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Performance Evaluation of Deep Learning Approaches in In-Situ Nondestructive Testing

  • 2025
  • OriginalPaper
  • Chapter
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Authors
Thi Tuyet Nga Phu
Hong Giang Nguyen
Published in
Proceedings of Tenth International Congress on Information and Communication Technology
Publisher
Springer Nature Singapore

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Abstract

This chaptere delves into the performance evaluation of deep learning models, specifically LSTM and GRU, in predicting the compressive strength of concrete using non-destructive testing (NDT) methods. The study focuses on the impact of different activation functions (AFs) on the accuracy of these models, comparing fixed, parametric, and non-parametric AFs. Key findings include the superior performance of the GRU model with the tanhLU activation function, achieving the highest predictive accuracy. The research also highlights the importance of data normalization and the use of combined RH and UPV methods for enhancing prediction reliability. The study concludes that deep learning models, particularly GRU with specific AFs, offer a viable and efficient approach for evaluating concrete compressive strength, reducing the need for destructive testing and human resources.
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Title
Performance Evaluation of Deep Learning Approaches in In-Situ Nondestructive Testing
Authors
Thi Tuyet Nga Phu
Hong Giang Nguyen
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Proceedings of Tenth International Congress on Information and Communication Technology

Print ISBN: 978-981-9664-37-5

Electronic ISBN: 978-981-9664-38-2

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-96-6438-2_16
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