Project plan and research progress on regional and urban earthquake risk assessment and monitoring technology
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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。Abstract: With the acceleration of the urbanization process in China, the population and wealth become highly concentrated, and cities tend to be larger and more complex, which makes them more vulnerable to earthquakes. What is worse, most of the cities in China are unfortunately located in the earthquake-prone regions, making the risk of earthquake disaster rise rapidly. It has become a top priority of China’s earthquake disaster mitigation work to carry out the research on earthquake risk assessment and monitoring technology by making full use of the international advanced ideas on seismic disaster risk mitigation and combining today’s intelligence technology. The national key research and development project entitled “Research on regional and urban earthquake risk assessment and monitoring technology” was initiated to establish the dynamic evaluation index system, assessment technology and software platform for the regional and urban major earthquake risk by developing high-performance regional and urban seismic disaster monitoring, networking and observation technologies. In this project, demonstration applications are carried out to realize scientific, accurate and dynamic evaluation on the region and urban earthquake risk, and to provide key technical support for significantly improving China’s capability to deal with the earthquake disaster risk. After the two years research, the MEMS accelerometer samples have been designed and produced, and the optimal method for arranging the observation network, optimizing arrangement of accelerometers for typical structure and the improved multi-hop routing algorithm for data transmission have been presented. The convolutional neural network Damage-Net for identifying the visual damage of RC members was established, a strong tracking filter algorithm was adopted for effectively tracking the time-varying physical parameters of the building structural system, and the seismic resilience assessment method of buildings was developed. A data anomaly detection method based on computer vision was proposed. A bridge damage index model based on difference rate of the elastic-plastic energy dissipation and a bridge damage identification method based on convolutional neural network and recursive plots have been put forward. Benchmark model of bridge seismic damage monitoring and performance evaluation has been established. The building information extraction technology based on the remote sensing data, recovery function model for the structural performance of both the individual building and regional buildings, and the method to quantify the structural resilience have been proposed. The index system and dynamic risk model have been developed for evaluating the regional and urban seismic disaster risk subjected to major earthquakes. The framework of the internet of things based on earthquake disaster monitoring system and the bridge parametric earthquake risk assessment model taking multiple damage states into account have been proposed. Earthquake disaster simulation system for buildings has been developed. The schematic design for the seismic monitoring system of the demonstration building has been preliminarily completed. The construction of the seismic monitoring network of the demonstration bridge has been accomplished. The multivariate information of Sanhe city has been collected and its database has been constructed. The earthquake disaster monitoring network has been preliminarily designed for the Sanhe region.
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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 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 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 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 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 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 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
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