Tracking operating trains and identifying illegal intruders are two important and critical issues in subway safety management. One challenge is to find a reliable methodology that would enable these two needs to be addressed with high sensitivity and spatial resolution over a long-distance range. This paper proposes a novel monitoring approach based on distributed vibration, which is suitable for both train tracking and incursion detection. For an actual subway system, ultra-weak fiber Bragg grating (FBG) sensing technology was applied to collect the distributed vibration responses from moving trains and intruders. The monitoring data from the subway operation stage were directly utilized to evaluate the feasibility of the proposed method for tracking trains. Moreover, a field simulation experiment was performed to validate the possibility of detecting human intrusion. The results showed that the diagonal signal pattern in the distributed vibration response can be used to reveal the location and speed of the moving loads (e.g., train and intruders). Other train parameters, such as length and the number of compartments, can also be obtained from the vibration responses through cross-correlation and envelope processing. Experimental results in the time and frequency domains within the selected intrusion range indicated that the proposed method can distinguish designed intrusion cases in terms of strength and mode.

It is challenging for engineers to timely identify illegal ground intrusions in underground systems such as subways. In order to prevent the catastrophic collapse of subway tunnels from intrusion events, this paper investigated the capability of detecting the ground intrusion of underground structures based on dynamic measurement of distributed fiber optic sensing. For an actual subway tunnel monitored by the ultra-weak fiber optic Bragg grating (FBG) sensing fiber with a spatial resolution of five meters, a simulated experiment of the ground intrusion along the selected path was designed and implemented, in which a hydraulic excavator was chosen to exert intrusion perturbations with different strengths and modes at five selected intrusion sites. For each intrusion place, the distributed vibration responses of sensing fibers mounted on the tunnel wall and the track bed were detected to identify the occurrence and characteristics of the intrusion event simulated by the discrete and continuous pulses of the excavator under two loading postures. By checking the on-site records of critical moments in the intrusion process, the proposed detection approach based on distributed structural vibration responses for the ground intrusion can detect the occurrence of intrusion events, locate the intrusion ground area, and distinguish intrusion strength and typical perturbation modes.

Improving the accuracy and efficiency of damage detection of bridge structures is a major challenge in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic sensing technology and applying a deep-learning algorithm to perform structural damage detection. With a scaled-down bridge model, three categories of damage scenarios plus an intact state were simulated. A 13-layer supervised learning model based on the deep convolutional neural networks was proposed. After the training process of original continuous deflection under 10-fold cross-validation, the model accuracy can reach 96.9% for damage classification with the performance outperforming that of the other four methods (random forest = 81.6%, support vector machine = 79.9%, $k$ -nearest neighbor = 77.7%, and decision tree = 74.8%). The proposed model also demonstrated its decent abilities in automatically extracting damage features and distinguishing damage from structurally symmetrical locations.

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 deep-learning algorithm to perform structural damage detection. With a scale-down 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 ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.

The foundation of an intelligent highway network is the construction of a high-density distributed strain monitoring system, which is based on sensing elements that can sensitively capture external information. In this research, the development and application for the structure of a novel strained optical fiber cable based on the weak fiber Bragg grating (wFBG) arrays are discussed. A modulation and demodulation solution of wavelength division multiplexing combined with time division multiplexing is developed by utilizing the property by which the wavelength of the strained optical fiber cable is periodically switched. Further, the strain transfer model of the optical cable is analyzed hierarchically using the theory of elasticity. The strain transfer coefficients of the overhanging region and the gluing region are combined to deduce the sensitivity model of the strained optical fiber cable. Moreover, the finite element technique is integrated to optimize the structural parameters of the optical cable for high-sensitivity or large-scale range. The strained optical fiber cable based on wFBG arrays is applied to a steel-concrete composite bridge. The static and dynamic loading tests show that the sensing optical cable can be monitored for strain variation in order to realize the functions of lane identification, weighing vehicle tonnage as well as velocity discrimination.