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Simulation-Based Transfer Learning for Concrete Strength Prediction
Zhanzhao Li
, Te Pei
, Weichao Ying
, Wil V. Srubar
, Rui Zhang
, Jinyoung Yoon
, Hailong Ye
, Ismaila Dabo
,
Aleksandra Radlińska
Materials Science and Engineering
Materials Research Institute (MRI)
Institute of Energy and the Environment (IEE)
Civil and Environmental Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
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Chapter
5
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Scopus citations
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Keyphrases
Physics-based Model
100%
Simulation-based
100%
Transfer Learning
100%
Machine Learning
50%
Physical Model
50%
Learning Process
50%
Training Samples
50%
Prediction Accuracy
50%
Concrete Compressive Strength
50%
Generalization Performance
50%
Concrete Materials
50%
Learning Framework
50%
Engineering Domains
50%
Data-driven Methods
50%
Transfer Learning Model
50%
Generalization Ability
50%
Machine Learning Models
50%
Prior Domain Knowledge
50%
Scientific Domain
50%
Amount of Training
50%
Model Generalization
50%
Knowledge Embedding
50%
Data Scarcity
50%
Computer Science
Transfer Learning
100%
Learning System
66%
Machine Learning
66%
Speed-up
33%
Practical Solution
33%
Domain Knowledge
33%
Learning Approach
33%
Generalization Performance
33%
Inherent Complexity
33%
Physical Model
33%
Learning Framework
33%
Prediction Accuracy
33%
Domain Engineering
33%
Learning Process
33%
Training Sample
33%
Generalization Ability
33%
Engineering
Concrete Strength
100%
Transfer Learning
100%
Learning System
66%
Engineering
33%
Domain Knowledge
33%
Learning Approach
33%
Simulated Result
33%
Physical Model
33%
Concrete (Composite Building Material)
33%
Knowledge Source
33%
Concrete Compressive Strength
33%
Chemical Engineering
Learning System
100%