| abstract |
The invention relates to a method for quickly identifying the particle size distribution of building spoil based on a convolutional neural network. The method identifies a convolutional neural network model based on a pre-trained building spoil particle size distribution, processes the image of the building spoil to be tested, and obtains an image of the building spoil to be tested. The particle size distribution of the tested construction spoil; wherein, the image of the construction spoil is: the construction spoil to be tested is pretreated and dispersed in a solution to obtain a dilute solution, and then the dilute solution is photographed and image-processed in turn. The resulting binary image. Compared with the prior art, the present invention combines artificial intelligence with traditional geotechnical tests, adopts non-direct contact detection means, realizes long-distance rapid detection and effective recording of the particle size distribution of building spoil, and has the advantages of convenient operation, simple and practical, The results have the advantages of good reproducibility and high test accuracy, which provide a reliable and effective pretreatment method for the utilization of building spoil resources, and have high promotion value and environmental benefits. |