also used to classify IE signals. The SVM model performed significantly better than the other models, with an accuracy rate, true positive rate, and true negative rate of 97%, 92%, and 98%, respectively. The SVM model also generated more accurate defect maps for all investigated bridges. IE data from the Federal Highway Administration’s InfoBridge website were used to investigate the efficacy of the developed models. The investigation yielded promising results for the proposed SVM model when used for a new set of IE data. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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21 pages, 7839 KiB  
Article
Laboratory Investigation on Detecting Bridge Scour Using the Indirect Measurement from a Passing Vehicle
by Bin Zhang, Hua Zhao, Chengjun Tan, Eugene J. OBrien, Paul C. Fitzgerald and Chul-Woo Kim
Remote Sens. 2022, 14(13), 3106; https://doi.org/10.3390/rs14133106 - 28 Jun 2022
Cited by 7 | Viewed by 2142
Abstract
For bridges with surface foundations, scour is one of the main reasons for bridge failures. In regard to structural health monitoring, vibration-based scour detection techniques have received increasing attention over the past two decades. Scour occurs below the water surface in rivers or [...] Read more.
For bridges with surface foundations, scour is one of the main reasons for bridge failures. In regard to structural health monitoring, vibration-based scour detection techniques have received increasing attention over the past two decades. Scour occurs below the water surface in rivers or sea, leading to difficulty in equipment installation and maintenance. Recently, the concept of “drive-by” SHM using the indirect measurement of passing vehicle responses has been developed rapidly due to its convenience and low cost. This paper proposes a method to detect scour using the vehicle responses under an operational vehicle speed. The wavelet transform was applied to vehicle accelerations to obtain the wavelet energy. It was found that the wavelet energy increases with the increase in the scour damage level. However, the wavelet energy may also be affected by the on-site operating environments, such as sensor noise and other variabilities, which interferes with the identification of scour in practice. Hence, in this work, a statistical-wavelet-based approach was presented to effectively detect the presence of scour and even its location. The feasibility of the proposed approach is verified in both numerical simulation and lab experiments. The results show that the proposed method has a good potential to detect scour using indirect measurements. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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24 pages, 104142 KiB  
Article
Application of TLS Method in Digitization of Bridge Infrastructures: A Path to BrIM Development
by Masoud Mohammadi, Maria Rashidi, Vahid Mousavi, Yang Yu and Bijan Samali
Remote Sens. 2022, 14(5), 1148; https://doi.org/10.3390/rs14051148 - 25 Feb 2022
Cited by 13 | Viewed by 3911
Abstract
Over the past years, bridge inspection practices and condition assessments were predicated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to [...] Read more.
Over the past years, bridge inspection practices and condition assessments were predicated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to turn an actual civil infrastructure asset into a detailed and precise digital model using state-of-the-art emerging technologies such as laser scanners has become in demand among structural engineers and managers, especially bridge asset managers. Although advanced remote technologies such as Terrestrial Laser Scanning (TLS) are recently established to overcome these challenges, the research on this subject is still lacking a comprehensive methodology for a reliable TLS-based bridge inspection and a well-detailed Bridge Information Model (BrIM) development. In this regard, the application of BrIM as a shared platform including a geometrical 3D CAD model connected to non-geometrical data can benefit asset managers, and significantly improve bridge management systems. Therefore, this research aims not only to provide a practical methodology for TLS-derived BrIM but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction. This methodology was further verified and demonstrated via a case study on a cable-stayed bridge called Werrington Bridge, located in New South Wales (NSW), Australia. In this case, the process of extracting a precise 3D CAD model from TLS data using the sliced-based method and a workflow to connect non-geometrical information and develop a BrIM are elaborated. The findings of this research confirm the reliability of using TLS and the sliced-based method, as approaches with millimeter-level geometric accuracy, for bridge inspection subjected to precise 3D model extraction, as well as bridge asset management and BrIM development. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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29 pages, 7456 KiB  
Article
Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies
by Solmaz Pourzeynali, Xinqun Zhu, Ali Ghari Zadeh, Maria Rashidi and Bijan Samali
Remote Sens. 2022, 14(1), 119; https://doi.org/10.3390/rs14010119 - 28 Dec 2021
Cited by 5 | Viewed by 2156
Abstract
Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for [...] Read more.
Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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23 pages, 7367 KiB  
Article
Trajectory Tracking and Load Monitoring for Moving Vehicles on Bridge Based on Axle Position and Dual Camera Vision
by Dongdong Zhao, Wei He, Lu Deng, Yuhan Wu, Hong Xie and Jianjun Dai
Remote Sens. 2021, 13(23), 4868; https://doi.org/10.3390/rs13234868 - 30 Nov 2021
Cited by 10 | Viewed by 3163
Abstract
Monitoring traffic loads is vital for ensuring bridge safety and overload controlling. Bridge weigh-in-motion (BWIM) technology, which uses an instrumented bridge as a scale platform, has been proven as an efficient and durable vehicle weight identification method. However, there are still challenges with [...] Read more.
Monitoring traffic loads is vital for ensuring bridge safety and overload controlling. Bridge weigh-in-motion (BWIM) technology, which uses an instrumented bridge as a scale platform, has been proven as an efficient and durable vehicle weight identification method. However, there are still challenges with traditional BWIM methods in solving the inverse problem under certain circumstances, such as vehicles running at a non-constant speed, or multiple vehicle presence. For conventional BWIM systems, the velocity of a moving vehicle is usually assumed to be constant. Thus, the positions of loads, which are vital in the identification process, is predicted from the acquired speed and axle spacing by utilizing dedicated axle detectors (installed on the bridge surface or under the bridge soffit). In reality, vehicles may change speed. It is therefore difficult or even impossible for axle detectors to accurately monitor the true position of a moving vehicle. If this happens, the axle loads and bridge response cannot be properly matched, and remarkable errors can be induced to the influence line calibration process and the axle weight identification results. To overcome this problem, a new BWIM method was proposed in this study. This approach estimated the bridge influence line and axle weight by associating the bridge response and axle loads with their accurate positions. Binocular vision technology was used to continuously track the spatial position of the vehicle while it traveled over the bridge. Based on the obtained time–spatial information of the vehicle axles, the ordinate of influence line, axle load, and bridge response were correctly matched in the objective function of the BWIM algorithm. The influence line of the bridge, axle, and gross weight of the vehicle could then be reliably determined. Laboratory experiments were conducted to evaluate the performance of the proposed method. The negative effect of non-constant velocity on the identification result of traditional BWIM methods and the reason were also studied. Results showed that the proposed method predicted bridge influence line and vehicle weight with a much better accuracy than conventional methods under the considered adverse situations, and the stability of BWIM technique also was effectively improved. The proposed method provides a competitive alternative for future traffic load monitoring. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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17 pages, 3545 KiB  
Article
Artificial Intelligence Based Structural Assessment for Regional Short- and Medium-Span Concrete Beam Bridges with Inspection Information
by Ye Xia, Xiaoming Lei, Peng Wang and Limin Sun
Remote Sens. 2021, 13(18), 3687; https://doi.org/10.3390/rs13183687 - 15 Sep 2021
Cited by 14 | Viewed by 3379
Abstract
The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based [...] Read more.
The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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18 pages, 6417 KiB  
Article
The Integration of Two Interferometric Radars for Measuring Dynamic Displacement of Bridges
by Piotr Olaszek, Andrzej Świercz and Francesco Boscagli
Remote Sens. 2021, 13(18), 3668; https://doi.org/10.3390/rs13183668 - 14 Sep 2021
Cited by 12 | Viewed by 1896
Abstract
Measurements of displacements of bridges under dynamic load are particularly difficult in the case of structures where access to the area under the tested structure is impossible. Then, remote measurement methods are preferred, such as interferometric radar. Interferometric radar has high accuracy when [...] Read more.
