Slope reliability analysis method based on active learning multivariate adaptive regression spline
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
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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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