A Plastic Optical Fiber Sensing System for Bridge Deflection Measurement Deflection is one of the key parameters that reflects the state of a bridge. However, deflection measurement is difficult for a bridge that is under operation. Most existing sensors and measuring techniques often do not meet the requirements for health monitoring for various types of bridges. Therefore, based on changes of optical fiber intensity, a novel sensing system using connected pipes to measure bridge deflection in different positions is proposed in this paper. As an absolute reference, the liquid level position along the structure is adopted for the deflection measurement, and an additional external reference to the ground is not needed in this system. The proposed system consists of three parts: connected pipes to connect the measurement points along the structure, liquid to fill in the connected pipes, and the sensing element to detect the change of level. A plastic optical fiber sensor based on the intensity change is used as the sensing element of the developed system. Then, a set of experimental tests are conducted for performance evaluation purposes. Results show that this system has an accurate linear response and high reliability under various environmental conditions. The deflection of the test beam measured by the sensor agrees with linear variable differential transformer (LVDT) within an error margin of 2.1%. The proposed system shows great potential applicability for future health monitoring of longspan bridges.

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.