地震后泥石流灾害柔性防治冲击动力响应分析

采用当前方法分析地震后泥石流灾害柔性防治冲击的动力响应时,不能准确的分析柔性防护体系随时间变化产生的位移变化以及落石在冲击过程中对柔性防护体系的冲击时间与能量关系,得到的分析结果与实际不符,存在分析准确率低的问题。提出地震后泥石流灾害柔性防治冲击动力响应分析方法 ,构建防护体系有限元模型,利用Lagrange描述方法在连续介质力学的基础上构建撞击系统对应的控制方程以及弹塑性材料接触力学模型,在以上两个模型的基础上通过ANSYS/LS-DYNA软件模拟甘肃省陇南市某泥石流治理工程环境,通过分析柔性防护体系随时间变化产生的位移变化以及落石在冲击过程中对柔性防护体系的冲击时间与能量关系,实现地震后泥石流灾害柔性防治冲击动力的响应分析。实验结果表明,所提方法的的分析准确率较高。     

震后早期阶段余震预测研究进展

震后早期快速、准确的余震预测对震后灾害风险应对和采取有效的处置措施十分重要.震后早期阶段地震目录不完整性是影响现有余震预测方法快速、准确预测的关键因素.近年来,随着技术和模型的发展,使得震后早期数据缺失阶段的余震预测成为可能.本文针对震后早期数据缺失阶段难以开展有效的余震预测问题,分别从提升余震检测率角度阐述了匹配滤波技术和深度学习技术,从统计地震学的余震补齐角度阐述了双尺度变换技术,从最大限度利用余震信息实时预测角度阐述了Omi模型和Lippiello模型等研究进展,分析了各类方法的优劣势,并提出了综合解决震后早期数据缺失阶段余震预测“瓶颈期”问题的技术路线,为从事地震检测、余震预测以及震后趋势研判等相关工作的科研人员提供科学参考.     

地震灾情获取中无人机直播关键技术研究

近年来国内多次地震应急经验表明,在震后第一时间获取灾情信息是公共服务的关键,能够有效减轻地震灾害带来的损失,因此快速获取灾情信息并制定合理的救援策略成为减轻灾害的有效方法。无人机灾情获取技术可与传统灾情获取手段相互融合,在多次地震应急中取得了良好的效果。针对单兵系统依赖的远端服务器稳定性差、本地布设的服务器价格高、展示渠道单一等问题,本文研究了基于无人机视频传输快速获取震后现场灾情信息的方法,有助于完善现有灾情信息获取手段。无人机直播技术采用实时消息传输协议,在云服务器中设定推流、拉流对应地址,将采集的图像信息推流至云服务器,实现数据的拉流,现场灾情信息可同时在前后方电脑端、手机端显示。当网络中断时,利用现场应急通信车的卫星组网功能,仍可将灾情信息回传至后方指挥大厅。     

基于迁移学习的无人机高分影像地震泥石流信息检测

地震后经常会引发大量的泥石流灾害(称作地震泥石流),容易造成极大的破坏,无人机低空遥感技术以其便捷、时效性强等特点成为一种快速获取灾害信息的手段,但其影像的光谱信息较为缺乏,较难准确地检测地震泥石流灾害信息。针对以上问题,提出了一种基于迁移学习机制地震泥石流检测方法,该方法在已构建地震泥石流灾害样本库的基础上,将卷积神经网络训练得到的特征迁移到地震泥石流灾害信息检测中,完成地震泥石流灾害信息的自动检测,并将面向对象的地震泥石流灾害信息检测结果与迁移学习支持下的检测结果进行了对比与分析。结果表明:基于迁移学习的地震泥石流灾害信息检测结果在精度上稍优于面向对象的地震泥石流灾害信息检测结果,且前者在保持地震泥石流的平滑性和完整性上要优于后者。     

