ASR crack identification in bridges using deep learning and texture analysis Article Swipe
Andy Nguyễn
,
Vahidreza Gharehbaghi
,
Ngoc Thach Le
,
Lucinda Sterling
,
Umar Inayat Chaudhry
,
Shane Crawford
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.istruc.2023.02.042
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.istruc.2023.02.042
Alkali-Silica Reaction (ASR), commonly known as 'concrete cancer,' is an expansive reaction occurring over time between aggregate constituents and alkaline hydroxides from cement. As a destructive phenomenon, the need to detect the onset of ASR in concrete structures to ensure their long-term durability and structural integrity is thus evidenced. In the structural health monitoring field, vision-based approaches have been found to be viable, fast, and cost-effective in diagnosing numerous types of cracks using physical attributes and surface patterns. However, achieving high accuracy in detecting ASR cracks using traditional visual inspection techniques is challenging and time-consuming. Inspired by artificial intelligence technology, this paper proposes and evaluates a two-phase computer vision procedure for detecting and classifying ASR cracks utilizing a collection of ASR images recorded from several bridges in Queensland, Australia. In the first phase, the procedure compares common pre-trained CNN models to investigate their capability in classifying ASR cracks and to select the best-performed model. In the second phase, a novel Feature Enhancement Process (FEP) was first proposed to increase the contrast between ASR cracks and the heavily textured backgrounds within the images. Next, to better highlight the ASR crack features, the feature-adjusted images are processed further through different texture analysis algorithms including: (i) Texture Morphology, (ii) Adaptive thresholding, and (iii) Local range filtering. The study shows that the proposed FEP can improve the ASR crack classification accuracy of InceptionV3, which is the best CNN model selected from Phase 1, from 90.9% to 92.48%. Furthermore, by combining FEP with texture morphology, a robust two-stage tool for assessing ASR cracks can be made with an impressive validation accuracy of 94.07%. This research contributes towards the application of novel AI deep learning technology in providing cost-effective autonomous ASR crack classification tools to support the owners and managers of civil public works assets and other constructed infrastructures.
Related Topics
Concepts
Feature (linguistics)
Computer science
Thresholding
Artificial intelligence
Expansive
Texture (cosmology)
Durability
Process (computing)
Pattern recognition (psychology)
Identification (biology)
Alkali–silica reaction
Aggregate (composite)
Computer vision
Materials science
Image (mathematics)
Database
Composite material
Philosophy
Biology
Operating system
Botany
Compressive strength
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.istruc.2023.02.042
- OA Status
- hybrid
- Cited By
- 31
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321180431
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4321180431Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.istruc.2023.02.042Digital Object Identifier
- Title
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ASR crack identification in bridges using deep learning and texture analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-17Full publication date if available
- Authors
-
Andy Nguyễn, Vahidreza Gharehbaghi, Ngoc Thach Le, Lucinda Sterling, Umar Inayat Chaudhry, Shane CrawfordList of authors in order
- Landing page
-
https://doi.org/10.1016/j.istruc.2023.02.042Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.istruc.2023.02.042Direct OA link when available
- Concepts
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Feature (linguistics), Computer science, Thresholding, Artificial intelligence, Expansive, Texture (cosmology), Durability, Process (computing), Pattern recognition (psychology), Identification (biology), Alkali–silica reaction, Aggregate (composite), Computer vision, Materials science, Image (mathematics), Database, Composite material, Philosophy, Biology, Operating system, Botany, Compressive strength, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 12, 2023: 9Per-year citation counts (last 5 years)
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33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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