基于验证荷载和监测数据的桥梁可靠性修正与贝叶斯预测
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摘要
结构健康监测是土木工程领域的当前热点研究方向，健康监测领域经历了两个阶段：第一阶段是传感器的安装以及数据的采集，此阶段现在已处于成熟阶段；第二阶段是健康监测数据的合理利用。结构健康监测系统在长期的运营中，积累了海量的数据，如何有效地分析这些数据，为桥梁结构的性能评估与预测提供科学依据，是健康监测领域的关键问题之一，也是当前迫切需要解决的问题之一。本文以贝叶斯修正和预测理论为基础，基于桥梁结构的验证荷载试验和健康监测数据，对桥梁结构构件及体系可靠性的修正与预测进行了系统的研究，主要内容包括：
(1)研究了基于验证荷载信息和抗力退化模型的桥梁结构构件可靠性修正方法。结合确定性与随机性的荷载历史信息，考虑结构抗力的退化模型，采用截尾分布法和Bayes方法，得到了验证荷载信息以及抗力退化模型对桥梁构件可靠性的影响规律。
(2)提出了基于贝叶斯动态线性模型的桥梁构件可靠性预测方法。采用健康监测数据，分别针对一次多项式回归模型、AR(1)模型和ARMA(1,1)模型，建立了相应的贝叶斯动态线性模型，研究了模型监控机制。考虑贝叶斯动态线性模型的多样性，建立了监测数据的混合贝叶斯动态线性模型；基于所建立的单一及混合贝叶斯动态线性模型，采用一次二阶矩方法，对桥梁构件可靠性进行了预测分析。
(3)提出了基于贝叶斯动态非线性模型的桥梁构件可靠性预测方法。采用健康监测数据，分别详细建立了基于二次多项式函数和三次多项式函数的贝叶斯动态非线性模型，并提出了两种近似处理贝叶斯动态非线性模型的方法：其一，通过泰勒级数展开技术，将贝叶斯动态非线性模型近似转化为贝叶斯动态线性模型，并建立了相对应的模型监控机制；其二，直接通过马尔科夫链蒙特卡洛模拟(MCMC)实现。基于所建立的贝叶斯动态非线性模型，结合一次二阶矩方法，对桥梁构件可靠性进行了预测分析。
(4)提出了基于混合高斯粒子滤波器的桥梁构件可靠性在线实时预测方法。建立了基于健康监测数据的动态模型，引入混合高斯粒子滤波器，基于粒子滤波方法和动态模型，对状态变量的分布参数和监测值的一步向前预测分布参数进行了预测。提出了混合高斯粒子滤波方法重采样技术，解决了粒子模拟退化的问题。结合一次二阶矩方法，对桥梁构件可靠性进行了预测分析。
(5)研究了基于验证荷载效应和健康监测信息的桥梁结构体系可靠性在线实时预测方法。采用MIDAS软件模拟构件验证荷载效应，通过构件验证荷载效应修正构件的应力限值（广义抗力）分布，建立了基于健康监测数据（荷载效应）的混合高斯粒子滤波器，基于修正的应力限值分布和荷载效应的混合高斯粒子滤波器，实现了桥梁构件的可靠性修正与预测，采用结构体系可靠度方法，实现了结构体系的可靠性预测。以天津富民桥（单塔空间索面自锚式悬索桥）为工程应用背景，验证了所提理论与方法的正确性和适用性。
Structural health monitoring is one of present research hotspots in the field of civil engineering. The research on structural health monitoring generally experiences two stages. The first stage, falling in the mature stage, is to install sensors on the structures and conduct much research on the data transition system, data acquisition technology, system integration technology and other aspects. The second stage is mainly the application of health monitoring information. Novel monitoring systems used in structural engineering contain sensors providing a large amount of monitored data. Proper handling of the continuously provided monitored data is one of the main difficulties in the field of structural health monitoring. In this dissertation, based on Bayesian updating and prediction theory, the structural inspection data (e.g. proof loads and proof load effects) and monitored data, systematic research on reliability updating and prediction of bridge members or bridge system was carry out.
The main research contents of this dissertation are described as follows:
This paper presents the reliability updating method based on proof loads and resistance degradation model of bridge members. With the truncated method and Bayesian method, the effects of proof loads, proof load effects and resistance degradation model on the reliability of bridge members are analyzed.
