To ensure the safety of pit, proposed the intelligent forecasting and early warning method.
Patent on one such early warning method, GRNN, is discussed. A brief review of the status about
models of soil and excavation stability studies, pointed out that it is necessary that pit slope stability is
conducted by the intelligent algorithm. With specific examples of projects, used HS model of PLAXIS
geotechnical engineering software to analyze finite element including seepage calculation and get the
training data and test data required by generalized regression neural network. With the date made inversion calculation of
the soil parameters. Then the strength reduction was combined with the warning grading thought to build intelligent early
warning system to predict excavation stability. The study pointed out that the pit overall stability intelligent forecasting
and early warning method can effectively control error and avoid ambiguity forecast.
Keywords: Pit, intelligent, generalized regression neural network, HS model, overall stability, warning grading, forecasting
and early warning.
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