Optimal Layout Of Life-span Sensors For Long-span Steel-concrete Arch Bridge

Civil engineering structure and system lifetime reliability are the hotspots of global engineering frontier research.Life-time monitoring can ensure the safety of bridges from design to construction to operation period,and can realize their assessment and early warning.At present,there is no real formation of a true life-cycle monitoring system.These stages are independent.How to effectively l ink the monitoring data at different stages of the bridge is the focus of the life-cycle monitoring model.This paper optimizes the layout of the full-life arch rib strain sensor and the bridge surface acceleration sensor,effectively combining the construction phase and the operation phase,so that the sensors in the construction and operation phases can work effectively together.First of all,based on the reinforcement construction project of Liujing Bridge,the model modification theory is proposed.For structural modeling,simulate different arch rib modeling methods,and select appropriate modeling methods by analyzing data comparison.For the built model,based on the actual data measured by the static load experiment,the structural parameters are corrected based on the sensitivity and least squares method,and the model closer to the actual structure is obtained.Secondly,analyze the stress and displacement of the bridge during the construction phase and evaluate it.Early warning of the units and construction stages that may be subject to greater stress or change during the construction process,and propose a plan for construction monitoring based on the analysis results.Finally,the gray evaluation method is used to evaluate the stress during the construction stage of the arch rib.Finally,the sensor deployment technology based on the construction-operation phase is proposed.In the operational phase,consider deploying an acceleration sensor on the bridge deck and adding a strain sensor to the arch rib.For the acceleration sensor,the vertical modal information is selected,and the maximum non-diagonal element of the MAC matrix is selected as the optimization target.For strain sensors,the matching fitness is used to measure the ability of the d eployed sensor to work with the newly added sensor.The ability to complement the sensors in the construction phase and the operational phase is evaluated by matching fitness.Considering that the arch rib is a curved member,the curvature mode is selected as the optimization information.Both use an improved genetic algorithm to optimize the sensor and obtain the final optimization result.