基于遥感影像的大当量爆炸建筑物毁伤评估模型

A remote sensing imagery-based model for assessment of building damage induced by large-equivalent explosions

为了研究大当量爆炸建筑物毁伤评估问题,基于遥感影像解译和大数据分析构建了大当量爆炸建筑物毁伤评估模型。首先,基于大当量爆炸的具体历史案例构建了毁伤数据集,具体指基于遥感影像提取建筑物毁伤信息,辅助大数据信息补充毁伤细节,利用地理信息系统空间分析数字化毁伤信息,构成毁伤数据集。然后,基于毁伤数据集中的训练样本修正经验模型参数,构建了适用于大当量爆炸的针对不同类型建筑物的毁伤评估模型,并基于毁伤数据集中的验证样本测试了模型性能。实验证明:所构建模型拟合优度高于96%,检验样本准确度高于84%,整体误差在可接受范围内。所构建模型在一定精度要求下可为大当量爆炸事故评估提供参考。

To address challenges in the field of large-scale explosive building damage assessment,where the explosion process is too complex for high-precision numerical simulation,and relying solely on change detection from remote sensing imagery cannot capture detailed internal information and lacks the capability of predicting in advance,this paper establishes a building damage assessment model for large-scale explosive events by coupling empirical mechanics models with remote sensing image interpretation and big data analysis.The study initially constructs a damage dataset based on specific historical cases of largescale explosions.This involves extracting building damage information(including building types and damage levels)from remote sensing imagery and supplementing damage details with additional big data sources such as collected online images,videos,and news reports to enhance the precision of the sampled data.Geographic information systems spatial analysis is employed to digitize the damage information,obtaining data on building types,damage levels,and the distance from the target building to the explosion center,forming the damage dataset.Subsequently,the empirical model parameters are refined based on the training samples from the damage dataset,creating damage assessment models applicable to different building types for large-scale explosive events.The performance of the model is then tested using validation samples from the damage dataset.Experimental results demonstrate a model fitting goodness of over 96%,accuracy on validation samples exceeding 84%,and an overall error within an acceptable range.The model,under certain accuracy requirements,can provide guidance for site selection of storage locations for chemicals and hazardous materials,emergency evacuation of people in the event of a risk of large-scale explosions,critical equipment evacuation during an emergency,resource dispatching for rescue and relief after an accident,and building damage assessment.