A Classification Method of Building Earthquake Damage Level Based on Deep Learning

Abstract

The present invention proposes a method for classifying earthquake damage levels of buildings based on deep learning, which relates to the field of earthquake engineering. First, based on the shooting of individual buildings in the area, the earthquake damage and damage images are obtained, and then the nerves are segmented through images based on deep learning. The network quickly and accurately segments the buildings in the captured image, that is, extracts the earthquake-affected buildings from the complex image background; and then uses the image classification neural network based on deep learning to classify the earthquake damage of the segmented buildings. The invention can quickly and efficiently classify the damage of buildings in the earthquake-stricken area accurately. The image classification method based on deep learning can use the computing power and fast decision-making ability of the deep neural network to replace manual evaluation on site and greatly reduce the time. And energy consumption, under the condition of ensuring the accuracy rate, it can efficiently judge whether the buildings in the large-scale earthquake-stricken area collapse.

Description

Technical field [0001] The present invention belongs to the field of seismic engineering technology, in particular relates to a deep learning-based seismic failure level classification method for buildings. Background [0002] In the earthquake damage assessment of the building area, because it is necessary to quickly assess the damage to the building in the earthquake damage area after the earthquake, and to obtain the level and quantity of damage after the building after the earthquake in time, it is convenient for auxiliary decision-making and emergency rescue after the earthquake. How to accurately classify the damage of buildings in the earthquake damage area while meeting the requirements of timeliness, that is, to investigate the distribution and number of disasters in a timely manner after the earthquake and carry out rapid assessment of earthquake damage is an effective way to reduce the loss of earthquake disasters. Based on this status quo, an effective classification ...

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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