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Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0002-1375-3322
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering. SINTEF Narvik AS, Narvik, 8517, Norway.ORCID iD: 0000-0001-9423-7436
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0002-5154-7044
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0001-7799-5809
2021 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 13, no 14, article id 2665Article in journal (Refereed) Published
Abstract [en]

For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 13, no 14, article id 2665
Keywords [en]
Bridge 3D modeling, Bridge inspection, Computer vision, Damage assessment, Damage detection, Damage segmentation, Intelligent hierarchical DSfM, UAV, Unmanned inspections
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Structural Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-86504DOI: 10.3390/rs13142665ISI: 000677000500001Scopus ID: 2-s2.0-85110626604OAI: oai:DiVA.org:ltu-86504DiVA, id: diva2:1582461
Funder
Swedish Research Council Formas, 2019-01515
Note

Validerad;2021;Nivå 2;2021-08-02 (beamah)

Available from: 2021-08-02 Created: 2021-08-02 Last updated: 2023-10-20Bibliographically approved
In thesis
1. An Algorithmic Framework for Intelligent Concrete Structural Defects Detection and Classification
Open this publication in new window or tab >>An Algorithmic Framework for Intelligent Concrete Structural Defects Detection and Classification
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The primary objective of inspecting concrete civil structures is to gather information concerning the deterioration of concrete elements, including issues like concrete cover cracking, delamination, or corrosion. Typically, this data is documented through field inspection notes, hand-drawn sketches, and photographs. Unfortunately, this information is often stored in diverse formats, relying on close-range images and paper-based records. Moreover, the process heavily depends on the inspector's experience, structural knowledge, and familiarity with the material properties of the system under investigation. Traditional inspection methods have inherent limitations, as they generally focus on easily accessible areas due to time constraints, safety concerns, or the challenging terrains often encountered in the field. This is particularly relevant when inspecting large structures like bridges, where examining the entire area would be time-consuming and potentially unsafe. The transfer of knowledge from one inspection period to the next becomes problematic when different inspectors are involved. Hence, there is a compelling need to explore modern inspection and monitoring techniques for structures, with a focus on reducing disruption and enhancing the efficiency and reliability of data acquisition.

In this context, the previously published licentiate thesis was aimed to contribute by developing optical alternatives that complement existing techniques. These alternatives should be cost-effective, suitable for on-site application, and easily deployable. To align with the objectives of this research project, the following research questions were addressed before in the licentiate seminar:

1.    How accurate is Close-Range Photogrammetry (CRP) for monitoring geometric deviations and detecting defects?

2.    In pursuit of maximum accuracy and minimal computation time in crack detection, which convolutional neural network (CNN) architecture performs best in classification and semantic segmentation tasks?

3.    Is there a correlation between surface deformations in reinforced concrete, measured through Digital Image Correlation (DIC), and strains in the embedded reinforcement?

However, there are still challenges to be addressed. Concrete civil structure inspection involves more than just defect detection and measurement. In this final thesis, the objective is to discuss an algorithm for creating an intelligent machine capable of classifying concrete defects. This requires the computer, acting as the inspector, to possess substantial knowledge about the concrete structure, including protocols, standards, guidelines, and an understanding of the overall structure's status. Consequently, two additional research questions are introduced:

1.    Building on our previous discussion in the licentiate seminar regarding the correlation between surface deformations and strains in embedded rebars, we aim to enhance the accuracy of the strain estimation. To achieve this, we intend to train intelligent regression models using available experimental data and newly generated synthetic data. Research Question 1: How can we estimate strains on embedded rebars with the application of machine learning regression, employing a hybrid-learning approach? This question is explored in the paper "Prediction of strain in embedded rebars for RC member: application of a hybrid learning approach."

2.    While computer vision techniques are effective in defect detection, structural health assessment encompasses more than just identifying defects. The objective is to provide a comprehensive solution that bridges the gap between defect detection, classification, and assessment, ultimately contributing to a more accurate understanding of detected defects. Research Question 2: How can we bridge the gap between defect detection and classification to achieve effective defect classification? This question is the subject of the forthcoming manuscript, "Defect Classification and Structural Assessment of Concrete Bridges: A Data-Driven Decision-Making Approach".

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2023
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Civil Engineering Structures, defect detection, Structural Health Assessment, Bridge Inspection, Computer Vision, Point-Cloud Generation, Severity Assessment, Machine Learning Regression, Bridge 3D Modeling, Unmanned Inspections
National Category
Infrastructure Engineering Building Technologies
Research subject
Structural Engineering
Identifiers
urn:nbn:se:ltu:diva-101744 (URN)978-91-8048-416-9 (ISBN)978-91-8048-417-6 (ISBN)
Public defence
2023-12-14, F 341, Luleå tekniska universitet, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council Formas, 2019-01515
Available from: 2023-10-20 Created: 2023-10-20 Last updated: 2023-11-23Bibliographically approved

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Mirzazade, AliPopescu, CosminBlanksvärd, ThomasTäljsten, Björn

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