Image Analysis and Functional Data Clustering for Random Shape Aggregate Models Article Swipe
Jonghyun Yun
,
Sanggoo Kang
,
Amin Darabnoush Tehrani
,
Suyun Ham
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/math8111903
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/math8111903
This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.
Related Topics
Concepts
Aggregate (composite)
Cluster analysis
Computer science
Range (aeronautics)
Mesoscale meteorology
Image (mathematics)
Data mining
Artificial intelligence
Pattern recognition (psychology)
Algorithm
Machine learning
Geology
Engineering
Materials science
Nanotechnology
Climatology
Aerospace engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math8111903
- https://www.mdpi.com/2227-7390/8/11/1903/pdf?version=1604135428
- OA Status
- gold
- Cited By
- 2
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3095690567
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3095690567Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/math8111903Digital Object Identifier
- Title
-
Image Analysis and Functional Data Clustering for Random Shape Aggregate ModelsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-31Full publication date if available
- Authors
-
Jonghyun Yun, Sanggoo Kang, Amin Darabnoush Tehrani, Suyun HamList of authors in order
- Landing page
-
https://doi.org/10.3390/math8111903Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7390/8/11/1903/pdf?version=1604135428Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-7390/8/11/1903/pdf?version=1604135428Direct OA link when available
- Concepts
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Aggregate (composite), Cluster analysis, Computer science, Range (aeronautics), Mesoscale meteorology, Image (mathematics), Data mining, Artificial intelligence, Pattern recognition (psychology), Algorithm, Machine learning, Geology, Engineering, Materials science, Nanotechnology, Climatology, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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