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Slope system failure probability calculation method based on radial basis function RBF

A technology of failure probability and basis function, applied in complex mathematical operations and other directions, can solve problems such as being unsuitable for complex slope systems and difficult to locate, and achieve the effect of reducing the number of initial sample points and improving computational efficiency.

Active Publication Date: 2020-06-26
CHENGDU UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

LEM is often chosen as the deterministic analysis method to evaluate the FS of slopes. The advantages of LEM are its simplicity and low computational cost, but its main disadvantages are: it is difficult to locate the critical sliding surface when the critical slip surface is not known in advance; moreover, The test sliding surface is usually assumed to be circular, which may not be suitable for complex slope systems, especially when the slope has weak interlayers

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  • Slope system failure probability calculation method based on radial basis function RBF
  • Slope system failure probability calculation method based on radial basis function RBF
  • Slope system failure probability calculation method based on radial basis function RBF

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[0072] In order to make the above objects, features and advantages of the present application more obvious and comprehensible, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0073] refer to figure 1 , shows a flow chart of the steps of a method for calculating the failure probability of a slope system based on radial basis function RBF in the present application, which may specifically include the following steps:

[0074] Step S1: In the standard normal space, use the initial sampling point strategy to generate the training sample set of the slope system;

[0075] Reasonable selection of initial sample points can speed up the convergence of the training process. The initial training sample set can be constructed with Latin Hypercube Sampling (LHS), but this may not be suitable for some models with low probability of failure, since it must contain two classes of points (e.g...

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Abstract

The invention provides a slope system failure probability calculation method based on a radial basis function RBF. An intensity reduction method SRM method is provided to evaluate the stability coefficient; an initial sampling strategy and an active learning function are adopted; an active learning radial basis function ARBF proxy model of an original limit state function LSF is constructed. The Monte Carlo simulation MCS and the ARBF agent model are combined to evaluate the failure probability of the slope system. According to the method, t The influence of random variables and related parameters thereof on the slope stability can be quantified, the number of initial sample points is greatly reduced, the calculation efficiency is effectively improved, the sliding surface of any shape in the soil slope can be automatically recognized, and reliability analysis is more convenient when the layered slope with a complex geometrical shape is subjected to reliability analysis.

Description

technical field [0001] This application relates to the field of stability analysis of soil slopes, in particular to a method for calculating failure probability of slope systems based on radial basis function (RBF). Background technique [0002] Slope stability evaluation is a complicated geotechnical engineering problem, and its input parameters are uncertain. The traditional deterministic analysis method using the stability factor (FS) may not truly reflect the safety of the slope. In order to quantify the influence of uncertainty, probabilistic methods are widely used in slope reliability analysis. [0003] A slope may fail along different sliding surfaces, and the failure of any sliding surface will cause the failure of the slope, thus forming a series of system problems. Accurate and effective reliability analysis for such complex problems is the main problem facing the application of probabilistic methods in geotechnical engineering practice. [0004] The direct sim...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/15G06F17/18
CPCG06F17/15G06F17/18Y02A10/23
Inventor 曾鹏张天龙李天斌孙小平
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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