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Detection and quantification of cracks in concrete bridges using drone-image inspection
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.ORCID iD: 0009-0003-2220-2770
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The assessment of civil infrastructure plays an important role in ensuring the safety of the general public and the durability of structures. Traditional inspection methods often involve manual labour and subjective evaluations, resulting in limitations in efficiency and accuracy. In recent years, there has been an increasing interest on using advanced technologies, such as unmanned aerial vehicles (UAVs), image analysis and machine learning techniques, to establish them as alternatives for the inspection process. These techniques provide different advantages compared with the manual method in terms of time, objectivity, and safety. The results of these techniques can allow the engineers in charge of the assessment and maintenance planning to obtain detailed results that can improve their efficiency but they are not without challenges. This research project aims to evaluate different methods used for damage detection and quantification and their integration with UAVs as an alternative to structural inspections. The proposed methodology combines image analysis techniques, Convolutional Neural Networks (CNNs) with drones to address the different aspects of inspections, from the data gathering to the damage detection and quantification. This methodology focuses on detecting and quantifying small cracks as narrow as 0.1 mm on concrete structures, aiming to achieve results comparable to those of traditional inspection. Furthermore, an application demonstrating the feasibility of the methodology in inside environments is also presented, focusing on the inspection of the internal section of a box girder bridge, including the creation of 3Dphotogrammetrical models to improve the inspection process. The results of the thesis highlight the methodology’s capabilities in detecting small cracks with a high probability and the possibility to use it for inside inspections without ideal conditions. This demonstrates the potential of integrating the different methods to transform structural inspections toward more efficient methodologies. Furthermore, the analysis of the results evaluates the drawbacks encountered and outlines future research directions aimed at advancing image-based inspections and their practical application.

Abstract [sv]

Bedömningen av civil infrastruktur spelar en viktig roll för att säkerställa allmänhetens säkerhetoch strukturernas hållbarhet. Traditionella inspektionsmetoder innebär ofta manuellt arbete ochsubjektiva bedömningar, vilket leder till begränsningar i effektivitet och noggrannhet. Undersenare år har intresset ökat för att använda avancerad teknik, såsom obemannade flygfarkoster(UAV), bildanalys och maskininlärningstekniker, för att etablera dem som alternativ förinspektionsprocessen. Dessa tekniker ger olika fördelar jämfört med den manuella metoden närdet gäller tid, objektivitet och säkerhet. Resultaten från dessa tekniker kan ge ingenjörer somansvarar för bedömning och underhållsplanering detaljerade resultat som kan förbättra deraseffektivitet, men de är inte utan utmaningar.

Detta forskningsprojekt syftar till att utvärdera olika metoder som används för att upptäcka ochkvantifiera skador och deras integration med UAV som ett alternativ till manuella inspektioner.Den föreslagna metoden kombinerar bildanalystekniker, Convolutional Neural Networks(CNN) med drönare för att hantera de olika aspekterna av inspektioner, från datainsamling tillskadedetektering och kvantifiering. Metoden fokuserar på att upptäcka och kvantifierasmåsprickor som är så små som 0.1 mm på betongkonstruktioner och syftar till att uppnå resultatsom är jämförbara med dem vid traditionell inspektion. Dessutom presenteras en tillämpningsom visar att metoden är genomförbar i inomhusmiljöer, med fokus på inspektion av den inresektionen av en lådbalkbro, inklusive skapandet av 3D-fotogrammetriska modeller för attförbättra inspektionsprocessen.

Resultaten i uppsatsen belyser metodikens förmåga att upptäcka mikrosprickor med högsannolikhet och möjligheten att använda tekniken som en del av inomhusinspektioner utanidealiska förhållanden. Detta visar potentialen i att integrera de olika metoderna för att bidratill att omvandla manuella inspektioner mot mer effektiva metoder. Dessutom utvärderaranalysen av resultaten de nackdelar som uppstått och skisserar framtida forskningsriktningarsom syftar till att främja bildbaserade inspektioner och deras praktiska tillämpning.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2024. , p. 40
Series
TRITA-ABE-DLT ; 2417
National Category
Infrastructure Engineering
Research subject
Civil and Architectural Engineering, Structural Engineering and Bridges
Identifiers
URN: urn:nbn:se:kth:diva-346571ISBN: 978-91-8040-965-0 (print)OAI: oai:DiVA.org:kth-346571DiVA, id: diva2:1858916
Presentation
2024-06-14, M108, Brinellvägen 23, KTH Campus, public video conference link https://kth-se.zoom.us/j/62274505390, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20240521

