基于压缩感知隧道结构健康监测与可靠性评价

为了弥补隧道监测数据缺失导致信息丢失的不足,从而能够更好地评价地铁隧道结构健康状况,基于压缩感知理论对健康监测数据进行处理,通过对运营地铁的结构健康监测数据进行压缩和重构,减少对原始监测数据的传输和存储压力,以提高获取的地铁健康相关数据的信息完备与可靠性,为隧道结构健康安全评价提供数据基础。在此基础上,构建运营地铁隧道安全风险指标评价体系,在获取完备数据的基础上,基于不确定性视角,采用云推理综合评估运营地铁隧道的可靠程度,以及时掌握运营地铁的安全状态,并找到影响其可靠性的关键风险,从而制定相应的管控措施。最后以武汉地铁2号线长汉区间监测点的累计沉降作为数据样本进行实例分析,结果显示压缩感知能使监测数据重构以获得完备数据,再利用逐级云推理得到安全风险等级云图与实际情况相符。

Health Monitoring and Reliability Evaluation of Tunnel Structure Based on Compressed Sensing Theory

In order to make up for the information loss caused by the lack of tunnel monitoring data and better evaluate the structural health of the subway tunnel, this paper deals with the health monitoring data based on the compressed sensing theory, and compresses and reconstructs the structural health monitoring data of the operating subway to reduce the transmission and storage pressure of the original monitoring data. Thus, the information integrity and reliability of the acquired subway health related data are improved and data foundation is provided for tunnel structure health and safety evaluation. On this basis, a safety risk index evaluation system is established for operating subway tunnels. Based on the acquisition of complete data and the uncertainty perspective, a series of methods using cloud models are used to comprehensively evaluate the reliability of operating subway tunnels and grasp the safety status of operating subways in time to identify key risks that affect their reliability and formulate appropriate control measures. Finally, the cumulative settlement of the Changhan interval monitoring point of Wuhan Metro Line 2 is taken as a data sample for case analysis. The results show that the compressed sensing can reconstruct the monitoring data to obtain complete data, and then the step-by-step cloud reasoning is employed to get the security risk level cloud map to match the actual situation.