基于一种新的考虑周期的shm数据同化方法的桥梁性能预测

IF 5.4 2区 工程技术
Guang Qu, Limin Sun, Hongwei Huang
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引用次数: 0

摘要

现代桥梁由越来越多的传感器网络监控,这些传感器网络产生大量数据,用于桥梁性能预测。利用监测数据对既有桥梁时变可靠度进行合理、动态预测,已成为结构健康监测中亟待解决的问题之一。本研究将结构应力随时间的动态测量作为时间序列,提出了一种基于具有周期性的极端应力数据的可靠性预测数据同化方法。为此,本文的目标是提出:(a)基于具有循环性的极端应力数据的基于高斯混合模型的贝叶斯循环动态线性模型(ggm - bcdlm)和(b)基于一阶二阶矩(FOSM)方法的ggm - bcdlm和SHM数据相结合的动态可靠性预测方法。以一座在役桥梁为例,说明了该方法的应用和可行性。最后,与其他具有循环性的极端应力数据预测方法相比,验证了所提模型的有效性和预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bridge Performance Prediction Based on a Novel SHM-Data Assimilation Approach considering Cyclicity
Modern bridges are monitored by an increasing network of sensors that produce massive data for bridge performance prediction. Reasonably and dynamically predicting with monitored data for the time-variant reliability of the existing bridges has become one of the urgent problems in structural health monitoring (SHM). This study, taking the dynamic measure of structural stress over time as a time series, proposes a data assimilation approach to predicting reliability based on extreme stress data with cyclicity. To this aim, the objectives of this article are to present the following: (a) a Gaussian mixture model-based Bayesian cyclical dynamic linear model (GMM-BCDLM) based on extreme stress data with cyclicity and (b) a dynamic reliability prediction method in the combination of GMM-BCDLM and SHM data via first-order second-moment (FOSM) method. An in-service bridge for providing real-time monitored stress data is applied to illustrate the application and feasibility of the proposed method. Then, the effectiveness and prediction precision of the proposed models are proved to be superior compared to other prediction approaches to extreme stress data with cyclicity.
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring Engineering-Building and Construction
自引率
13.00%
发文量
0
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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