Method for Predicting and Evaluating Post Earthquake Damage of Urban Buildings Based on Artificial Intelligence Algorithms

Abstract

This article mainly focuses on the damage assessment of buildings after earthquakes. Firstly, a structural damage model was established based on most reinforced concrete buildings and described using a function. Then, a BP neural network was used to solve the function. Traditional neural networks are prone to falling into local optima. Therefore, in order to improve the performance of neural networks, cross fusion with genetic algorithms is used to avoid falling into local optima, Improve the efficiency of the algorithm. Finally, through experimental verification, the proposed method can quickly evaluate the damage of building structures, with an accuracy rate of 97%.