An ambient vibration test on a concrete bridge constructed in 1971 and calibration of its finite element model are presented.The bridge is characterized by a system of post-tensioned and simply supported beams.The dyn...An ambient vibration test on a concrete bridge constructed in 1971 and calibration of its finite element model are presented.The bridge is characterized by a system of post-tensioned and simply supported beams.The dynamic characteristics of the bridge,i.e.natural frequencies,mode shapes and damping ratios were computed from the ambient vibration tests by using the Eigensystem Realization Algorithm(ERA).Then,these characteristics were used to update the finite element model of the bridge by formulating an optimization problem and then using Genetic Algorithms(GA)to solve it.From the results of the ambient vibration test of this type of bridge,it is concluded that two-dimensional mode shapes exist:in the longitudinal and transverse;and these experimentally obtained dynamic characteristics were also achieved in the analytical model through updating.The application of GAs as optimization techniques showed great versatility to optimize any number and type of variables in the model.展开更多
文摘An ambient vibration test on a concrete bridge constructed in 1971 and calibration of its finite element model are presented.The bridge is characterized by a system of post-tensioned and simply supported beams.The dynamic characteristics of the bridge,i.e.natural frequencies,mode shapes and damping ratios were computed from the ambient vibration tests by using the Eigensystem Realization Algorithm(ERA).Then,these characteristics were used to update the finite element model of the bridge by formulating an optimization problem and then using Genetic Algorithms(GA)to solve it.From the results of the ambient vibration test of this type of bridge,it is concluded that two-dimensional mode shapes exist:in the longitudinal and transverse;and these experimentally obtained dynamic characteristics were also achieved in the analytical model through updating.The application of GAs as optimization techniques showed great versatility to optimize any number and type of variables in the model.
