项目名称: 时变结构在非平稳噪声下的实时结构参数识别、噪声参数识别与模型选择
项目编号: No.51508201
项目类型: 青年科学基金项目
立项/批准年度: 2016
项目学科: 建筑科学
项目作者: 慕何青
作者单位: 华南理工大学
项目金额: 17万元
中文摘要: 结构健康监测已经成为土木工程中结构安全与防灾减灾研究的重要方向。实时结构识别算法通过利用实时采集的数据对结构状态做出实时评估,它既适用于长期运营过程中的全寿命实时监测,也适用于结构受到灾害侵袭时的实时结构性能诊断,因此,它是结构健康监测的重点研究方向之一。然而,许多实时结构识别算法在实际应用过程中受限于三类问题:1)运营环境与载荷的非平稳性;2)测量误差与干扰;3)结构的退化与变化。本项目以上述三类问题为切入点,整合贝叶斯理论与扩展卡尔曼滤波器,揭示了结构健康监测中噪声非平稳性、参数不确定性与模型不确定性三个因素之间的相互关系,最终提出实时多模型结构识别算法,为实现时变结构在非平稳噪声下的鲁棒实时识别提供全新的研究思路和方法,继而为结构全寿命实时监测与结构受到灾害侵袭时的实时结构性能诊断提供理论基础和技术支撑。
中文关键词: 贝叶斯方法;不确定性分析;鲁棒参数识别;模型选择;卡尔曼滤波器
英文摘要: Structural health monitoring is one of the important issues in civil engineering. One of the main streams in structural identification is to estimate structural state and performance based on measured data in the real-time manner. However, most of the existing monitoring algorithms are limited by the following issues: 1) Operational condition and noise nonstationarity; 2) Measurement error and disturbance; 3) Structural modelling error. This project attempts to tackle all these three issues simultaneously by Bayesian probabilistic inference. By embedding the novel noise parametric identification and model class selection component into the extended Kalman filter, a novel robust multi-model extended Kalman filter is proposed and applied for identification of time-varying structure under nonstationary noise, providing a theoretical basis and technical support for monitoring of large scale structure under long-term monitoring and/or subjected to natural disaster.
英文关键词: Bayesian method;uncertainty quantification;robust parametric identification;model class selection;Kalman filter