Building seismic damage grade classification method based on deep learning

Abstract

The invention provides a building seismic damage grade classification method based on deep learning, and relates to the field of seismic engineering, and the method comprises the steps: obtaining a seismic damage and damage image based on the photographing of a single building in a region, and carrying out the quick and accurate segmentation of a building in the photographed image through an imagesegmentation neural network based on deep learning, extracting a seismic building from a complex image background; and then carrying out seismic damage classification on the segmented building through an image classification neural network based on deep learning. According to the method, accurate damage classification can be quickly and efficiently carried out on buildings in the seismic area, the image classification method based on deep learning can replace on-site manual evaluation by utilizing the calculation capability and the quick decision capability of the deep neural network, the time and energy consumption is greatly reduced, under the condition that the accuracy is guaranteed, whether buildings in a large-range seismic area collapse or not is efficiently judged.

Description

technical field [0001] The invention belongs to the technical field of earthquake engineering, in particular to a method for classifying earthquake damage levels of buildings based on deep learning. Background technique [0002] In the earthquake damage assessment of the building area, it is necessary to quickly assess the damage of the building in the earthquake-damaged area after the earthquake, so as to obtain the damage level and quantity of the building after the earthquake in time, which is convenient for auxiliary decision-making and emergency rescue after the earthquake. . How to accurately classify the damage levels of buildings in the earthquake-damaged area and at the same time meet the timeliness requirements, that is, to investigate the distribution and quantity of disasters in a timely manner after the earthquake and implement a rapid assessment of earthquake damage is an effective way to reduce the loss of earthquake disasters . Based on this situation, it i...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of accurately classifying the damage level of buildings in the earthquake damage area in the earthquake damage assessment of the building area, and at the same time meet the timeliness requirements of the assessment. A deep learning-based Classification Method for Earthquake Damage Levels of Buildings

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