The invention relates to the technical field of
machine learning, in particular to a rock and soil stability prediction method and
system based on
machine learning, and the method comprises the following steps: extracting
shear stress, displacement and energy dissipation rate
time series data, calculating the
stress change rate of adjacent time steps, screening the stress increment sudden change period, and calculating the shear deformation gradient change rate.
Mutation points in the strain direction are screened, and a key
variable interval is obtained. According to the method, traditional analysis limitation is broken through by combining displacement-stress second derivative and energy dissipation rate
cross validation, a hardening-
softening conversion area is dynamically identified, multi-stage
strain gradient differences are compared to establish a space-
time correlation parameter
system, and static and dynamic parameter conversion is realized by using a strain increment space gradient tracking technology; and quantitative mechanical interaction and energy dissipation collaborative analysis is carried out to describe a
stress diffusion path, stress release characteristics and deformation gradient distribution are fused to construct a fracture
principal direction multi-dimensional
verification mechanism, and a microscopic fracture and macroscopic
instability correlation model is established.