Slope reliability analysis method based on active learning multivariate adaptive regression spline
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An adaptive regression and active learning technology, applied in instrumentation, geometric CAD, design optimization/simulation, etc., can solve problems such as heavy workload, and achieve the effect of concise and easy-to-understand goals and advantages
Pending Publication Date: 2022-07-29
NANCHANG INST OF TECH
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Especially for the case of small failure probability, it often requires hundreds of thousands of numerical simulation calculations so that the workload is too large to meet the requirements of practical applications
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Embodiment 1
[0061] see figure 1 , which shows the slope reliability analysis method based on active learning multivariate adaptive regression spline in the first embodiment of the present invention, and the method specifically includes steps S101 to S108:
[0062] S101, acquiring slope parameters of the target object, where the slope parameters at least include slope dimensions and soil characteristic parameters;
[0063] S102, using preset simulation calculation software, the size of the side slope and the characteristic parameters of the soil body to construct a corresponding side slope analysis model, and preprocessing the side slope analysis model to obtain a stability file;
[0064] S103, determining a random field statistical index, and using the LHS method and the random field statistical index to obtain a plurality of independent standard normal distribution samples and an independent standard normal distribution sample pool;
[0065] According to each of the independent standard...
Embodiment 2
[0142] In this embodiment, for a single layer The stability and reliability of the slope is analyzed.
[0143] see Figure 5 , shows the slope model in this embodiment, the slope height is 10m, the slope toe is 45°, and the slope is an average slope. The soil parameters of the probabilistic stability analysis are shown in Table 3. This example only focuses on the spatial variability of soil cohesion and internal friction angle. The cross-correlation coefficient is assumed to be -0.5, and other parameters are regarded as constants. According to the known parameters, FLAC3D is used to initially establish the model, and the safety factor is calculated by using the strength reduction method to be 1.228, which is very close to 1.204 and 1.205 in the prior art, indicating the correctness of the model.
[0144] Table 3 Statistical characteristics of soil parameters
[0145]
[0146] from Figure 5 It can be seen that the meshing of the established model contains 1190 quadrila...
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Abstract
The invention provides a slope reliability analysis method. The method comprises the following steps: acquiring slope parameters of a target object; building a slope analysis model by using simulation calculation software and the slope parameters, and preprocessing the slope analysis model to obtain a stability file; determining a random field statistical index, and obtaining an independent standard normal distribution sample and an independent standard normal distribution sample pool by using an LHS method and the random field statistical index; according to each independent standard normal distribution sample, each independent standard normal distribution sample pool and the stability file, performing discretization by using a KL series expansion method to obtain a sample matrix; calculating each sample matrix by using simulation calculation software to obtain a corresponding slope safety coefficient; constructing a multivariate adaptive regression model by using the slope safety coefficient, and verifying the accuracy of the multivariate adaptive regression model; and if the accuracy of the multivariate adaptive regression model meets the preset accuracy requirement, calculating the multivariate adaptive regression model by adopting an MCS method to obtain the slope failure probability.
Description
technical field [0001] The present application relates to the technical field of slope engineering, in particular to a slope reliability analysis method based on active learning multivariate adaptive regression splines. Background technique [0002] Due to the complex deposition process of natural soils, the properties of soils often exhibit spatial variability in space. Even with various exploration and testing methods, there are still uncertainties in the obtained soil properties. The main reason is that soil formation is a complex process, which is affected by geological, environmental and physicochemical processes. Therefore, the traditional method of obtaining the safety factor is often calculated with a set of certain soil characteristic parameters, which cannot accurately analyze the slope stability. Therefore, in recent years, scholars have done a lot of research on the spatial variability of slopes. [0003] In order to carry out the slope reliability analysis, ma...
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