Slope displacement prediction method based on hybrid intelligent algorithm

A prediction method and intelligent algorithm technology, applied in computing, computing models, computer components, etc., can solve the problems of inaccurate evaluation results, sample redundant dimensions, sample data processing, etc., and achieve the effect of solving processing and computing difficulties.

Pending Publication Date: 2021-11-30
STATE GRID FUJIAN ELECTRIC POWER RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Without quantitative analysis of slope displacement, it is impossible to accurately predict slope displacement
At the same time, the sample data is not processed, resulting in redundant and high-dimensional samples, which makes the evaluation results inaccurate and requires a large amount of calculation.

Method used

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  • Slope displacement prediction method based on hybrid intelligent algorithm
  • Slope displacement prediction method based on hybrid intelligent algorithm
  • Slope displacement prediction method based on hybrid intelligent algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] see figure 1 and 2 , a slope displacement prediction method based on a hybrid intelligent algorithm, including the following steps:

[0058] Install a number of different monitoring equipment at specific positions of the target slope, collect monitoring information of the slope, and establish an initial database according to the monitoring information; the monitoring information is data from the same slope in different periods, and periodically collect the same side slope Slope information, different types of information at the same time form a piece of data;

[0059] Eliminate redundant information in the initial database to obtain a reduced set database; redundant information is data with a low degree of correlation with the target result or with high repeatability with other information;

[0060] Reducing the dimensionality of data in the reduced set database to obtain a comprehensive index database; large-scale high-dimensional data has extremely high requirements...

Embodiment 2

[0067] A method for predicting slope displacement based on a hybrid intelligent algorithm. On the basis of Embodiment 1, each of the monitoring devices takes time T 1 Collect monitoring information periodically; the monitoring information includes:

[0068] A. time T 1 surface displacement of the inner slope;

[0069] B. time T 1 Horizontal displacement, average positive earth pressure, average lateral earth pressure, and average pore water pressure at different depths at the toe of the inner slope;

[0070] C. time T 1 Accumulated rainfall on the inner slope, number of rainy days, number of consecutive rainy days, and maximum daily rainfall.

[0071] In a specific implementation manner of this embodiment, B of the monitoring information is specifically: time T 1 Horizontal displacement at depths of 1m, 2m, 3m, 5m and 8m at the toe of the inner slope; time T 1 Average positive earth pressure at depths 2m and 5m at the toe of the inner slope; time T 1 Average lateral ear...

Embodiment 3

[0074] A slope displacement prediction method based on a hybrid intelligent algorithm, on the basis of Embodiment 2, the redundant information in the initial database is removed to obtain a reduced set database, including the following steps:

[0075] Step S1, discretize the monitoring information in the initial database by K-means clustering algorithm;

[0076] Step S2, constructing a decision table P according to the monitoring information, the condition attribute set C of the decision table P is a set of B and C of the monitoring information, and the decision attribute D is A of the monitoring information;

[0077] Step S3, calculate the conditional attribute set C positive field POS of the decision attribute D c (D);

[0078] Step S4, set the conditional attribute set R=C, remove the conditional attribute a from the conditional attribute set R, and obtain the conditional attribute set R-{a}, where a is any conditional attribute in the conditional attribute set C;

[0079...

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Abstract

The invention relates to a slope displacement prediction method based on a hybrid intelligent algorithm. The method comprises the following steps: collecting the monitoring information of a slope, and building a database according to the monitoring information; eliminating redundant information in the database; reducing the dimension of data in the database; making a training sample and a test sample according to the data in the database; establishing a model, and training the model through the training sample to obtain a first prediction model; correcting the first prediction model through the test sample to obtain a second prediction model; predicting the displacement of the target slope through a second prediction model and the real-time monitoring information of the target slope to obtain a first prediction value; and correcting the first predicted value through a Markov method to obtain a final predicted value. Redundant information in the collected information is eliminated, and dimensionality reduction is carried out on the redundant information, so that the prediction result is more accurate, and the calculated amount is lower. And the slope displacement is predicated through a least square support vector machine and a particle swarm optimization algorithm.

Description

technical field [0001] The invention relates to a slope displacement prediction method based on a hybrid intelligent algorithm, and belongs to the technical field of slope displacement prediction. Background technique [0002] The southeastern coastal areas of my country are dominated by hilly landforms, and a large number of slope projects often appear during the construction of power transmission and transformation facilities. Especially in economically developed areas, the site conditions of substations and transmission lines are mostly non-topographically optimal, not only The location of the site is poor, and the site conditions are also relatively poor. Water and soil erosion and slope stability during construction and operation have always been one of the key and difficult issues. In particular, the heavy rainfall brought by strong typhoons has caused serious soil erosion and slope instability disasters. Therefore, the monitoring and early warning of slope instability...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/2135G06F18/2411G06F18/295
Inventor 李熙江世雄陈垚陈泽钦王重卿杨海云林亚涛车艳红吴凡翁孙贤程慧青苏杭张弛施华
Owner STATE GRID FUJIAN ELECTRIC POWER RES INST
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