zhenbo

ISSN 2096-7780 CN 10-1665/P

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

区域与城市地震风险评估与监测技术研究项目及研究进展

张令心 钟江荣 林旭川 孙利民 公茂盛 纪晓东 鲍跃全 何浩祥

张令心, 钟江荣, 林旭川, 孙利民, 公茂盛, 纪晓东, 鲍跃全, 何浩祥. 区域与城市地震风险评估与监测技术研究项目及研究进展[J]. 地震科学进展, 2020, (3): 1-19. doi: 10.3969/j.issn.2096-7780.2020.03.001
引用本文: 张令心, 钟江荣, 林旭川, 孙利民, 公茂盛, 纪晓东, 鲍跃全, 何浩祥. 区域与城市地震风险评估与监测技术研究项目及研究进展[J]. 地震科学进展, 2020, (3): 1-19. doi: 10.3969/j.issn.2096-7780.2020.03.001
Lingxin Zhang, Jiangrong Zhong, Xuchuan Lin, Limin Sun, Maosheng Gong, Xiaodong Ji, Yuequan Bao, Haoxiang He. Project plan and research progress on regional and urban earthquake risk assessment and monitoring technology[J]. Progress in Earthquake Sciences, 2020, (3): 1-19. doi: 10.3969/j.issn.2096-7780.2020.03.001
Citation: Lingxin Zhang, Jiangrong Zhong, Xuchuan Lin, Limin Sun, Maosheng Gong, Xiaodong Ji, Yuequan Bao, Haoxiang He. Project plan and research progress on regional and urban earthquake risk assessment and monitoring technology[J]. Progress in Earthquake Sciences, 2020, (3): 1-19. doi: 10.3969/j.issn.2096-7780.2020.03.001

区域与城市地震风险评估与监测技术研究项目及研究进展

doi: 10.3969/j.issn.2096-7780.2020.03.001
基金项目: 国家重点研发计划项目(2017YFC1500600)资助。
详细信息
    通讯作者:

    钟江荣(1974-),男,副研究员,主要从事地震灾害及风险评估工作。E-mail:zjrll@163.com

  • 中图分类号: P315.9

Project plan and research progress on regional and urban earthquake risk assessment and monitoring technology

  • 摘要: 我国城市化进程的加快使人口与财富高度集中,城市向大型化、复杂化发展,在地震面前变得越发脆弱,而我国多数城市位于地震高危险区,灾害风险迅速攀升。充分借鉴国际减轻地震灾害风险先进理念,结合当今智能技术,开展地震风险评估与监测技术研究,已成为我国当前防震减灾工作的重中之重。国家重点研发计划项目“区域与城市地震风险评估与监测技术研究”以研发高性能区域与城市地震灾害监测及组网观测技术为手段,建立融合工程结构性态、社会和经济等多元信息的区域与城市大震风险动态评价指标体系、评估技术和软件系统平台,并开展应用示范,实现区域与城市地震灾害风险科学化、精准化和动态化评估,为显著提升我国抗御地震灾害风险能力提供关键技术支撑。经过两年的研究,设计并生产了MEMS加速度计样品,提出了观测网络优化布置方法、典型结构台阵优化布设方案和改进的数据多跳路由算法数据传输模式;构建了RC构件可视损伤识别的卷积神经网络Damage-Net,引入强跟踪滤波算法,实现了建筑结构体系时变物理参数的有效追踪,并建立了建筑抗震韧性评价方法;提出了基于计算机视觉的数据异常探测方法、桥梁结构基于弹塑性耗能差率的损伤指数模型和基于卷积神经网络和递归图的桥梁损伤识别方法,建立了桥梁地震破坏监测和性态评估标准 Benchmark模型;分别建立了基于遥感数据的建筑物提取技术、单体建筑结构和区域建筑群结构性能水平恢复函数模型和结构恢复能力计算方法,构建了区域和城市大震风险评估指标体系和风险动态评价模型;提出了基于物联网大震灾害监测系统总体架构、考虑多损伤状态的参数化桥梁地震灾害风险评估模型,开发了建筑群地震灾害仿真系统;初步完成了示范建筑地震监测方案设计,完成了示范桥梁地震监测网络建设和三河市多元信息的数据库建设;初步设计了三河市区域地震灾害监测网络。

