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Concrete aging factor prediction using machine learning
Woubishet Zewdu Taffese
, Gustavo Bosel Wally
, Fábio Costa Magalhães
,
Leonardo Espinosa-Leal
Not published at VTT
Arcada University of Applied Sciences
Catholic University of Pelotas
Federal University of Rio Grande do Sul
Research output
:
Contribution to journal
›
Article
›
Scientific
›
peer-review
12
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Citations (Scopus)
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Keyphrases
Machine Learning
100%
Mean Absolute Error
100%
Root Mean Square Error
100%
Concrete Deterioration
100%
Mean Square Error
100%
Age Factors
100%
Pozzolan
100%
Exposure Conditions
50%
Prediction Accuracy
50%
Random Forest
50%
Superior Performance
50%
Innovative Methods
50%
Machine Learning Techniques
50%
Machine Learning Models
50%
Performance-based
50%
Experimental Testing
50%
Model Performance
50%
Cement Type
50%
Cement Content
50%
Coefficient of Determination
50%
Concrete Age
50%
XGBoost
50%
Gradient Boosting
50%
AdaBoost
50%
LightGBM
50%
LightGBM Algorithm
50%
Reinforced Concrete Design
50%
CatBoost
50%
Exposure Age
50%
Engineered Features
50%
Engineering
Root Mean Square Error
100%
Mean Square Error
100%
Learning System
100%
Mean Absolute Error
100%
Exposure Condition
50%
Cement Content
50%
Accurate Prediction
50%
Random Forest
50%
Input Feature
50%
Machine Learning Technique
50%
Adaboost
50%
Reinforced Concrete Design
50%
INIS
machine learning
100%
aging
100%
errors
100%
prediction
100%
performance
66%
algorithms
33%
roots
33%
datasets
33%
cements
33%
durability
16%
comparative evaluations
16%
resources
16%
design
16%
forests
16%
reinforced concrete
16%
testing
16%
randomness
16%
Material Science
Reinforced Concrete
50%