Measurements of displacements of bridges under dynamic load are particularly difficult in the case of structures where access to the area under the tested structure is impossible. Then, remote measurement methods are preferred, such as interferometric radar. Interferometric radar has high accuracy when measuring displacement in the direction of its target axis. The problems appear when a bridge vibrates in two directions: horizontal (lateral or longitudinal) and vertical. The use of one radar to measure those vibrations may be impossible. This paper presents the application of a set of two interferometric radars to measure vertical vibration and horizontal longitudinal vibration with high accuracy. The method was positively verified by experimental tests on two railway bridges characterized by different levels of horizontal displacement. The accuracy of the radar measurements was tested by the direct measurement of vertical displacements using inductive gauges. In conclusion, in the case of vertical displacement measurements using one interferometric radar, the influence of horizontal displacements should be excluded. In the case of locating radars at the area of bridge supports, it is necessary to either use a set of two radars or first investigate the magnitude of possible horizontal displacements in relation to vertical displacements. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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22 pages, 4891 KiB  
Article
Quality Evaluation of Digital Twins Generated Based on UAV Photogrammetry and TLS: Bridge Case Study
by Masoud Mohammadi, Maria Rashidi, Vahid Mousavi, Ali Karami, Yang Yu and Bijan Samali
Remote Sens. 2021, 13(17), 3499; https://doi.org/10.3390/rs13173499 - 03 Sep 2021
Cited by 65 | Viewed by 6422
Abstract
In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known [...] Read more.
In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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19 pages, 8223 KiB  
Article
A Novel Slip Sensory System for Interfacial Condition Monitoring of Steel-Concrete Composite Bridges
by Faraz Sadeghi, Xinqun Zhu, Jianchun Li and Maria Rashidi
Remote Sens. 2021, 13(17), 3377; https://doi.org/10.3390/rs13173377 - 25 Aug 2021
Cited by 5 | Viewed by 1893
Abstract
Steel-concrete composite (SCC) beams are widely employed in bridge decks. The interfacial shear transfer between the top concrete slab and the supporting steel beams significantly affects the overall load carrying capacity and performance of a bridge deck. The inaccessibility of the connection system [...] Read more.
Steel-concrete composite (SCC) beams are widely employed in bridge decks. The interfacial shear transfer between the top concrete slab and the supporting steel beams significantly affects the overall load carrying capacity and performance of a bridge deck. The inaccessibility of the connection system makes the visual inspection difficult, and the traditional vibration-based methods are insensitive to this type of local damage. In this study, a novel interlayer slip monitoring system has been developed for interfacial condition assessment of SCC beams. The monitoring system is mainly based on the Ultra-flat Industrial Potentiometer Membrane (UIPM). The sensor film that is glued on a steel base is mounted on the concrete slab, and the wiper is installed on the steel beam. The interlayer slip between the concrete slab and steel beam is monitored by the relative displacement between the sensor film and the wiper. An experimental study has been carried out on a 6-m long composite bridge model in the laboratory. In the model, the concrete slab and the steel beams are bolt-connected, and the bolts could be loosened to simulate the defects in the shear connection system. Seven slip sensors are evenly installed along the bridge model. The sensors are calibrated using the testing machine before they are installed on the bridge model. Three damage scenarios are simulated by loosening bolts at different locations. Different loadings are also applied on the bridge to simulate the operational conditions. Undamaged and damaged scenarios have been considered within load increments, and data are collected and interpreted to find out how the slip changes. The results show that this system is reliable and efficient to monitor the interlayer slip for assessing the interface condition of composite structures. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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26 pages, 9181 KiB  
Article
Comprehensive Study of Moving Load Identification on Bridge Structures Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies
by Solmaz Pourzeynali, Xinqun Zhu, Ali Ghari Zadeh, Maria Rashidi and Bijan Samali
Remote Sens. 2021, 13(12), 2291; https://doi.org/10.3390/rs13122291 - 11 Jun 2021
Cited by 25 | Viewed by 3225
Abstract
Bridge infrastructures are continuously subject to degradation due to aging and excess loading, placing users at risk. It has now become a major concern worldwide, where the majority of bridge infrastructures are approaching their design life. This compels the engineering community to develop [...] Read more.