面向地震应急的自媒体信息挖掘模型

从近几年发生的特大自然灾害事件中可以发现,社交媒体平台正日益成为普通公众及时发布和获取灾情信息的最主要、最便捷的新途径,在这类平台获取的数据中隐藏了大量记录灾情现状的文字、图片等信息。文中首先对海量的历史灾情数据进行统计分析,构建了面向地震应急的信息类别体系和危急度评价体系;基于此训练了用于信息分类的朴素贝叶斯模型,模型的准确率为73. 6%;同时采用机器学习模型和语义计算模型这种特征融合的分类方法,对灾情信息的危急度进行评价,评价模型的准确率为89. 2%。该模型能够在震后实时地对自媒体中出现的灾情信息进行爬取、分类和评价等操作,可从海量的自媒体信息中挖掘出少量危急又重要的信息,以辅助震后的灾情研判和精准救援。文中最后以2017年8月8日九寨沟地震事件为例,从地震烈度速报、震后精准救援2个角度对挖掘数据的可用性进行了研究分析。     

不同容重的泥石流淤积厚度计算方法研究

泥石流的淤积厚度是泥石流灾害评估和防治的最重要的参数之一,但迄今还没有较好的方法计算不同容重的泥石流淤积厚度。本文通过研究泥石流屈服应力的特点,提出了用地区参数和泥石流体积浓度来计算泥石流屈服应力,进而计算泥石流淤积厚度的方法。采用本文方法计算已发生泥石流地区不同容重泥石流的淤积厚度,可以很好地应用于泥石流灾害评估和防治。     

关于开展震害损失评估工作的几点建议

地震灾害损失评估是地震部门的一项职责。科学、准确地进行地震灾害损失评估是震后抢险救灾、重建家园、争取国内国际援助的重要依据。本文从城震灾害评估队伍的建立、充分利用灾区掌握的灾情、应用多种评估技术和方法，开展震灾预测以及编制地震灾害损失评估标准图集等几个方面，对我国目前的地震灾害损失评估工作提出了几点建议。     

钢筋混凝土框架结构震后可存活概率预测方法研究

针对当前钢筋混凝土震后存活预测相关方法存在预测值与实际值拟合度低的问题,提出基于有限元的钢筋混凝土框架结构震后可存活概率预测方法。利用混凝土本构模型关联数值和钢筋本构模型数值计算,实现钢筋混凝土框架材料本构模型关联数值分析。结合建筑和结构施工图实现钢筋混凝土有限元模拟,将钢筋混凝土框架有限元模型的最大竖向荷载作为结构整体构造竖向极限承载力,并引入随机Pushdown方法及随机竖向IDA法得到钢筋混凝土框架震后可存活概率。经实验证明,将有限元应用至钢筋混凝土框架结构震后可存活概率预测中切实可行;预测值与实际值拟合度高于目前常用方法。所提方法的性能完善,可为该领域发展提供可借鉴的信息。     

基于GIS的震后修复建筑物空间分布格网化方法研究

针对传统格网化方法存在计算误差较大、计算效率较低等问题,提出一种新的方法——基于GIS的震后修复建筑物空间分布格网化方法。利用DEM数据、土地数据以及基础地理数据等,提取地形、地貌等各种影响因子。利用建筑面积总和以及土地利用分类数据进行多元线性回归建模,获取50 mm格网尺度下的建筑分布,利用POI数据与建筑空间的相关性,引入四叉树获取最高中心点集,在上述基础上构建格网震后修复建筑物空间分布模型,完成震后修复建筑物空间分布。实验结果表明,所提方法可有效降低计算误差,提高计算效率。     

面向历史灾害地震的Web信息精确抽取与分析方法

以中国大陆地区灾害地震目录为基础，选取2010—2019年灾害地震的互联网信息，提出基于百度搜索引擎的信息获取技术，并以“时间、地名、震级”为关键词，设计一套URL生成规则。使用该技术进行百度检索，得到前100个站点的主体文字信息，建立地震信息基础语料库，形成灾害地震的网络灾情信息获取方法；通过采用已有的停用词词库剔除无用信息，对爬取到的信息进行初步清洗工作，进一步深入挖掘隐含信息，探索灾害关联关系，为震后互联网灾情信息快速获取建立基础。     

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.     

