Application of machine learning models in predicting the compressive strength of GFRP-confined concrete




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Author
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Mai Anh DucThe University of Danang - University of Science and Technology, Vietnam
Từ khóa:
Tóm tắt
Accurately predicting the compressive strength of GFRP-confined concrete plays a key role in the use of GFRP in strengthening reinforced concrete structures. This study predicts the compressive strength of GFRP-confined concrete using five machine learning (ML) models. The performance of the ML models was compared with that of the design-oriented stress-strain model suggested by ACI 440.2R-17 for FRP-confined concrete. A dataset of 167 test results of GFRP-confined concrete was used for training and testing the ML models. It has been found that the predicted results obtained from the ML models were in good agreement with the test results. Furthermore, the ML models outperformed the design-oriented stress-strain model in predicting the compressive strength of GFRP-confined concrete. Among the machine learning models, the RandomTree and RandomForest models demonstrated the highest prediction accuracy.
Tài liệu tham khảo
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