Study On Intelligent Location And Identification Method Of Multi-type Seismic Damage Of Building Structures

With the significant improvement of urbanization and the continuous increase of urban scale and volume,its ability to deal with natural disaster risk challenges and emergency response is becoming increasingly important.As a crucial link of earthquake disaster assessment,earthquake damage identification of urban building complex structure has great reference value for government emergency rescue decision-making and post-earthquake building reinforcement and maintenance.Due to its special morphological characteristics,complex regionality and strong background noise interference,the traditional manual detection has some shortcomings,such as lack of timeliness and strong subjective dependence of professionals.However,traditional image processing methods such as threshold segmentation and edge detection have some challenges,such as low precision,slow speed and large amount of calculation.In contrast,computer vision perception and recognition technology provides a new way for structural damage detection,location and evaluation because of its fast and high precision.In this paper,the damage detection and location method of building structural components based on YOLOv4 deep learning model was proposed,which realized the high-accuracy identification of multiple damage targets of structural components,and then developed the u-net semantic segmentation model for multi type component classification,which verified the effectiveness of the fusion evaluation of component damage and corresponding components.The contents of this study are as follows:（1）The damage data set of typical seismic structural members at home and abroad was established.Using the internal damage images of buildings after the Ecuador earthquake in 2016 and the Beichuan earthquake in 2008 as the data source,five types of damage labels such as cracking,spalling,failure,reinforcement exposure and reinforcement buckling were established.Due to the special geometric and morphological characteristics of cracks,the single classification recognition training for crack damage was carried out first.Then,multi classification target detection training was carried out for four types of regional damage: spalling,failure,reinforcement exposure and reinforcement buckling.（2）The damage identification and location method of complex cross cracks was developed,and the data set was enriched and improved by means of data enhancement.Based on the original YOLOv4 neural network,the hyperparameters optimization was carried out.The Focal Loss function was introduced to modify the classification loss in the original three types of losses: classification loss,positioning loss and confidence loss.The hyperparameters α and γ was introduced into classification loss for solving the problems of low recognition rate,false detection,missing detection and so on.In addition,it can improve the proportion weight of difficult to recognize samples in the loss function,and optimize the weight proportion of background and recognition target area in the loss function.Based on the improvement of the original network structure,the scoring mechanism of the evaluation indicators of crack identification was optimized to achieve a good and practical crack damage location and identification effect.（3）Aiming at the location and identification of multi-categories regional damage,the weighted data enhancement method was used to solve the problems of data imbalance and small proportion of individual damage categories.By introducing the modules of channel attention mechanism and spatial attention mechanism,the effect of damage area recognition was improved.In addition,the upsampling module was optimized,the mish activation function was used in the upsampling module to improve the multi classification damage identification and evaluation indicators.（4）U-Net semantic segmentation Neural Network was used to classify and identify interior building components.The data set adopts the building interior damage images of the Beichuan earthquake in 2008 and the Luxian earthquake in 2021.The building components were divided into four categories: wall,column,beam and slab.Through the improvement of optimization algorithm strategy,the experiment of learning rate reduction method and hyperparameters optimization,the effect of component semantic segmentation and recognition was improved.The weighted combination of damage area,damage category and component location information can obtain the damage degree information of building components,and finally realize a high-precision and high-speed damage degree evaluation method of building components.