Intelligent identification method for three-dimensional crack nucleation of rock

Abstract

This application relates to an intelligent identification method for three-dimensional crack nucleation in rocks, comprising: step S1 acquiring acoustic emission signals of rocks damaged under compressive loading conditions; step S2 performing three-dimensional localization and coordinate normalization; step S3 constructing a point set by rolling according to an intelligent time window or strain window; step S4 selecting a candidate clustering scale set Dk; step S5 outputting the optimal DBSCAN parameters; step S6 obtaining density clustering to identify candidate nucleation clusters; step S7 determining whether each cluster Ck is a crack nucleation cluster and evaluating the confidence level of the nucleation cluster; step S8 obtaining a three-dimensional nucleation body mesh Bk,j; step S9 outputting each nucleation body and quantifying its characterization; step S10 matching; and step S11 providing an early warning or control interface for use by a monitoring system. This method combines real-time monitoring of acoustic emission and microseismic events, and employs adaptive intelligent parameter optimization, density clustering analysis, and three-dimensional reconstruction techniques to automatically identify rock crack nucleation regions.

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