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Bridge inspections using unmanned aerial vehicles – A case study in Sweden
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0001-9423-7436
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0002-1375-3322
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0001-5187-2552
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.ORCID iD: 0000-0002-8682-876x
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2021 (English)Report (Other academic)
Abstract [en]

The aim of the current project is to digitalize inspections and monitoring of structures’ health using drones in order to identify and allow for easier inspection of damages in transport infrastructure. The objectives set are to perform aerial photogrammetry to recreate the as-is condition to enable off-site inspection of difficult to reach areas in structures and identify damages – e.g. cracks, spalling, corrosion. The drone is controlled either autonomously or with the use of a remote control by a pilot from the ground. The drone can carry a wide range of imaging technologies including still, video and infrared sensors. The high flexibility and accessibility of drones in hard-to-reach or risk exposed areas makes the airborne photogrammetry a better alternative to the ground-based method. Given the potential of UAVs to help bridge inspectors performing inspections off-site, the Swedish Transport Administration developed a demonstration project to evaluate the effectiveness and future opportunities within inspection field. Five bridges of varying sizes and types were selected as demonstrators. Data collection including the 3D model creation has been performed by three different contractors while the model-based inspection for all bridges was performed by the same team. It has been shown that the 3D models could serve as a tool for bridge inspectors from which measurements could be extracted and certain damages identified. A full off-site inspection is currently not feasible as some areas of the bridges were difficult to capture. The models are only providing near-surface information, and therefore, in-depth inspection should not be overlooked. The difficulty to capture local defects such as delaminations and narrow cracks also reduces versatility. The main conclusion from the study is that drones cannot be used independently to conduct inspections. Currently, they can only be used as a complement to traditional inspections. The added value of a 3D model derives from the possibility of using it as tool to better plan large inspections in the field and/or future maintenance work.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2021. , p. 37
Series
Technical report / Luleå University of Technology, ISSN 1402-1536
Keywords [en]
Drone inspection, Bridge inspections, Unmanned aerial vehicles
National Category
Infrastructure Engineering
Research subject
Structural Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-83454ISBN: 978-91-7790-814-2 (electronic)OAI: oai:DiVA.org:ltu-83454DiVA, id: diva2:1540949
Available from: 2021-03-30 Created: 2021-03-30 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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Popescu, CosminMirzazade, AliOhlsson, UlfSas, Gabriel

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