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Accurate prediction method and system for horizontal displacement of concrete faced rockfill dam

A concrete panel and horizontal displacement technology, applied in neural learning methods, biological neural network models, geometric CAD, etc., can solve problems such as overfitting, insufficient nonlinear ability of mathematical statistical models, and falling into local minimum values, etc., and achieve high precision High and powerful self-learning ability, simple operation effect

Pending Publication Date: 2020-03-24
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the deficiencies of the prior art, and propose a precise method and system for predicting the horizontal displacement of a concrete face rockfill dam, which integrates the mathematical statistical method and the neural network method, and not only solves the lack of nonlinear ability of the traditional mathematical statistical model It also overcomes the defects that the neural network method is prone to fall into local minimum and overfitting, and can accurately predict the horizontal displacement of the concrete face rockfill dam, ensuring the safe operation of the dam and its auxiliary structures

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  • Accurate prediction method and system for horizontal displacement of concrete faced rockfill dam
  • Accurate prediction method and system for horizontal displacement of concrete faced rockfill dam
  • Accurate prediction method and system for horizontal displacement of concrete faced rockfill dam

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Embodiment 1

[0031] In one or more embodiments, a method for accurately predicting the horizontal displacement of a concrete face rockfill dam based on statistical optimization neural network technology is disclosed.

[0032] To illustrate this example more clearly, refer to figure 1 , the implementation process of this method can be specifically described as follows:

[0033] (1) Acquisition of measured data. Select a concrete face rockfill dam as the application object, through long-term on-site monitoring of the target dam (if the dam has accumulated a large amount of measured data, it can be called directly), continuously monitor and accumulate the horizontal displacement of the dam body , dam body temperature, reservoir water level and other elements, it is required that the time series of the measured data of the above monitoring elements last for more than one year. If the dam has accumulated a large amount of measured data (time series over one year) including dam body horizontal...

Embodiment 2

[0074] In one or more embodiments, a precise system for predicting the horizontal displacement of a concrete face rockfill dam is disclosed, including:

[0075] A module for obtaining historical measured data of the concrete face rockfill dam to be tested and preprocessing the obtained data;

[0076] It is used to classify the environmental factors that affect the horizontal displacement of concrete face rockfill dams, and select the input variable module of the multiple linear regression model according to the correlation between each influencing factor and the horizontal displacement;

[0077] A module for establishing a multiple linear regression model for predicting the horizontal displacement of concrete face rockfill dams considering the delayed response of rockfill materials and the deformation characteristics of cyclic loading and unloading, and obtaining the preliminary predicted value of the horizontal displacement;

[0078] It is used to establish a statistically op...

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Abstract

The invention discloses an accurate prediction method and system for horizontal displacement of a concrete faced rockfill dam. The accurate prediction method comprises: obtaining and preprocessing historical measured data of the concrete faced rockfill dam to be measured; classifying environmental factors influencing the horizontal displacement of the concrete faced rockfill dam, and selecting aninput variable of a multiple linear regression model according to the correlation between each influence factor and the horizontal displacement; establishing a concrete faced rockfill dam horizontal displacement prediction multiple linear regression model considering rockfill material delay response and cyclic loading and unloading deformation characteristics, and obtaining a preliminary prediction value of horizontal displacement; and establishing a statistical optimization neural network model and carrying out optimization training, and taking a dependent variable of the multiple linear regression model and the preliminary prediction value as inputs of the statistical optimization neural network model to obtain a horizontal displacement prediction value of the concrete faced rockfill dam. The horizontal displacement of the concrete faced rockfill dam can be accurately predicted, and safe operation of the dam and accessory structures of the dam is guaranteed.

Description

technical field [0001] The invention relates to the technical field of earth-rockfill dam safety monitoring, in particular to a method and system for accurately predicting the horizontal displacement of a concrete face rockfill dam based on statistical optimization neural network technology. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] As a very important hydraulic structure, concrete face rockfill dam has been widely used in hydraulic engineering fields all over the world. The safe operation of concrete face rockfill dam is related to the safety of people's life and property. Therefore, for the steady development of the national economy and the safety of people's lives and properties, the health status of concrete face rockfill dams should be monitored. As the most widely used monitoring variable in dam monitoring at home and abroad,...

Claims

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Application Information

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IPC IPC(8): G06F30/13G06F30/27G06N3/08G06F111/10
CPCG06N3/08
Inventor 韩勃耿飞代松黄国香甘高源刘士亮
Owner SHANDONG UNIV