     

  • 图  1  课题设置及其关联关系图

    Figure  1.  Task setting and its relation graph

    图  2  项目研究技术路线

    Figure  2.  Technical route for project research

    图  3  加速度传感器接口ASIC版图

    Figure  3.  ASIC layout designed for the new accelerometer

    图  4  背景场环境实测

    Figure  4.  Test of the new accelerometer at seismological station

    图  5  初步搭建的振动监测试验系统

    Figure  5.  Preliminary test system of vibration monitoring

    图  6  累加猴群算法流程图

    Figure  6.  Flow chart of the Monkey Algorithm(MA)

    图  7  高层结构模拟分析与环境振动测试

    Figure  7.  Numerical analysis and vibration test of a high-rise building

    图  8  3种方案工作原理

    Figure  8.  Diagrams of the three data transmission schemes

    图  9  RC构件可视损伤语义分割数据库样本

    Figure  9.  Visual damage semantic segmentation database sample of RC component

    图  10  RC构件可视损伤识别神经网络4Cate-Net和Crack-Net

    Figure  10.  Neural networks for visual damage identification of RC members:4Cate-Net and Crack-Net

    图  11  利用试验数据识别的模型各层间刚度变化曲线

    Figure  11.  The stiffness curves of the model layers were identified by the test data

    图  12  修正模型计算响应和试验测量响应对比图

    Figure  12.  Comparison of calculated response and measured response of the modified model

    图  13  建筑功能恢复目标图

    Figure  13.  The goal of building function restoration

    图  14  建筑功能和修复时间、修复费用关系图

    Figure  14.  Diagram of building function,repair time and cost

    图  15  桥梁地震反应分析

    Figure  15.  Seismic response analysis of bridge

    图  16  基于弹塑性耗能差率的损伤指数模型的桥梁地震损伤识别结果

    Figure  16.  Earthquake damage identification of bridge based on damage index model of elastic-plastic energy dissipation difference rate

    图  17  基于深度学习和权重采样的结构可靠度主动学习算法

    Figure  17.  Active learning algorithm of structural reliability based on depth learning and weight sampling

    图  18  海文大桥Benchmark模型平台

    Figure  18.  Benchmark platform of Haiwen Bridge

    图  19  三河市人口公里网格分布图

    Figure  19.  Population-kilometer grid of Sanhe city

    图  20  三河市GDP公里网格分布图

    Figure  20.  GDP-kilometer grid of Sanhe city

    图  21  SIR恢复函数与3种恢复函数恢复能力对比

    Figure  21.  Comparison of recovery ability between SIR recovery function and three kinds of recovery function

    图  22  单体建筑物中人员伤亡的预测示例

    Figure  22.  Prediction example of casualties in single building

    图  23  区域和城市震后功能可恢复性评估框架

    Figure  23.  Evaluation framework for regional and urban functional recoverability after earthquake

    图  24  区域与城市地震灾害风险评估指标体系示意图

    Figure  24.  Sketch map of regional and urban earthquake disaster risk assessment index system

    图  25  区域监测系统架构图

    Figure  25.  Regional monitoring system architecture

    图  26  地震动反演结果-无噪声

    Figure  26.  Seismic inversion results(no noise)

    图  27  地震动时程反演结果-10%噪声

    Figure  27.  Seismic inversion results(10% noise)

    图  28  示例路网结构

    Figure  28.  Example road network structure

    图  29  与示例路网结构对应的贝叶斯网络结构

    Figure  29.  Bayesian network structure corresponding to example road network structure

    图  30  区域桥梁网络布置图

    Figure  30.  Layout map of regional bridge network

    图  31  区域各桥梁的地震风险评估结果

    Figure  31.  Earthquake risk assessment results of regional bridges

    图  32  结构健康监测与拟实时区域在线分析原理

    Figure  32.  Principle of structural health monitoring and quasi-real-time zone on-line analysis

