Using a Machine Learning Algorithm Integrated with Data DeNoising Techniques to Optimize the Multipoint Sensor Network In this paper, for an intensity wavelength division multiplexing (IWDM)based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long shortterm memory (LSTM) machine learning algorithm, integrated with data denoising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDMbased FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDMbased FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signaltonoise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long shortterm memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (denoising) techniques. The DWT data denoising methods are important techniques for analyzing and denoising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after denoising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data denoising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDMbased FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deeplearning algorithm to perform structural damage detection. With a scaledown bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under tenfold crossvalidation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, knearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.