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Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review
Open accessAbstract
Applying computer science techniques such as artificial intelligence (AI), deep learning (DL), and computer vision (CV) on digital image data can help monitor and preserve cultural heritage (CH) sites. Defects such as weathering, removal of mortar, joint damage, discoloration, erosion, surface cracks, vegetation, seepage, and vandalism and their propagation with time adversely affect the structural health of CH sites. Several studies have reported damage detection in concrete and bridge structures using AI techniques. However, few studies have quantified defects in CH structures using the AI paradigm, and limited case studies exist for their applications. Hence, the application of AI-assisted visual inspections for CH sites needs to be explored. AI-assisted digital inspections assist inspection professionals and increase confidence levels in the damage assessment of CH buildings. This review summarizes the damage assessment techniques using image processing techniques, focusing mainly on DL techniques applied for CH conservation. Several case study applications of CH buildings are presented where AI can assist in traditional visual inspections.
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Studies report detection accuracies around 97%-99% in some bridge inspection cases.
“Studies report detection accuracies around 97%–99% in some bridge inspection cases.”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Mishra et al., 2022
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Artificial intelligence in civil engineering: emerging applications and opportunities
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Metadata
- Authors
- Mishra, M., & Lourenço, P. B.
- Affiliations
- University of Minho (PT)
- Corresponding author
- Mishra, Mayank
- Venue
- Journal of Cultural Heritage
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 66
- Pages
- 536-550
- ISSN
- 1296-2074 (ISSN-L)
- OpenAlex
- W4391202537
- Cited by
- 165
- References
- 90
- Citations by year
- 2026: 67 · 2025: 70 · 2024: 28
- FWCI
- 27.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Cultural Heritage (publishedVersion, cc-by)
- CINECA IRIS Institutional Research Information System (University of Basilicata) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- Funders
- EU Framework Programme for Research and Innovation Marie Skłodowska-Curie Actions (101063722)
- Fundação para a Ciência e a Tecnologia
- Ministério da Ciência, Tecnologia e Ensino Superior (UIDB / 04029/2020)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- artificial intelligence, visual inspection, cultural heritage preservation, damage detection, deep learning, computer vision, weathering, structural deterioration, historic buildings, image processing, concrete structures, bridges
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