Building wind field ground roughness identification method based on multi-scale input neural network

Abstract

The invention discloses a building wind field ground roughness identification method based on a multi-scale input neural network, and the method comprises the following steps: S1, dividing at least two scales according to the size of the surrounding range of a building, and collecting a satellite image; s2, using an ESDU method to calibrate the ground roughness of the image; s3, constructing a multi-scale input convolutional neural network model, and respectively training the input sector diagram of each scale by adopting mutually independent convolutional neural networks until a SoftMax layer outputs a result; s4, fusing training output results of the fan-shaped diagrams with various scales on a SoftMax layer, and calculating a Loss function; s5, comparing Loss function value convergence errors to obtain a classification model; and S6, inputting the to-be-detected image into the classification model for identification and classification to obtain a classification result. The method is used for improving the accuracy and efficiency of building wind field test simulation.

Description

technical field [0001] The invention relates to the technical fields of wind field analysis and image recognition of building structures, in particular to a ground roughness recognition method for wind field of buildings based on a multi-scale input neural network. Background technique [0002] The simulation of the wind field in the wind tunnel experiment of the building structure needs to determine the ground roughness category in advance, and then determine the corresponding wind profile according to the basic wind pressure and other parameters. Conventional methods to determine the category of ground roughness include standard qualitative discrimination method, ESDU digital wind model method and terrain simulation test method. The factors are large, and for the situation where there are many surrounding environmental elements, the reliability of the judgment result of this method is low; the terrain simulation test method involves model making, measuring point layout, et...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a ground roughness recognition method of building wind field based on multi-scale input neural network, to overcome the shortcomings of low recognition accuracy and poor execution efficiency of conventional ground roughness judgment methods, and to improve the accuracy of building wind field test simulation and efficiency

Images