Expanding sparse point deflection measurements to spatially continuous data via optical fiber sensors in long-span suspension bridges

In structural health monitoring, only the deflection of key sections of the bridge can be monitored; the spatial continuous deflection of the main girder cannot be identified. To solve this problem, a method for expanding sparse point deflection measurements to spatially continuous data via optical fiber sensors in long-span suspension bridges is proposed. First, the distributed fiber-optic sensors are arranged longitudinally along the bridge to obtain the strain data of high-density measurement points on the main girder. Second, the influences of ambient temperature and cable system on the main girder strain of the suspension bridge are eliminated by using multiple types of sensors, and a transformation model from strain to deflection of the main girder based on an inverse finite element method is established. Then, by using thin-walled bar torsion analysis and deflection data obtained from point sensors, a method for expanding the deflection data of high-density measurement points on long-span suspension bridges that combines data interpolation and particle swarm optimization is proposed. The proposed method can extend the deflection monitoring data at key sections to the spatial continuous position of the main girder, thus effectively identifying the deflection of high-density measurement points on the main girder. Finally, a numerical simulation and monitoring data of a real bridge are used to evaluate the effectiveness of the proposed method, and the results show that the deflection identification results of the proposed method are more accurate than the conjugate beam method and the inverse finite element method without considering the main girder torsion.