基于压缩感知的地铁安全监测数据重构与施工安全风险评估

项目名称： 基于压缩感知的地铁安全监测数据重构与施工安全风险评估

项目编号： No.71271098

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 余明晖

作者单位： 华中科技大学

项目金额： 58万元

中文摘要： 目前地铁施工安全监测主要以人工监测为主，其结果受到施工环境、天气条件、监测精度以及人为因素等的影响，存在缺失和失真现象。本研究针对单监测点的数据缺失问题，将现有监测数据视作对原始信号的压缩采样，采用一维信号压缩感知方法，对其进行滤波与恢复。包含多个监测点的安全监控区域的监测数据是一个具有时空分布特性的二维时变信号，采用信号本身稀疏和信号源稀疏两种思路对其进行重构，从而以统一的框架解决数据缺失和失真问题。由于压缩感知对具备稀疏特性的信号，只需远少于香农采样定理所要求的采样数目就能以很高的概率重建原始信号，可为安全监测采样频率的确定提供一个理论下限。通过采用基于最大信息系数的关联性分析方法，探索地铁施工安全影响因素之间的关系，解决贝叶斯网络的结构建模，结合统计分析得到的概率分布，建立施工安全状态的贝叶斯网络模型，将传统的人工判断转化为一个可计算的过程，实现施工安全监测数据与风险评估的有机结合。

中文关键词： 安全管理；地铁建设；不完全数据；压缩感知；风险评估

英文摘要： Safety management is playing an increasingly important role in Metro construction. But the current safety monitoring is mainly done by manual way in Metro construction, which makes the resulted data being incomplete. This proposal is going to use compressive sensing to solve this problem. The incomplete monitoring data of one monitoring point can be regarded as a compressive sampling of original signal, which can be reconstructed and filtered by 1-dimension compressive sensing. As for the monitoring field which contains many monitoring points, the monitoring data in this field with a 2-dimension time-varying property can be reconstructed based tow different assumptions: data sparsity and signal sources sparsity. So a unified data recovering framework of safety monitoring is established, which makes the common monitoring data analysis methods to be used freely on recovered data. Since compressive sensing can recover the original signal with much less data than that required by Shannon sampling theory, the research result of monitoring data recovery can be used to define a theoretical lower bound of sampling frequency of safety monitoring from the point of view of data recovery. So a more concrete safety monitoring regulation can be partly supported by this point. To assess the risk of Metro construction, a MIC (M

英文关键词： Safety Management；Metro Construction；Incomplete Data；Compressive Sensing；Risk Assement