November 30, 2025MachinesOpen Access

Hybrid One-Dimensional Convolutional Neural Network—Recurrent Neural Network Model for Reconstructing Missing Data in Structural Health Monitoring Systems

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Accurate signal reconstruction was achieved under both single- and multi-channel loss conditions, enhancing reliability.
The best network configuration for single-channel loss yielded an MAE of 0.019 m/s² and R² of 0.987, confirming effectiveness.
Using a hybrid model of 1D-CNN and RNN allows for greater adaptability in practical applications of SHM.
The findings support the use of deep learning to address data loss challenges in structural health monitoring systems.

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Implication

This approach demonstrates reliable signal reconstruction in structural health monitoring, suggesting improved data recovery techniques using deep learning.

Cite This Study

Nga et al. (2025) studied this question.

synapsesocial.com/papers/692b9da91d383f2b2a37a689 https://doi.org/10.3390/machines13121101

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Implication

This approach demonstrates reliable signal reconstruction in structural health monitoring, suggesting improved data recovery techniques using deep learning.

Hybrid One-Dimensional Convolutional Neural Network—Recurrent Neural Network Model for Reconstructing Missing Data in Structural Health Monitoring Systems

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Implication

Cite This Study

Hybrid One-Dimensional Convolutional Neural Network—Recurrent Neural Network Model for Reconstructing Missing Data in Structural Health Monitoring Systems

Key Points

Discussion

Implication

Cite This Study

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