Bridge infrastructures are continuously subject to degradation due to aging and excess loading, placing users at risk. It has now become a major concern worldwide, where the majority of bridge infrastructures are approaching their design life. This compels the engineering community to develop robust methods for continuous monitoring of bridge infrastructures including the loads passing over them. Here, a moving load identification method based on the explicit form of Newmark-β method and Generalized Tikhonov Regularization is proposed. Most of the existing studies are based on the state space method, suffering from the errors of a large discretization and a low sampling frequency. The accuracy of the proposed method is investigated numerically and experimentally. The numerical study includes a single simply supported bridge and a three-span continuous bridge, and the experimental study includes a single-span simply supported bridge installed by sensors. The effects of factors such as the number of sensors, sensor locations, road roughness, measurement noise, sampling frequency and vehicle speed are investigated. Results indicate that the method is not sensitive to sensor placement and sampling frequencies. Furthermore, it is able to identify moving loads without disruptions when passing through supports of a continuous bridge, where most the existing methods fail. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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Review

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22 pages, 1810 KiB  
Review
Unmanned Aircraft System Applications in Damage Detection and Service Life Prediction for Bridges: A Review
by Hongze Li, Yanli Chen, Jia Liu, Zheng Zhang and Hang Zhu
Remote Sens. 2022, 14(17), 4210; https://doi.org/10.3390/rs14174210 - 26 Aug 2022
Cited by 5 | Viewed by 2441
Abstract
The increasing need for inexpensive, safe, highly efficient, and time-saving damage detection technology, combined with emerging technologies, has made damage detection by unmanned aircraft systems (UAS) an active research area. In the past, numerous sensors have been developed for damage detection, but these [...] Read more.
The increasing need for inexpensive, safe, highly efficient, and time-saving damage detection technology, combined with emerging technologies, has made damage detection by unmanned aircraft systems (UAS) an active research area. In the past, numerous sensors have been developed for damage detection, but these sensors have only recently been integrated with UAS. UAS damage detection specifically concerns data collection, path planning, multi-sensor fusion, system integration, damage quantification, and data processing in building a prediction model to predict the remaining service life. This review provides an overview of crucial scientific advances that marked the development of UAS inspection: underlying UAS platforms, peripherals, sensing equipment, data processing approaches, and service life prediction models. Example equipment includes a visual camera, a multispectral sensor, a hyperspectral sensor, a thermal infrared sensor, and light detection and ranging (LiDAR). This review also includes highlights of the remaining scientific challenges and development trends, including the critical need for self-navigated control, autonomic damage detection, and deterioration model building. Finally, we conclude with a brief discussion regarding the pros and cons of this emerging technology, along with a prospect of UAS technology research for damage detection. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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38 pages, 3375 KiB  
Review
UAV-Based Remote Sensing Applications for Bridge Condition Assessment
by Sainab Feroz and Saleh Abu Dabous
Remote Sens. 2021, 13(9), 1809; https://doi.org/10.3390/rs13091809 - 06 May 2021
Cited by 78 | Viewed by 8367
Abstract
Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection [...] Read more.
Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection techniques, such as visual inspection, are time-consuming, subjective, and often incomplete. Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. The primary objective of this study is to conduct a comprehensive review of the application of UAVs in bridge condition monitoring, used in conjunction with remote sensing technologies. Remote sensing technologies such as visual imagery, infrared thermography, LiDAR, and other sensors, integrated with UAVs for data acquisition are analyzed in depth. This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems. In addition to comparison of stand-alone and integrated NDT-UAV methods, the facilitation of bridge inspection using UAVs is thoroughly discussed in the present article in terms of ease of use, accuracy, cost-efficiency, employed data collection tools, and simulation platforms. Additionally, challenges and future perspectives of the reviewed UAV-NDT technologies are highlighted. Full article
(This article belongs to the Special Issue Bridge Monitoring Using Remote Sensors)
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