Rock-soil mass parameter two-dimensional space variability characterization method based on multi-surface wave exploration

Abstract

The invention relates to a rock-soil mass parameter two-dimensional space variability characterization method based on multi-surface wave exploration, and belongs to the field of geotechnical engineering investigation. The method includes establishing a two-dimensional space linear dynamic system for representing the spatial variability of the rock and earth mass parameters; constructing a Bayesian equation for identifying parameters of a two-dimensional space linear dynamic system according to a Bayesian theory under the condition that two-dimensional shear wave velocity data of multi-surface wave exploration and an empirical relationship between exploration data in literatures and rock-soil body parameters are given; solving optimal model parameters by adopting a Bayesian updating method based on subset simulation; and obtaining the most possible two-dimensional spatial distribution of the rock-soil body parameters by adopting a forward recursive algorithm and a backward recursive algorithm. According to the invention, the influence of the two-dimensional spatial correlation of the shear wave velocity data on the spatial variability characterization recognition of the rock-soil body parameters is reasonably considered, the two-dimensional spatial exploration data are fully utilized to characterize the unstable spatial variability of the rock-soil body parameters, the limitation of a traditional steady-state random field model is overcome, and an effective way is provided for improving the exploration accuracy of a geotechnical engineering site.

Description

technical field [0001] The invention relates to the field of geotechnical engineering investigation, in particular to a method for characterizing the two-dimensional spatial variability of rock and soil parameters based on multi-channel surface wave exploration. Background technique [0002] During its formation, the rock and soil body has experienced various complex geological actions, so it has a complex structure. Therefore, in the design of geotechnical engineering, engineers not only have to face complex stratum structures, but also take into account the spatial variability of geotechnical types and physical and mechanical properties. The accuracy and reliability of geotechnical parameters (such as soil bulk density and effective friction angle) will directly affect the results of geotechnical engineering design. However, there are uncertainties in the geotechnical parameters and stratigraphic profiles described by engineers based on limited survey data. In order to r...

AI Technical Summary

Problems solved by technology

[0003] At present, the widely used characterization model for the spatial variability of rock and soil parameters is the random field model. Directly identify the two-dimensional stationary random field model to characterize the spatial variability of rock and soil parameters based on the surveyed two-dimensional profile data

In addition, the in-situ test methods mentioned above are all intrusive tests, which will cause disturbance to the rock and soil mass of the site

Engineers cannot test every position of the site, and for the spatial positions without any survey information in the geotechnical engineering site, they can only infer and predict based on limited survey information

Although there are many interpolation methods and fitting methods, it is still difficult to predict possible unknown soil layers, such as the presence of different rock and soil types between two adjacent boreholes or survey points or local weak interlayer lenses Wait

Therefore, it is impossible to obtain a comprehensive spatial variability of the rock and soil mass on the site based only on the in-situ test and survey information.

The non-intrusive multi-channel surface wave exploration method can provide two-dimensional spatial data, but the traditional steady-state random field model is difficult to capture the two-dimensional spatial variation trend of rock and soil parameters

Images