  Data loss in Structural Health Monitoring (SHM) networks has recently become
one of the main challenges for engineers. Therefore, a data recovery method for
SHM, generally an expensive procedure, is essential. Lately, some techniques
offered to recover this valuable raw data using Neural Network (NN) algorithms.
Among them, the convolutional neural network (CNN) based on convolution, a
mathematical operation, can be applied to non-image datasets such as signals to
extract important features without human supervision. However, the effect of
different parameters has not been studied and optimized for SHM applications.
Therefore, this paper aims to propose different architectures and investigate
the effects of different hyperparameters for one of the newest proposed
methods, which is based on a CNN algorithm for the Alamosa Canyon Bridge as a
real structure. For this purpose, three different CNN models were considered to
predict one and two malfunctioned sensors by finding the correlation between
other sensors, respectively. Then the CNN algorithm was trained by experimental
data, and the results showed that the method had a reliable performance in
predicting Alamosa Canyon Bridge's missed data. The accuracy of the model was
increased by adding a convolutional layer. Also, a standard neural network with
two hidden layers was trained with the same inputs and outputs of the CNN
models. Based on the results, the CNN model had higher accuracy, lower
computational cost, and was faster than the standard neural network.
