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Multimodal modeling method for vehicle axle load data of bridge weight-in-motion system

A dynamic weighing system and dynamic weighing technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of the increase of the likelihood function value and the inability to guarantee the global optimal point, etc.

Active Publication Date: 2016-08-31
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

However, since the EM algorithm is a hill-climbing algorithm, the value of its likelihood function keeps rising through continuous iteration, which makes the algorithm a local search algorithm, which can only find the local optimal value, but cannot guarantee the global optimal point

Method used

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  • Multimodal modeling method for vehicle axle load data of bridge weight-in-motion system
  • Multimodal modeling method for vehicle axle load data of bridge weight-in-motion system
  • Multimodal modeling method for vehicle axle load data of bridge weight-in-motion system

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Embodiment Construction

[0077] Below in conjunction with accompanying drawing, further illustrate the method of the present invention.

[0078] A multimodal modeling method for vehicle axle load data of a bridge dynamic weighing system according to the present invention, the specific implementation process is as follows:

[0079] A. Process WIM raw data;

[0080] A1. Put WIM raw data into excel or matlab file;

[0081] A2. Based on literature and specifications, part of the original data is omitted according to certain conditions;

[0082] B. Perform statistical analysis on WIM data to understand the basic statistical characteristics of the data;

[0083] B1. Calculate the mean and variance of the total axle load data of various vehicles;

[0084] B2. Divide vehicles into seven types: A, B, C, D, E, F, and G, calculate the mean and variance of the total axle load data of each type, and make a histogram of the total axle load data of each vehicle type ;

[0085] B3. Observe the statistical proper...

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Abstract

A multimodal modeling method for vehicle axle load data of a bridge weight-in-motion system comprises the following steps of (A) processing WIM original data; (B) performing statistical analysis on the WIM data, and obtaining basic statistical characteristics of the data; (C) performing limited distribution model multimodal modeling; (D) using a genetic algorithm to calculate correlation coefficient parameters in probability density functions; and (E) determining an optimal fitting model and parameters.

Description

technical field [0001] The invention relates to the fields of structural health monitoring, statistical mathematical modeling, etc., and specifically performs probability distribution multimodal modeling on vehicle axle load data obtained by a bridge dynamic weighing system based on a genetic algorithm. Background technique [0002] Structural failure of bridges in service is mainly caused by degradation of structural performance and increasing vehicle loads. Therefore, traffic flow and vehicle load are important indicators for safety assessment, maintenance strategy optimization, and life cycle cost analysis of bridge structures. In the past, vehicle load models were determined primarily by calculations by experienced bridge engineers based on very limited survey data. In order to obtain more effective real-time traffic information, weighing-in-motion (WIM) systems are widely used in urban road and bridge structural health monitoring (SHM) systems. Through the WIM monitor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/23G06F30/367
Inventor 叶肖伟苏有华奚培森
Owner ZHEJIANG UNIV
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