Coarse-grained soil filler gradation recognition method and application system based on convolutional neural network

Abstract

The invention relates to a coarse-grained soil filler gradation recognition method and application system based on a convolutional neural network, constructing a first neural network and several second neural networks; collecting images of fillers on site, and using the first neural network to output the classification of particle size ranges Results; input the filler image and classification results into the second neural network corresponding to the particle size range to obtain the quality of coarse-grained soil in each single particle size range; count the mass distribution of coarse-grained soil in various particle size ranges to obtain gradation and grade matching curve. The present invention obtains the particle size range through the first neural network, and obtains the total mass of a single particle size group through the corresponding second neural network, thereby improving the accuracy of mass calculation, thereby ensuring the accuracy of gradation calculation. Multiple second neural networks are processed in parallel, which ensures the training efficiency of the second neural network and the on-site processing efficiency. High degree of automation, no need for complex image processing algorithms, no manual intervention, no dependence on operator experience, strong environmental adaptability, and high precision.

Description

technical field [0001] The invention relates to the field of coarse-grained soil embankment filling and rolling construction and its quality control technology, in particular to a convolutional neural network-based coarse-grained soil filler gradation identification method and an application system. Background technique [0002] Coarse-grained soil filler, as an important part of railway track bed and subgrade, is composed of coarse and fine particles of different sizes, with a wide range of particle sizes and large differences in the characteristics of coarse and fine particles. Generally, it is a single-grain structure, and small particles fill large particles. In the gaps formed, the well-graded coarse-grained soil has a higher density than the poorly-graded coarse-grained soil, and it is easier to be compacted during filling and rolling construction. The gradation of coarse-grained soil greatly affects the service performance of the railway track bed and subgrade such as...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the problems of time-consuming, disturbing site state, and manual intervention in the current grading determination, a coarse-grained soil filler grading recognition method and application system based on deep learning convolutional neural network is proposed, and training is carried out by obtaining a certain number of image samples , after the training of a large sample library, the establishment of the convolutional neural network (CNN) was completed, and it was applied to the construction site of intelligent rolling and filling of railway subgrades to identify subgrade filling in real time, quickly, intelligently, efficiently and accurately. The gradation of coarse-grained soil filler before and after construction and rolling, and the quality of intelligent rolling and filling can be evaluated in real time

Images