Structural damage identification using millimeter wave imaging and image processing

A. Noori Hoshyar, S. Kharkovsky, B. Samali

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

In the past decades, structural health monitoring (SHM) has received wide attention in preventing the sudden failure of structural components by identifying the damages in its early stages. Basically, to ensure the safety and reducing the serviceabili Otsu's thresholding ty of civil infrastructure it is important to inspect and assess the physical and functional condition of structures. Currently, manual inspection is the main form of assessing the conditions to ensure structure still meets the safety requirements. However, there are still several accidents that are reported as a result of insufficient inspection and conditional assessment of structures. In order to prevent further incidents, it is necessary to continuously inspect and assess the condition of structures with appropriate techniques. This is why the development and application of efficient non-destructive testing and computer vision methods for infrastructure health monitoring are in demand. This paper present the developed smart damage detection system for a local infrastructure health monitoring which complement the image-based damage detection methods in hazardous scenarios. The system is based on a relatively simple millimeter wave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of automatic imaging of flaws such as cracks in a steel plate at different standoff distances, monitoring the crack to avoid further deformation through a sequence of inspections at intervals. However, in some cases the original images based on measured data do not provide desired information, leading to developing the image processing algorithm to enhance the imaging result. The proposed algorithm is based on Otsu's thresholding method and Prewitt approximation in six directions in an image which has been created from the measured data to facilitate the structural damage identification. The proposed algorithm can successfully enhance the quality of images and visualize the crack in a steel plate under dielectric coating at different standoff distances.

Original languageEnglish
Title of host publicationProceedings of the SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure
EditorsSaeed Mahini, Tommy Chan
Place of PublicationManitoba
PublisherInternational Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
Pages294-303
Number of pages10
ISBN (Electronic)9781925553055
ISBN (Print)9781510864573
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII 2017): Structural Health Monitoring in Real-World Application - Brisbane, Australia
Duration: 5 Dec 20178 Dec 2017
Conference number: 8th

Conference

Conference8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII 2017)
CountryAustralia
CityBrisbane
Period5/12/178/12/17

Fingerprint

Millimeter waves
Image processing
Damage detection
Inspection
Cracks
Imaging techniques
Monitoring
Health
Steel
Reflectometers
Structural health monitoring
Nondestructive examination
Computer vision
Accidents
Waveguides
Antennas
Coatings
Defects

Cite this

Noori Hoshyar, A., Kharkovsky, S., & Samali, B. (2017). Structural damage identification using millimeter wave imaging and image processing. In S. Mahini, & T. Chan (Eds.), Proceedings of the SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (pp. 294-303). Manitoba: International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII.
Noori Hoshyar, A. ; Kharkovsky, S. ; Samali, B. / Structural damage identification using millimeter wave imaging and image processing. Proceedings of the SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure. editor / Saeed Mahini ; Tommy Chan. Manitoba : International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII, 2017. pp. 294-303
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title = "Structural damage identification using millimeter wave imaging and image processing",
abstract = "In the past decades, structural health monitoring (SHM) has received wide attention in preventing the sudden failure of structural components by identifying the damages in its early stages. Basically, to ensure the safety and reducing the serviceabili Otsu's thresholding ty of civil infrastructure it is important to inspect and assess the physical and functional condition of structures. Currently, manual inspection is the main form of assessing the conditions to ensure structure still meets the safety requirements. However, there are still several accidents that are reported as a result of insufficient inspection and conditional assessment of structures. In order to prevent further incidents, it is necessary to continuously inspect and assess the condition of structures with appropriate techniques. This is why the development and application of efficient non-destructive testing and computer vision methods for infrastructure health monitoring are in demand. This paper present the developed smart damage detection system for a local infrastructure health monitoring which complement the image-based damage detection methods in hazardous scenarios. The system is based on a relatively simple millimeter wave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of automatic imaging of flaws such as cracks in a steel plate at different standoff distances, monitoring the crack to avoid further deformation through a sequence of inspections at intervals. However, in some cases the original images based on measured data do not provide desired information, leading to developing the image processing algorithm to enhance the imaging result. The proposed algorithm is based on Otsu's thresholding method and Prewitt approximation in six directions in an image which has been created from the measured data to facilitate the structural damage identification. The proposed algorithm can successfully enhance the quality of images and visualize the crack in a steel plate under dielectric coating at different standoff distances.",
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Noori Hoshyar, A, Kharkovsky, S & Samali, B 2017, Structural damage identification using millimeter wave imaging and image processing. in S Mahini & T Chan (eds), Proceedings of the SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure. International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII, Manitoba, pp. 294-303, 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII 2017), Brisbane, Australia, 5/12/17.

Structural damage identification using millimeter wave imaging and image processing. / Noori Hoshyar, A.; Kharkovsky, S.; Samali, B.

Proceedings of the SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure. ed. / Saeed Mahini; Tommy Chan. Manitoba : International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII, 2017. p. 294-303.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AU - Samali, B.

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SN - 9781510864573

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PB - International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII

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Noori Hoshyar A, Kharkovsky S, Samali B. Structural damage identification using millimeter wave imaging and image processing. In Mahini S, Chan T, editors, Proceedings of the SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure. Manitoba: International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII. 2017. p. 294-303