RESEARCH ON SELF-MEDIA INFORMATION MINING MODEL FOR EARTHQUAKE EMERGENCY RESPONSE

From the events of catastrophic natural disasters that have occurred in recent years, it can be found that social media platforms are increasingly becoming the most important and most convenient way for the general public to timely release and obtain information on disasters. The information obtained from such platforms contains a large amount of information in the form of texts, pictures, etc. that record the current situation of the disaster. And it also has characteristics of high efficiency and high spatial distribution to serve the rapid emergency after the earthquake. In this paper, we firstly make a statistical analysis of 32 689 pieces of historical disaster data acquired from 5 earthquakes with obvious characteristics, such as post-earthquake disaster events, user's expression habits and so on, and adopts cross-validation method. Then information classification system which includes seven first-level categories and more than 50 second-level categories is constructed. The information classification system and evaluation system of crisis degree for post-earthquake emergency response are constructed both using cross-validation method. The former is referred to the thought of existing classification basis and the experience knowledge of several emergency experts. Based on the five indicators of subject word, action word, degree word, time and position measurement, an evaluation system of critically with four levels of severity, moderate intensity, mildness and others was constructed. Considering the sparse features of self-media information and the large difference in the number of training sets, a naive Bayes model for information classification is trained based on the classification system and evaluation system. Its accuracy rate is 73.6%. At the same time, the classification method of feature fusion of machine learning model and semantic calculation model is used to evaluate the criticality of the disaster information. The accuracy rate of the evaluation model is 89.2%, higher than 85.2% of the semantic computing model and 77% of the naive Bayesian model. The evaluation model has combined the advantages of semantic computing method which can evaluate all index features with machine learning method which has high classification efficiency and accuracy. The thresholds for classification between mild and moderate intensity, moderate intensity and severe intensity were 15.2 and 27.39. The model realized in this paper can crawl, classify and evaluate the disaster information in the media in real time after an earthquake, and realizes mining of a small amount of critical and important information from the massive self-media information, thus, to assist in earthquake intensity rapid reporting and accurate rescue. Finally, taking the Jiuzhaigou earthquake on August 8, 2017 as an example, 17 432 pieces of data were crawled in real time within 48 hours after the earthquake. At the same time, based on ArcGIS, the mining information is visualized in time and space, and the availability of the data is evaluated from two perspectives of earthquake intensity quick reporting and accurate rescue after the earthquake. The disaster information of Jiuzhaigou County in the earthquake area is obviously more than that of the non-earthquake area in terms of quantity and emergency degree. The results show that the self-media information with high spatial distribution can effectively find the severer disaster grade area after the earthquake, shorten the time of earthquake intensity prediction, effectively classify and extract information, provide real-time information for precise rescue, and improve the efficiency of emergency response after the earthquake.     

地震重灾区诱发次生地质灾害风险评价研究

为了揭示地震重灾区与次生地质灾害的关联性,以某地震灾区为例,在分析次生地质灾害类型与危害的基础上,通过计算一次泥石流总量及流量确定其危险度,并引入易损性指数,确定各评价单元次生山地灾害的易损性大小,设置评价指标;结合研究区实际,采用层次分析法构建次生地质灾害风险评价模型,对地震重灾区诱发次生地质灾害风险进行评价。实验以次生地质灾害中的泥石流产生的灾害风险为例进行研究,结果表明,采用本文研究方法可在有效确定地震重灾区诱发次生地质灾害的位置方面具有一定优势,但在纵向地震造成次生地质灾害风险评价方面需进一步进行研究。     

