基于LM-BP神经网络的地震直接经济损失快速评估方法研究

在综合考虑地震致灾因子、抗震设防因子、经济指标因子的基础上,选取地震震级、震源深度、受灾面积、受灾人口、设计基本地震加速度、人均GDP和产业机构比例等7个因素作为主要评价指标,运用神经网络分析方法,建立了基于LM-BP神经网络的地震直接经济损失评估模型。从历史地震事件中提取相关数据作为样本,并使用该样本对网络进行训练。最后对模型输出结果的误差率和模型的泛化能力进行分析,认为该模型可以有效评估地震直接经济损失,并具有较高的稳定性。     

基于人工神经网络的地震经济损失评估

地震经济损失估计是涉及多方面、多级次的复杂指标体系的非线性动力问题。其指标体系的选取以具有代表性、可靠、易获取、易于定量化为原则。应用MATLAB6.5神经网络工具箱,建立了适用于震后经济损失快速评估的三层BP神经网络地震灾害经济评估模型。     

基于模糊数学的震后建筑火灾危险性评估模型研究

震后建筑火灾涉及因素多,传统评估模型忽略了建筑截面温度变化与建筑形变程度的影响,导致评估准确度较低。为解决此问题,通过模糊数学方法建立震后建筑火灾危险性评估模型。在建立判断矩阵的基础上,获取评估模型权重,确定隶属度矩阵;通过确定震后建筑火灾后截面温度变化评估的因素集与评语集,进行单因素评判,为评估因素集中的因素赋予权重,实现一级模糊评估;再将一级评估结果作为二级评估的单因素评估,结合模糊数学分析完成对震后建筑火灾危险性的评估。实验中以建筑横梁截面温度变化与形变程度为指标,对震后建筑火灾危险性进行评估。实验结果表明,采用所提模型进行危险性评估,震后在发生火灾时建筑结构受到火灾影响,横梁截面温度越高,导致形变程度越大,危险性更高,模拟实验结果与实际情况更加接近,所提模型评估精度高。     

强震后既有建筑火灾危险性的模糊数学评估模型研究

在强烈地震发生后,会引发建筑火灾等次生灾害,涉及因素较多,传统火灾危险性数学模型忽略了强震后既有建筑发生火灾时不同因素的随机性与模糊性特性,难于建立健全的评估数学模型,导致评估精度低。为解决该问题,通过分析强震后既有建筑火灾影响,用因素模糊数学方法建立强震后既有建筑火灾危险性评估数学模型及评估体系。具体方法是对评估指标体系中各层因素针对上层因素影响进行评分,建立判断矩阵,获取权重。确定隶属度矩阵,获取强震后既有建筑火灾危险性评估的因素集与评语集,构造单因素评判,给评估集中的因素赋予权重,进行一级模糊评估。把一级评估结果当成二级评估的单因素评估,通过模糊数学理论完成对强震后既有建筑火灾危险性的评估,得到综合评估结果。实验结果表明,采用所提模型进行危险性评估,得到结果符合实际情况,与其他模型相比,所提模型评估精度高。     

三维测量光流恢复算法结合BIM对震后建筑倾斜度测量的研究

震后危险建筑倾斜度的移动测量过程较为复杂,常规测量方法会存在对称偏差。为了提高震后危险建筑倾斜度测量的准确性,设计三维测量光流恢复算法结合BIM的震后建筑倾斜度测量方法。采用BIM技术采集并整合全部建筑倾斜度信息,构建危险建筑信息模型;采用三维测量光流恢复算法获取所构建建筑信息模型内多个目标特征点,设计震后危险建筑倾斜度的三维测量模型。仿真实验说明,所提方法测量震后危险建筑三维坐标值误差率均小于0.4%,平均评估时间仅为2.5 s,说明该方法可提高建筑倾斜度评估效果。     

基于宏观经济指标对张家口地区地震灾后经济损失快速评估

利用宏观经济指标—GDP(国内生产总值)结合宏观易损性模型对河北省张家口地区地震灾害损失进行快速评估。本文以实际震例数据为基础,通过宏观易损性分析法分别对震后科考实际影响场进行震后经济损失评估,并与震后实际经济损失进行对比,初步验证该方法在小尺度空间范围内震后经济损失快速评估中的可行性。     

震后建筑进度BIM估计模型改进

传统震后建筑进度BIM估计模型,未考虑精益管理对建筑施工的影响,造成建筑成本浪费较多,影响后期建筑施工进度。本文构建基于BIM和精益管理的震后建筑进度评估模型,根据模型细分震后建筑进度评估过程,在此基础上根据BIM实施三维算量,采用进度计划编制子模型获取各分项工程量,确定建筑施工的主要进度计划,实现对建筑进度计划的编制;通过虚拟施工和现实施工两条主线,利用进度控制子模型实现对施工状态的模拟和精益管理。以此为基础,进行挣值分析比较计划施工成本、实际施工成本和挣值曲线,获取震后建筑的施工进度与成本情况。实验结果说明,本文构建的模型可对震后建筑进度和工程成本进行精准估计,能够减少成本浪费。     

