Vision-Based Structural FE Model Updating Using Genetic Algorithm

Abstract

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

Structural condition assessment with structural health monitoring systems and nonlinear simplified models

“…On the other hand, some methods such as genetic algorithm and simulated annealing update parameters to obtain global optimum of determined objective function that controls difference between real and simulated dynamic models. 25,[30][31][32] In this study, two different optimization techniques were utilized in order to determine more accurate nonlinear curves of shear and flexural springs. One of them is Kalman Filter which is used in many studies.…”

“…Therefore, accuracy of these methods is dependent on quality of prior knowledge that directly affect prediction error between real and simulated dynamic models. On the other hand, some methods such as genetic algorithm and simulated annealing update parameters to obtain global optimum of determined objective function that controls difference between real and simulated dynamic models 25,30–32 . In this study, two different optimization techniques were utilized in order to determine more accurate nonlinear curves of shear and flexural springs.…”

Semantic Structure from Motion for Railroad Bridges Using Deep Learning

“…Lee et al [4] automatically extracted bridge design parameters based on point cloud data (PCD). Park et al [5] predicted the dynamic characteristics of structures using image data.…”