    图  33  清华大学土木馆强震监测云平台实时监测数据显示

    Figure  33.  Real-time strong earthquake monitoring data display of cloud platform in civil hall of Tsinghua University

    图  34  15层大比例尺的短肢剪力墙地震模拟振动台模型

    Figure  34.  15-story large-scale shaking table model of short-leg shear wall

    图  35  海文大桥主桥变形监测测点布置图

    Figure  35.  Layout of deformation monitoring points for main bridge of Haiwen Bridge

  • [1] Jonathan E F Remo,Nicholas Pinter. Hazus-MH earthquake modeling in the central USA[J]. National Hazards,2012,63(2):1055-1081 doi: 10.1007/s11069-012-0206-5
    [2] FEMA P-58-1. Seismic performance assessment of buildings: Volume 1-Methodology[R]. Washington, DC: Federal Emergency Management Agency, 2012
    [3] Muneo Hori,Tsuyoshi Ichimura. Current state of integrated earthquake simulation for earthquake hazard and disaster[J]. J. Seismol.,2008,12(2):307-321 doi: 10.1007/s10950-007-9083-x
    [4] 中国地震局. 数字强震动加速度仪(DB/T 10—2016)[S]. 北京: 地震出版社, 2016

    China Earthquake Administration. Digital strong motion accelerometer (DB/T 10—2016)[S]. Beijing: Seismological Press, 2016
    [5] Feng D M,Feng M Q. Vision-based multipoint displacement measurement for structural health monitoring[J]. Structural Control and Health Monitoring,2016,23(5):876-890 doi: 10.1002/stc.1819
    [6] Zhao R,Tang W. Monkey algorithm for global numerical optimization[J]. Journal of Uncertain Systems,2008,2(3):165-176
    [7] 周宝峰,樊圆,温瑞智,等. 建筑结构地震反应观测台阵的发展现状及展望[J]. 地震工程与工程振动,2017,37(3):57-66

    Zhou Baofeng,Fan Yuan,Wen Ruizhi,et al. Development status and prospect for building structures seismic response observation array[J]. Earthquake engineering and engineering dynamics,2017,37(3):57-66
    [8] Ji Xiaodong,Cheng Xiaowei,Xu Mengchao. Coupled axial tension-shear behavior of reinforced concrete walls[J]. Engineering Structures,2018,167:132-142 doi: 10.1016/j.engstruct.2018.04.015
    [9] Cheng Xiaowei,Ji Xiaodong,Henry Richard S,et al. Coupled axial tension-flexure behavior of slender reinforced concrete walls[J]. Engineering Structures,2019,188:261-276 doi: 10.1016/j.engstruct.2019.03.026
    [10] 纪晓东,程小卫,徐梦超. 小剪跨比钢筋混凝土墙拉剪性能试验研究[J]. 工程力学,2018,35(S1):53-61 doi: 10.6052/j.issn.1000-4750.2017.06.S004

    Ji Xiaodong,Cheng Xiaowei,Xu Mengchao. Experimental study on tension-shear behavior of low- aspect-ratio RC walls[J]. Engineering Mechanics,2018,35(S1):53-61 doi: 10.6052/j.issn.1000-4750.2017.06.S004
    [11] Tang Z Y,Chen Z C,Bao Y Q,et al. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring[J]. Structural Control and Health Monitoring,2019,26(1):e2296.1-e2296.22
    [12] Xu Y,Wei S Y,Bao Y Q,et al. Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network[J]. Structural Control and Health Monitoring,2019,26(3):e2313.1-e2313.22
    [13] Xiang Z L,Chen J H,Bao Y Q,et al. An active learning method combining deep neural network and weighted sampling for structural reliability analysis[J]. Mechanical Systems and Signal Processing,2020,140:106684 doi: 10.1016/j.ymssp.2020.106684
    [14] Xiang Z L,Bao Y Q,Tang Z Y,et al. Deep reinforcement learning-based sampling method for structural reliability assessment[J]. Reliability Engineering & System Safety,2020:106901
    [15] 何浩祥,陈奎,范少勇. 基于弹塑性耗能差率的地震损伤评估模型及分析方法[J]. 振动工程学报,2018,31(3):382-390

