Soil layer quantization layering method based on double-bridge static sounding data of BP neural network

Abstract

A soil layer quantization layering method based on double-bridge static sounding data of a BP neural network comprises the first step of collecting and sorting the double-bridge static sounding data and soil layering information, the second step of establishing a prediction model of the double-bridge static sounding soil property quantization layering BP neural network, the third step of training the prediction model of the double-bridge static sounding soil property quantization layering BP neural network, the fourth step of predicting the soil property type according to the trained prediction model of the double-bridge static sounding soil property quantization layering BP neural network, and the fifth step of determining the layering precision and carrying out layer combining processing on the prediction result to obtain the soil property quantization layering result finally. The soil layer quantization layering method has the advantages that a reliable theoretical basis is provided for soil property quantization layering, randomness and human factors in the soil type layering in the traditional reconnaissance are avoided, powerful support is provided for the reliability of the prediction model of the soil layer quantization layering BP neural network, the accuracy of the predication result is guaranteed, a large amount of drilling work in the engineering geological investigation in the future is reduced, and the investment cost of investigation is greatly saved.

Description

technical field [0001] The invention relates to the application research field of in-situ testing of geotechnical engineering, in particular to a method for quantifying and stratifying soil layers based on BP neural network-based static penetrating sounding data of double bridges. Background technique [0002] The traditional survey method is mainly based on drilling, during which the soil at a certain depth is sampled and its properties are described. Since the sampling section only accounts for a very small part of the total depth of engineering exploration, most of the rest can only be qualitatively described by visual and tactile identification of disturbed soil samples. Due to the uneven level and experience of the staff, the cognition of soil properties is different and random, resulting in the soil classification of engineering geological sections not being completely based on quantitative measurement, which makes the results of soil stratification reliable. Low, dif...

AI Technical Summary

Problems solved by technology

Due to the uneven level and experience of the staff, the cognition of soil properties is different and random, resulting in the soil classification of engineering geological sections not being completely based on quantitative measurement, which makes the results of soil stratification reliable. Low, difficult to check, easy

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