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Method for constructing bridge structure probability reference finite element model based on time-dependent temperature response

A bridge structure, temperature response technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to describe the parameter probability distribution, and achieve the effect of reducing the number of iterations, improving computational efficiency, and improving computational accuracy

Inactive Publication Date: 2017-10-24
HARBIN INST OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the traditional reference finite element model cannot describe the probability distribution of bridge structure modal parameters under time-varying temperature response, and propose a method for constructing a bridge structure probability reference finite element model based on time-varying temperature response

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  • Method for constructing bridge structure probability reference finite element model based on time-dependent temperature response
  • Method for constructing bridge structure probability reference finite element model based on time-dependent temperature response
  • Method for constructing bridge structure probability reference finite element model based on time-dependent temperature response

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specific Embodiment approach 1

[0064] Embodiment 1: A method for constructing a probabilistic benchmark finite element model of a bridge structure based on a time-varying temperature response includes the following steps:

[0065] Step 1: Establish a characteristic sample set of bridge structural modal parameters and ambient temperature, and perform clustering and grouping according to the Gaussian distribution;

[0066] Step 2: For the different cluster groups obtained in Step 1, respectively establish the finite element model and the Kriging model of the bridge structure;

[0067] Step 3: Use the kriging model and genetic algorithm established in step 2 to correct the average value of the correction parameters of the bridge structure finite element model established in step 2, and establish a benchmark finite element model of the bridge structure;

[0068] Step 4: According to the bridge structure reference finite element model established in step 3, determine the initial value of the correction parameter...

specific Embodiment approach 2

[0072] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the step 1, a characteristic sample set of bridge structure modal parameters and ambient temperature is established, and the specific process of clustering and grouping according to the Gaussian distribution form is as follows:

[0073] Step 11, using continuous acquisition technology to collect acceleration data and ambient temperature data of the bridge during a period of monitoring;

[0074] Step 12. Use the Eigenvalue Recognition Algorithm (ERA) to identify the modal parameters of the acceleration data collected per hour. The modal parameters are the first P-order natural frequency information f M , and the corresponding hourly temperature information T;

[0075] Step 13: Construct feature sample set Θ=[T,f M ], feature sample set (matrix) is made of samples of P+1 dimension continuous random distribution and probability density ζ (Θ) unknown; Described P is the total...

specific Embodiment approach 3

[0081] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the specific process of establishing the bridge structure finite element model and Kriging model in the said step two is:

[0082] Step 21, use ANSYS software to set up a finite element model of the bridge structure;

[0083] Step two two, determine the correction parameter variable θ, the correction parameter variable θ includes the elastic modulus of concrete and the elastic modulus and boundary conditions of the larger key structure affected by temperature and steel;

[0084] Step two or three, determine the modal parameter variable f (θ), and the modal parameter variable f (θ) is the frequency information of the previous P order of the bridge structure;

[0085] Step two and four, using the central composite design method, combined with the finite element model established by ANSYS, to obtain the sampling set [θ,f(θ)] of the Kriging model;

[0086] Step 25. The ...

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Abstract

The invention provides a method for constructing a bridge structure probability reference finite element model based on time-dependent temperature response is provided and relates to the method for constructing a bridge structure probability reference finite element model with an aim to overcome the defect that bridge structure modal parameters have no uniqueness since actual bridge structure material parameters and joint connection rigidity tend to change with changes in ambient temperature under the influence of time-dependent temperature response. The method includes: I, building a bridge structure modal parameter and ambient temperature characteristic sample set, and performing clustering in Gaussian distribution form; II, building a bridge structure finite element model and a Kriging model; III, correcting a corrected parameter mean of the bridge structure finite element model, and building the bridge structure reference finite element model; IV, determining initial values of corrected parameters of the probability reference finite element model; V, building the bridge structure probability reference finite element model. The method is applicable to the field of bridge structure damage diagnosis.

Description

technical field [0001] The invention relates to a method for constructing a bridge structure probability benchmark finite element model based on time-varying temperature response. Background technique [0002] The safety of bridge structures is closely related to people's travel safety. How to accurately diagnose the state of bridge structures to ensure people's travel safety is particularly important. With the rapid development of structural health monitoring technology, establishing a structural health monitoring system in bridge structures is regarded as an effective means to ensure the safe operation of bridge structures. Generally, there are two ways to use the structural health monitoring system for damage diagnosis of bridge structures, one is based on data-driven methods, and the other is based on model-driven methods. In a model-driven damage identification method for bridge structures, a benchmark finite element model that accurately describes the state of the bri...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/13G06F30/23G06F2119/06
Inventor 张绍逸刘洋曹建新周正
Owner HARBIN INST OF TECH
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