基于PSO优化ELM模型的震后直接经济损失评估

选取震级、震中烈度、震中烈度和抗震设防烈度之差、人均GDP、人口密度5个影响因子作为评估指标,筛选出1996—2014年间37个历史地震作为研究样本,构建PSO-ELM震后直接经济损失评估模型。实验表明：PSO-ELM模型预测的平均误差率为13.89%,其平均误差率相比于未优化的ELM和传统的BP网络模型分别降低了14.61%和36.79%;通过分析影响因子的敏感程度,得出人均GDP、人口密度和震中烈度影响最为明显,在震后直接经济损失评估中应该重点关注。综上,PSO-ELM模型具有精度高和泛化能力强等优点,可在实际案例中推广使用。     

基于神经网络方法的地下管道系统地震可靠性分析

基于反向传播的多层前馈网络（BP网络）理论，建立了管道单体地震反应预测模型和管道系统连通性预测模型。模拟数值计算提供的样本，两个模型通过离线学习，得到训练后的神经网络，以此执行实时模拟，两模型充分利用了BP网络较好的泛化能力，达到了迅速评估管网系统震后运行状态的目的，神经网络方法克服了传统方法需要大量计算时间的缺点，为地下管道的震后评估提供了一条新的途径。     

地震直接经济损失快速评估方法研究

回顾了我国地震直接经济损失评估研究的发展过程,以我国1990年~2013年间261次破坏性地震直接经济损失数据为样本,利用最小二乘法分别建立了基于震中烈度、震级、震中烈度与震级等3种地震直接经济损失快速评估模型,通过实例证明基于震中烈度与震级的评估模型更加合理可靠,可为地震直接经济损失快速评估提供参考。     

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.     

基于RS-PCA-GA-SVM的砂土液化预测方法研究

砂土液化是一种危害性比较大的自然灾害,对砂土液化进行判定预测在地质灾害防治领域中有重要的研究意义。通过粗糙集理论(Rough Set,RS)对影响砂土液化的6个初始评价指标(包括震级、土深、震中距、地下水位、标贯击数和地震持续时间)进行属性约简,去掉冗余或干扰信息,得到基于4个核心预测指标的数据集。通过主成分分析法(Principal Component Analysis,PCA)从核心评价指标中提取出主成分,采用支持向量机(Support Vector Machine,SVM)对数据集进行训练,用遗传算法(Genetic Algorithm,GA)优化参数,建立砂土液化的RS-PCA-GA-SVM预测模型。并结合砂土液化实际数据将预测结果与基于Levenberg-Marquardt算法改进的BP神经网络模型(LM-BP)的预测结果做比较。实例计算表明:基于RS-PCA-GA-SVM模型得到的砂土液化预测结果精度较LM-BP神经网络有很大的提高,判别结果与实际情况比较吻合,可在实际工程中应用。     

高精度静态人口空间分布研究——以银川市西夏区为例

地震发生后，人口空间分布密度是决定救援力量部署的重要依据。然而，高精度人口空间分布数据存在获取和更新困难的问题，缺少有效的解决途径。以银川市西夏区为例，基于高空间分辨率遥感影像，通过建筑物解译与实地调查相结合的方式获取住宅建筑物信息，建立人口与住宅建筑物之间的关系模型，得到更客观真实的人口空间分布情况。研究结果表明，以高空间分辨率遥感影像解译住宅建筑物作为人口空间分布指示因子建模，得到的总体预测人口误差率为3.56%，人口平均相对误差率为9.19%，研究结果具有较高的可靠性，为震前灾害风险评估和震后灾情快速评估提供可靠的人口空间分布数据。     

STUDY ON THE MRF-BASED METHOD FOR DAMAGED BUILDINGS EXTRACTION FROM THE SINGLE-PHASE SEISMIC IMAGE

Earthquake events are one of the most extraordinarily serious natural calamities, which not only cause heavy casualties and economic losses, but also various secondary disasters. Such events are devastating, and have far-reaching influences. As the main disaster bearing body in earthquake, buildings are often seriously damaged, thus it can be used as an important reference for earthquake damage assessment. Identifying damaged buildings from post-earthquake images quickly and accurately is of real importance, which has guidance meaning to rescue and emergency response. At present, the assessment of earthquake damage is mainly through artificial field investigation, which is time-consuming and cannot meet the urgent requirements of rapid emergency response. Markov Random Field(MRF)combines the neighborhood system of pixels with the prior distribution model to effectively describe the dependence between spatial pixels and pixels, so as to obtain more accurate segmentation results. The support vector machine(SVM)model is a simple and clear mathematical model which has a solid theoretical basis; in addition, it also has unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems. Thus, in this paper, a Markov random field-based method for damaged buildings extraction from the single-phase seismic image is proposed. The framework of the proposed method has three components. Firstly, Markov Random Field was used to segment the image; then, the spectral and texture features of the post-earthquake damaged building area are extracted. After that, Support Vector Machine was used to extract the damaged buildings according to the extracted features. In order to evaluate the proposed method, 5 areas in ADS40 earthquake remote sensing image were selected as experimental data, this image covers parts of Wenchuan City, Sichuan Province, where an earthquake had struck in 2008. And in order to verify the applicability of this method to different resolution images, an experimental area was selected from different resolution images obtained by the same equipment. The experimental results show that the proposed method has good performance and could effectively identify the damaged buildings after the earthquake. The average overall accuracy of the selected experimental areas is 93.02%. Compared with the result extracted by the widely used eCognition software, the proposed method is simpler in operation and can improve the extraction accuracy and running time significantly. Therefore, it has significant meaning for both emergency rescue work and accurate disaster information providing after earthquake.     

