Power or static power-based reinforced concrete simply supported beam fire model correction method

Abstract

The invention relates to a power or static power-based reinforced concrete simply supported beam fire model correction method, and belongs to the technical field of model correction methods. Accordingto the method, damage feature parameters are selected as input parameters for model correction; physical parameters under a corresponding damage state are selected as output parameters for model correction; the damage feature parameters are combined by utilizing frequency and vibration mode changes; and when to-be-corrected physical parameters are selected, structural boundary conditions and material performance parameters, which influence structural mode information and damage recognition, are comprehensively considered. According to the method, a support vector machine statistical learningalgorithm is applied and a distributed correction strategy is adopted, so that sample counting in once correction is decreased, training samples are greatly decreased, the model correction correctnessis improved, and corrected finite models can reflect structural vibration characteristics and other structural responses more really in fire processes or after fires.

Description

technical field [0001] The invention relates to a model correction method, in particular to a method for correction of a fire model of a reinforced concrete simply supported beam based on dynamic or static power. Background technique [0002] Structural health monitoring has long been a research hotspot in the field of structural earthquake prevention and disaster reduction, but the study of structural dynamic characteristics at high temperatures is still very rare at home and abroad due to many difficulties such as test conditions and finite element model establishment. There are many studies on model correction of bridges, but few studies on model correction of reinforced concrete simply supported beams. Most of the model correction methods based on dynamic tests are cumbersome and fail to consider the influence of vibration modes that are more sensitive to damage. Traditional methods It is very easy to cause "data explosion" and the reduction of mapping ability, and only ...

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Problems solved by technology

[0002] Structural health monitoring has long been a research hotspot in the field of structural earthquake prevention and disaster reduction, but the study of structural dynamic characteristics at high temperatures is still very rare at home and abroad due to many difficulties such as test conditions and finite element model establishment.

There are many studies on model correction of bridges, but few studies on model correction of reinforced concrete simply supported beams. Most of the model correction methods based on dynamic tests are cumbersome and fail to consider the influence of vibration modes that are more sensitive to damage. Traditional methods It is very easy to cause "data explosion" and the reduction of mapping ability, and only the material parameters are used as the parameters to be corrected, and the boundary conditions are not considered enough, so the finite element model cannot fully and correctly reflect the reality of the structure

[0003] For the finite element model correction algorithm, some scholars use the neural network method as the regression mechanism to carry out the finite element model, but theoretically speaking, the neural network uses the empirical risk minimization to select the model, but the empirical risk minimization replaces the expected risk minimization to select the model is wrong

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