This document summarizes a study that used an artificial neural network containing two hidden layers to predict the carbonation depth of concrete. The neural network model was trained on a database of 300 concrete samples that varied in binder content, fly ash percentage, water-to-binder ratio, carbon dioxide concentration, relative humidity, square root of time, and measured carbonation depth. The results showed that the neural network performed well at predicting carbonation depth, with an R2 value of 0.9536, RMSE of 1.7761, and MAE of 1.0325. The study concluded that neural networks are a useful machine learning technique for predicting carbonation depth and could help reduce the need for physical experiments.

1. EMERGING TECHNOLOGIES
AND APPLICATIONS
FOR GREEN INFRASTRUCTURE
Using Artificial Neural Network Containing Two
Hidden Layers for Predicting Carbonation
Depth of Concrete
Van Quan Tran
University of Transport Technology
Hanoi, 28 October 2021

2. Summary
Context Approach
and Method
Results and
Discussion
Conclusion

3. Context
Approach and
Method
Results and
Discussion
Conclusion

4. Context
Approach and
Method
Results and
Discussion
Conclusion

5. Context
Approach and
Method
Results and
Discussion
Conclusion

6. Context
Approach and
Method
Results and
Discussion
Conclusion
x1 Binder: Cement+ Fly ash
Water
W/B

7. Context
Approach and
Method
Results and
Discussion
Conclusion
x1
Xi-1
Xi
Binder: Cement+ Fly ash
Water
W/B
Time

8. Context
Approach and
Method
Results and
Discussion
Conclusion
x1
Xi-1
Xi
Binder: Cement+ Fly ash
Water
W/B

9. Context
Approach and
Method
Results and
Discussion
Conclusion
x1
Xi-1
Xi
Binder: Cement+ Fly ash
Water
W/B
Time

10. Context
Approach and
Method
Results and
Discussion
Conclusion
x1
Xi-1
Xi
Binder: Cement+ Fly ash
Water
W/B
Time
X=???

11. Context
Approach and
Method
Results and
Discussion
Conclusion
 
 
k
2
i i
2 i=1
k
2
i
i=1
v - v
R = 1-
v - v


(2)
 
k
2
i i
i=1
1
RMSE = v - v
k

(3)
 
N
i i
j=1
1
MAE = v - v
N

(4)
Machine learning algorithm: Artificial Neural Network
Model performance

12. Context
Approach and
Method
Results and
Discussion
Conclusion
Data analysis
Description of database
Unit count mean std min average max
Binder
kg/
m3 300.00 369.68 53.93 260.00 350.00 500.00
Fly ash % 300.00 21.44 20.56 0.00 17.50 70.00
Water/Binder - 300.00 0.45 0.09 0.28 0.45 0.63
[CO2] % 300.00 9.08 11.65 1.00 6.50 50.00
RH % 300.00 64.25 3.66 55.00 65.00 70.00
t^0.5 days 300.00 7.60 4.41 1.73 6.48 19.08
x mm 300.00 9.05 9.34 0.00 6.00 57.00

13. Context
Approach and
Method
Results and
Discussion
Conclusion
Correlation
between
experimental and
predicted residual
friction angle

14. Context
Approach and
Method
Results and
Discussion
Conclusion
 The ability of machine learning technique to predict the carbonation depth of
concrete was shown.
 To save time and money for conducting experiments, an ANN model one of the
most powerful algorithms was developed
 The carbonation depth of concrete has been predicted by ANN models with
network structure [6-12-8-1].
 The results indicated that ANN models performed well for predicting the
carbonation depth of concrete with higher performance such as R2, RMSE and
MAE value to be equal to 0.9536, 1.7761, 1.0325, respectively
 A performance comparison between ANN model and different machine learning
models such as tree model, random forest, support vector machine seems to be
interesting to perform in the future.

15. https://cigos2021.sciencesconf.org/
EMERGING TECHNOLOGIES
AND APPLICATIONS
FOR GREEN INFRASTRUCTURE
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