基于费用效益分析的建筑物加固决策体系研究

突发地震灾害往往带来巨大的生命财产损失,对建筑物进行科学合理的震前加固及震后功能快速修复;对降低地震灾害损失具有重要意义。通过定义社会贴现率和残值率等加固效益分析评价参数,基于加固费用模型和地震直接经济损失模型进行了加固费用现值与效益现值计算;采用费用效益比（Cost-Benefit Ratio,CBR）作为评价指标对建筑物进行加固决策分析,并最终建立了相应的加固决策体系。最后将提出的理论、模型及方法集成于课题组研发的"中国地震灾害损失评估系统（CEDLAS）"补充建立加固决策分析模块,进而通过陕西省灞桥区的综合应用示范,验证所建立的加固决策体系的合理性。研究可为政府制定在役建筑物的加固决策提供科学依据。     

玉树7.1级地震震后损失快速评估

2010年4月14日7时49分,在青海省玉树县发生里氏7.1级地震,震中位于北纬33.2°、东经96.6°,震源深度为14 km.在玉树地震发生后,基于经验模型的震后损失快速评估方法,用较少的信息和数据,对玉树地震灾情进行了快速应急评估,绘制了经验等震线图,给出了房屋损失的初步评估结果,与最终的损失调查统计结果相比,震...     

Simulation of Silty Clay Compressibility Parameters Based on Improved BP Neural Network Using Bayesian Regularization

Soil compressibility parameters are important indicators in the geotechnical field and are affected by various factors such as natural conditions and human interference. When the sample size is too large, conventional methods require massive human and financial resources. In order to reasonably simulate the compressibility parameters of the sample, this paper firstly adopts the correlation analysis to select seven influencing factors. Each of the factors has a high correlation with compressibility parameters. Meanwhile, the proportion of the weights of the seven factors in the Bayesian neural network is analyzed based on Garson theory. Secondly, an output model of the compressibility parameters of BR-BP silty clay is established based on Bayesian regularized BP neural network. Finally, the model is used to simulate the measured compressibility parameters. The output results are compared with the measured values and the output results of the traditional LM-BP neural network. The results show that the model is more stable and has stronger nonlinear fitting ability. The output of the model is basically consistent with the actual value. Compared with the traditional LM-BP neural network model, its data sensitivity is enhanced, and the accuracy of the output result is significantly improved, the average value of the relative error of the compression coefficient is reduced from 15.54% to 6.15%, and the average value of the relative error of the compression modulus is reduced from 6.07% to 4.62%. The results provide a new technical method for obtaining the compressibility parameters of silty clay in this area, showing good theoretical significance and practical value.     

基于GIS技术的震后泥石流灾害损失耦合预测方法

当前预测震后泥石流灾害损的方法所用时间较长,且预测结果误差较大,存在预测效率低和预测准确率低的问题。本文基于GIS技术的震后泥石流灾害损失耦合预测方法,采用GIS技术获取震后泥石流灾害的相关信息,根据获取的信息建立流域水量计算模型、固体物质量计算模型、泥石流起动模型,对泥石流的起动过程进行分析,在财产损失预测模型和人员损失预测模型的基础上构建震后泥石流灾害损失耦合预测模型,实现震后泥石流灾害损失的预测。结果表明:本文所提方法预测效率高、预测准确率高。     

泸定Ms6.8地震房屋损失快速评估

震后房屋损失的快速评估对于灾后应急救援等至关重要。现有的地震风险评估方法要么仅提供损失的均值,要么以某一方差常数来描述损失的分布特征,均无法准确有效地反映各空间位置点损失的随机性及相关关系,最终影响整体损失评估结果的准确度。本文基于Copula理论,提出了一种适用于地震巨灾风险分析的相关随机变量模拟方法,好处是在实现快速计算的同时,能够考虑地震损失中的不确定性与相关性。利用所提方法对2022年9月5日四川泸定6.8级地震的房屋损失进行评估,得到了各结构类型与县区的损失分布,并与PAGER方法所得到的损失分布进行对比。结果表明：此次地震房屋总体损失超过89.8%的概率处于10～100亿元人民币量级水平,其中超过50.8%的概率为20～50亿元人民币;损失较大的三个县区分别是泸定县、石棉县和荥经县,砌体结构的经济损失约是框架结构的2倍;相比于PAGER,该方法给出的损失概率分布形状更加灵活,能够详细地反映不同县区的房屋损失特征。研究方法和结果为震后损失快速评估技术提供参考,也为未来地震的灾后应急救援等提供科学依据。