    He Haoxiang,Chen Kui,Fan Shaoyong. Seismic damage model based on differential ratio of elastic plastic dissipated energy and application[J]. Journal of Vibration Engineering,2018,31(3):382-390
    [16] Bruneau M,Reinhorn A M. Exploring the concept of seismic resilience for acute care facilities[J]. Earthquake Spectra,2007,23(1):41-62 doi: 10.1193/1.2431396
    [17] Dong Y,Frangopol D M. Performance-based seismic assessment of conventional and base-isolated steel buildings including environmental impact and resilience[J]. Earthquake Engineering & Structural Dynamics,2016,45(5):739-756
    [18] 何浩祥,闫维明,李晓飞. 基于SIR模型的工程材料统一单轴本构关系研究[J]. 计算力学学报,2014,31(1):84-90

    He Haoxiang,Yan Weiming,Li Xiaofei. Uniform constitutive model based on double exponential function and application for engineering Materials[J]. Chinese Journal of Computational Mechanics,2014,31(1):84-90
    [19] 李英民,杨龙,刘硕宇,等. 基于可恢复指标的结构损伤机制评价方法[J]. 浙江大学学报(工学版),2017,51(11):2197-2206

    Li Yingmin,Yang Long,Liu Shuoyu,et al. Method of failure mode evaluation of structure based on seismic resilience index[J]. Journal of Zhejiang University-SCIENCE,2017,51(11):2197-2206
    [20] 韩建平,付志君. 钢筋混凝土框架结构震后功能恢复能力的量化研究[J]. 世界地震工程,2018,34(1):17-23

    Han Jianping,Fu Zhijun. Quantitative investigation on post earthquake resilience capacity of reinforced concrete frame structure[J]. World Earthquake Engineering,2018,34(1):17-23
    [21] Cimellaro G P,Reinhorn A M,Bruneau M. Seismic resilience of a hospital system[J]. Structure and Infrastructure Engineering,2010,6(1-2):127-144 doi: 10.1080/15732470802663847
    [22] 陆新征. 工程地震灾变模拟−从高层建筑到城市区域[M]. 北京: 科学出版社, 2015: 165-170

    Lu Xinzheng. Earthquake disaster simulation of civil infrastructures: From tall buildings to urban areas[M]. Beijing: Science Press, 2015: 165-170
    [23] Sun Limin,Shang Zhiqiang,Xia Ye,et al. A review of bridge structural health monitoring with the aid of big data and artificial intelligence:From condition assessment to damage detection[J]. Asce Journal of Structural Engineering,2020,146(5):04020073 doi: 10.1061/(ASCE)ST.1943-541X.0002535
    [24] 孙利民,尚志强,夏烨. 大数据背景下的桥梁结构健康监测研究现状与展望[J]. 中国公路学报,2019,32(11):1-20

    Sun Limin,Shang Zhiqiang,Xia Ye. Development and prospect of bridge structural health monitoring in the context of big dat[J]. China Journal of Highway and Transport,2019,32(11):1-20
    [25] 夏烨,王鹏,孙利民. 基于多源信息的桥梁网级评估方法[J]. 同济大学学报(自然科学版),2019,47(11):1574-1584

    Xia Ye,Wang Peng,Sun Limin. A condition assessment method for bridges at network level based on multi-source information[J]. Journal of Tongji University (Natural Science),2019,47(11):1574-1584
  • 加载中
图(35)
计量
  • 文章访问数:  1046
  • HTML全文浏览量:  375
  • PDF下载量:  76
出版历程
  • 收稿日期:  2019-03-09
  • 修回日期:  2019-03-16
  • 刊出日期:  2020-03-01

目录

    /

    返回文章
    返回
    本系统由北京仁和汇智信息技术有限公司设计开发 百度统计