Aiming at the problem that the drilling core cannot be drilled and core is unable to be drilled to find the stratum due to the construction measures or large equipment occupation in the survey site, based on the thickness information of the adjacent hole position of the borehole to be measured, the BP neural network model for stratum prediction was constructed, and the training data sampling method and comprehensive analysis results were put forward, which were based on the training data sampling method and comprehensive analysis results in the front, back, left, right and circle positions of the borehole, combined with the actual investigation Engineering. The influence of soil layer training data collected by different methods on the accuracy of soil layer thickness prediction was analyzed. The results showed that: (1) By comparing the prediction results of 10 groups of actual engineering holes to be measured, it was revealed that the prediction accuracy of soil thickness had great correlation with the orientation selection. (2) In azimuth prediction, each group collects 21 adjacent boreholes as training data samples by continuous cycle of three longitudinal and seven horizontal directions. The prediction results of neural network were stable. (3) A selection strategy was proposed to analyze the preliminary prediction results of neural network. Based on the circular prediction results, the data with the difference within ±0.2m in azimuth prediction were compared and screened. The comprehensive prediction value obtained by test was very close to the true value, and the data error is within 9%, and the prediction effect was good. The research results provide a way of thinking for the prediction of small sample soil sequence, and can provide reference for geotechnical engineering investigation and design application.
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