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作 者:赵真 郭红梅[1] 张莹[1] 尹文刚 鲁长江[1] 范开红[1] 张翼[1] ZHAO Zhen;GUO Hongmei;ZHANG Ying;YIN Wen'gang;LU Changjiang;FAN Kaihong;ZHANG Yi(Sichuan Earthquake Agency,Chengdu 610041,Sichuan,China;Officers College of PAP,Chengdu 610041,Sichuan,China)
机构地区:[1]四川省地震局,四川成都610041 [2]武警警官学院,四川成都610041
出 处:《地震工程学报》2024年第6期1405-1414,共10页China Earthquake Engineering Journal
基 金:四川省重点研发项目(20ZDYF1085);国家重点研发计划专题(2020YFA07106003-07);中国地震局地震工程与工程振动重点实验室重点专项(2019EEEVL0102-04);四川地震科技创新团队专项(201901)。
摘 要:破坏性地震发生后,迅速准确地预测建筑物破坏程度,对快速科学地开展地震应急指挥、救援力量部署等工作具有重大意义。针对现有建筑震害预测模型存在的评估结果粗糙、数据获取难度较高、计算工作量大、模型构建难度大、普适性不强等问题,提出一种基于遥感初判的建筑物震害Fisher判别法。首先,从地震强度和建筑物抗震能力两方面选取震级、震中距、场地条件及建筑物抗震能力4种震害作为判别因子;然后,基于判别分析理论,构建建筑物震害Fisher判别模型;最后,以四川泸定县6.0级地震为例,对文章提出方法进行验证。实验结果表明:该方法的预测结果与实际震害基本一致,准确率高达80%以上,证明该方法具有较高的可靠性,能较准确地对建筑物震害程度进行科学预测。After a destructive earthquake,quickly and accurately predicting the damage degree of buildings is of great importance to quickly and scientifically carry out earthquake emergency command and rescue force deployment.Existing building seismic damage prediction models yield imprecise evaluation results,struggle with data acquisition,and require a large amount of computing power;therefore,they are difficult to construct and lack universality.To address these issues,this paper proposes a Fisher discriminant method for seismic damage of buildings based on preliminary judgment from remote sensing.First,four seismic damage factors,including magnitude,epicentral distance,site condition,and seismic resistance of the building,were selected as the discriminant factors.Then,based on the discriminant analysis theory,the Fisher discriminant model of building damage was constructed.Finally,the method proposed in this paper was verified by taking the Luxian M 6.0 earthquake as an example.The experimental results show that the prediction results of the proposed method closely align with the actual earthquake damage,with an accuracy rate of 80%,which proves that this method is highly accurate,reliable,and can accurately predict the earthquake damage degree of buildings.
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