Abstract
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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