Available from: 2024-05-21 Created: 2024-05-20 Last updated: 2024-06-10Bibliographically approved
List of papers
1. Image based inspection of concrete cracks using UAV photography
Open this publication in new window or tab >>Image based inspection of concrete cracks using UAV photography
2022 (English)In: Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability / [ed] Casas JM, Frangopol DM, Turmo J, London: CRC Press, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Traditional inspections are typically performed manually. They require specialized  equipment, which usually is expensive, time-consuming and can often be a source of risk.  Unmanned Aerial Vehicles (UAV) provide an alternative to overcome the challenges of  traditional manual inspections. This has been stud-ied as well as the use of machine  learning and image analysis algorithms for detection and quantification of damages, in  particular cracks. This paper presents a framework for inspections that combines data  acquisition, crack detection, and the quantification of essential parameters of concrete  cracks. From this method, the width and length of cracks are determined and compared  with control measurements to estimate the accuracy of the method. The results show that  using the pre-trained network to detect cracks, only a few to no false negatives are  obtained. From the images classified as containing cracks, the quantification methodology is performed obtaining measurements of crack width down to 0.13 mm. 

Place, publisher, year, edition, pages
London: CRC Press, 2022
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-324970 (URN)10.1201/9781003322641 (DOI)
Conference
Eleventh International Conference on Bridge Maintenance, Safety and Management (IABMAS 2022)
Note

QC 20230328

Available from: 2023-03-22 Created: 2023-03-22 Last updated: 2024-05-20Bibliographically approved
2. Image-Based Concrete Crack Detection Method Using the Median Absolute Deviation
Open this publication in new window or tab >>Image-Based Concrete Crack Detection Method Using the Median Absolute Deviation
2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 9, p. 2736-2752Article in journal (Refereed) Published
Abstract [en]

This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method’s performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method had higher accuracy, precision, and F2 score values than the Otsu and Niblack methods.

Place, publisher, year, edition, pages
MDPI AG, 2024
Keywords
crack detection; probability of detection; median absolute value; thresholding; computer vision; damage detection
National Category
Infrastructure Engineering
Research subject
Civil and Architectural Engineering, Structural Engineering and Bridges
Identifiers
urn:nbn:se:kth:diva-346435 (URN)10.3390/s24092736 (DOI)001219995600001 ()38732844 (PubMedID)2-s2.0-85192716460 (Scopus ID)
Note

QC 20240527

Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-27Bibliographically approved
3. Drone-based photogrammetric inspection of interior box girder bridges
Open this publication in new window or tab >>Drone-based photogrammetric inspection of interior box girder bridges
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Digital inspections methods have gained increasing interest in the civil engineering field for assessing the condition of various structures including buildings and bridges. These methods are used primarily to evaluate the external surface condition of the structure. However, in the case of bridges, their application when inspecting the interior of these structures remains uncommon. This paper presents a case study for inspecting the interior of a concrete box girder bridge, the Strängnäs bridge. The inspection involved gathering data using a commercial drone, creating a photogrammetrical model, detecting and quantifying damages by employing a Convolutional Neural Network (CNN). The analysis successfully detected 60 cm long concrete cracks, a total area of 3.5 m2 leakage and corrosion over 40 cm. The study addressed the difficulties such as insufficient lighting, lack of GPS signal and dust clouds reducing visibility. Despite these obstacles, the study demonstrated the effectiveness of indoor drone-based inspections.

National Category
Infrastructure Engineering
Research subject
Civil and Architectural Engineering, Structural Engineering and Bridges
Identifiers
urn:nbn:se:kth:diva-346437 (URN)
Note

QC 20240520

Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-20Bibliographically approved

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Avendaño, Juan Camilo

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