Aqueduct pier advanced settlement forecasting method based on extreme learning machine

An extreme learning machine and settlement technology, which is applied in the field of water conservancy and civil engineering structural health monitoring, and can solve problems such as early warning of aqueduct health conditions.

Inactive Publication Date: 2020-03-31
HOHAI UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to provide a method for forecasting the advanced settlement of aqueduct pier based on extreme learning machine, so as to solve the defect in the prior art that the early warning cannot be carried out according to the health status of the aqueduct

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  • Aqueduct pier advanced settlement forecasting method based on extreme learning machine
  • Aqueduct pier advanced settlement forecasting method based on extreme learning machine
  • Aqueduct pier advanced settlement forecasting method based on extreme learning machine

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Embodiment Construction

[0024] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0025] like Figure 1 to Figure 5 Shown, a kind of prediction method of the advance settlement of aqueduct pier based on extreme learning machine, described method comprises the following steps:

[0026] Input data from aqueduct monitoring into pre-built predictive models;

[0027] Calculate the trend of the settlement amount through the calculation of the prediction model;

[0028] Analyze the variation trend of the settlement and make a forecast based on the analysis results.

[0029] In this embodiment, the data obtained by monitoring the aqueduct includes: settlement data around the aqueduct and settlement data of the aqueduct itself. In this embodiment, the method for obtaining the settlement data around the aqueduct and the settlement dat...

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Abstract

The invention discloses an aqueduct pier advanced settlement forecasting method based on an extreme learning machine. The aqueduct pier advanced settlement prediction method comprises: inputting dataobtained by aqueduct monitoring into a pre-constructed prediction model; performing operation through the prediction model to obtain a settlement amount change trend; analyzing the variation trend ofthe settling volume; performing forecasting according to the analysis results. According to the aqueduct pier advanced settlement prediction method based on an extreme learning machine, through historical data related to aqueduct piers, an extreme learning machine is combined to build and train a prediction model, the settlement amount of the aqueduct piers in a plurality of days in the future ispredicted, early warning of aqueduct pier settlement is achieved, and rich time is reserved for formulation of aqueduct emergency treatment measures.

Description

technical field [0001] The invention relates to the field of health monitoring of water conservancy and civil engineering structures, in particular to a method for forecasting the advanced settlement of aqueduct piers based on an extreme learning machine. Background technique: [0002] After long-term operation of the aqueduct project, there are aging, damage and other diseases, resulting in different degrees of structural damage or performance degradation, resulting in the structure of the aqueduct not meeting the requirements of normal use, and even affecting its structural safety, which has brought great harm to the safe operation and management of the aqueduct project. influences. The load of the aqueduct during operation may include water pressure, self-weight, temperature load, etc. The working state of the aqueduct pier plays an important role in the stability and safety of the overall structure. It mainly bears the water load and self-weight of the superstructure, an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N20/00
CPCG06N20/00G06Q10/04G06Q10/06393
Inventor 江守燕杜成斌赵林鑫万晨徐浩
Owner HOHAI UNIV
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