供水管网震后功能快速修复决策研究

突发地震灾害下,城市供水管网往往不可避免地会发生不同程度的破坏而导致城市供水功能的失效,对人们的生产生活产生了严重影响,快速对震后城市供水管网进行科学合理的功能修复,对于降低地震灾害损失具有重要意义。本文以城市供水管网为对象,针对震后管网系统功能评价及快速修复决策等问题进行了研究。在中国点式渗漏模型的基础上,采用基于破坏概率的渗漏面积计算方法,建立了考虑地震破坏随机性的管线渗漏流量计算模型;针对震后破坏管线展开了水力分析,并提出了以节点水头损失比为指标的节点服务性能失效状态量化方法;进而建立了震后管线的修复决策模型以及功能修复费用模型。研究成果可为震后功能修复工作中管线修复顺序及修复方案的优选提供科学依据,可显著提高震后应急救灾效率,实现城市供水功能的快速修复。     

灾区震后重建中建筑工程造价快速评估技术研究

针对传统方法无法对灾区震后重建中的建筑工程造价进行快速评估的问题,提出适合灾区震后重建的,基于灰色动态预测的建筑工程造价快速评估技术。采用朗格系数法对灾区重建中建筑工程造价进行综合评估分析,确定重建工程造价范围,通过加权平均数指数法对建筑工程造价情况进行判断,构建自回归移动模型防止建筑工程造价评估中受到异常因素影响,分析人工使用费、机械设备使用费、材料使用费,计算项目造价指数,根据基期单方造价与报告期单方造价计算结果,建立灰色动态预测图,实现对灾区震后重建中建筑工程造价快速评估。实验结果表明,采用改进评估技术可在短时间内对灾区震后重建中建筑工程造价进行评估,且评估误差较低,具有一定的优势。     

LM-BP神经网络在震后建筑损失评估模型中的应用

当前震后建筑经济损失评估模型得到的震后建筑经济损失评估精确度、效率低,针对单一神经网络易产生局部极值等问题,对神经网络方法进行了改进,提出LM-BP神经网络在震后建筑损失评估模型中的应用。输入样本要素为影响震后建筑经济损失的5项因素,输出样本是震后建筑经济损失评估结果,在此基础上采用LM-BP神经网络将训练转化成最小二乘问题,结合LM算法重新定义隐含层节点数量,构建基于LM-BP的神经网络震后经济损失评估模型,采用该模型获取最优震后建筑经济损失评估结果。仿真实验结果表明,所设计的评估模型最小评估误差为0.1%,相比同类模型具有高精确度的优势,是一种可靠的震后建筑经济损失评估模型。     

遥感信息中地震灾情危险等级在线应急评定方法研究

利用GIS技术对地震危险等级进行评定时,由于其地形控制点选取合理性较差,导致其所采集遥感图像清晰度较低,地震等级评定不够精准。针对此问题提出一种新的地震灾情遥感信息危险等级在线应急评定方法。利用图像几何校正法,对遥感图像进行分幅裁剪,基于裁剪结果选取地面控制点,提取有价值遥感数据信息,建立遥感解译评估指标。将推导出的综合震灾指数引入到指标中,将各个评价单元的信息进行等级排序和划分,完成地震灾情遥感信息危险等级在线应急评定。仿真实验中,对所提方法和GIS地震危险等级评定方法进行有效性对比测试。实验结果表明,地震灾情遥感信息危险等级在线应急评定方法提升了灾情地形控制点选取的合理性,使获取的遥感图像更清晰,灾情等级评定结果更精准。     

Pre- and post-earthquake regional loss assessment using deep learning

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.     

Loss forecasting of earthquake fire based on radial basis function network

Introduction The earthquake fire is one of main hazards in subsequent hazards of earthquake, which caused much severer damage than the ground motion itself. So it is significant to evaluate and predict loss of earthquake fire as a part of hazard of earthq…     

基于径向基函数网络的地震火灾损失预测

针对地震火灾的复杂性和多变性的特点，利用径向基函数网络的自学习、自适应能力和容错性特性，根据地震火灾的历史资料，建立了基于径向基函数网络的地震火灾损失预测模型，并对该模型进行了检验和讨论，说明本方法的适用性，也为其它自然灾害的损失预测提供了简单、有效的方法.