This paper presents the reliability prediction method of bridge members based on Bayesian dynamic linear model. With inspection data or monitored data, the1-order polynomial function, AR(1) model and ARMA(1,1) model are respectively adopted to build the corresponding Bayesian dynamic linear models (BDLM). And the model monitoring mechanism of BDLM is studied. Considering the diversity of BDLM, the combined BDLM is built based on the multiple BDLMs. Finally based on the single BDLM or combined BDLM, with first order second moment (FOSM) method, the reliability of structural member is predicted.
This paper presents the reliability prediction method of bridge members based on Bayesian dynamic nonlinear model (BDNM). Mainly with monitored data,2-order polynomial function and3-order polynomial function are respectively adopted to build the corresponding BDNM. The simulation processes of BDNM are handled with the following two methods. One method is to transform the built BDNM into approximate BDLM with Taylor series expansion technique, the monitoring mechanism of approximate BDLM is also studied. The other method is to directly simulate the processes with Markov Chain Monte Carlo (MCMC) method. Finally based on the built BDNM, with FOSM method, the reliability of structural members is predicted.
This paper presents the real-time on-line reliability prediction method of bridge members based on mixed Gaussian particle filter (MGPF). Firstly monitored data-based dynamic model is built, and then the MGPF is introduced. Based on the particle filter method and dynamic model, the distribution parameters of state variables and one-step prediction distribution are predicted. The MGPF solves the problem of particle simulation degeneracy through the resampling method. Finally with FOSM method, the reliability of structural members is predicted.
The paper presents the reliability updating and prediction method of bridge system based on proof load effects and monitored data. The proof load effects of structural members are obtained with MIDAS software. Firstly the distribution of stress threshold (genaralized resiatance distribution) is updated with proof load effects. Then the moinitored data(load effect data)-based mixed gaussian particle filter(MGPF) is built, and then based on the updated stress threshold distribution and load effect MGPF, the reliability updating and prediction of structural members is solved, finally with the system reliability method, the reliability of structural system is updated and predicted, and Tianjin Fumin bridge is provided to illustrate the feasibility and application of the given models and methods.
(1)研究了基于验证荷载信息和抗力退化模型的桥梁结构构件可靠性修正方法。结合确定性与随机性的荷载历史信息，考虑结构抗力的退化模型，采用截尾分布法和Bayes方法，得到了验证荷载信息以及抗力退化模型对桥梁构件可靠性的影响规律。
(2)提出了基于贝叶斯动态线性模型的桥梁构件可靠性预测方法。采用健康监测数据，分别针对一次多项式回归模型、AR(1)模型和ARMA(1,1)模型，建立了相应的贝叶斯动态线性模型，研究了模型监控机制。考虑贝叶斯动态线性模型的多样性，建立了监测数据的混合贝叶斯动态线性模型；基于所建立的单一及混合贝叶斯动态线性模型，采用一次二阶矩方法，对桥梁构件可靠性进行了预测分析。
(3)提出了基于贝叶斯动态非线性模型的桥梁构件可靠性预测方法。采用健康监测数据，分别详细建立了基于二次多项式函数和三次多项式函数的贝叶斯动态非线性模型，并提出了两种近似处理贝叶斯动态非线性模型的方法：其一，通过泰勒级数展开技术，将贝叶斯动态非线性模型近似转化为贝叶斯动态线性模型，并建立了相对应的模型监控机制；其二，直接通过马尔科夫链蒙特卡洛模拟(MCMC)实现。基于所建立的贝叶斯动态非线性模型，结合一次二阶矩方法，对桥梁构件可靠性进行了预测分析。
(4)提出了基于混合高斯粒子滤波器的桥梁构件可靠性在线实时预测方法。建立了基于健康监测数据的动态模型，引入混合高斯粒子滤波器，基于粒子滤波方法和动态模型，对状态变量的分布参数和监测值的一步向前预测分布参数进行了预测。提出了混合高斯粒子滤波方法重采样技术，解决了粒子模拟退化的问题。结合一次二阶矩方法，对桥梁构件可靠性进行了预测分析。
(5)研究了基于验证荷载效应和健康监测信息的桥梁结构体系可靠性在线实时预测方法。采用MIDAS软件模拟构件验证荷载效应，通过构件验证荷载效应修正构件的应力限值（广义抗力）分布，建立了基于健康监测数据（荷载效应）的混合高斯粒子滤波器，基于修正的应力限值分布和荷载效应的混合高斯粒子滤波器，实现了桥梁构件的可靠性修正与预测，采用结构体系可靠度方法，实现了结构体系的可靠性预测。以天津富民桥（单塔空间索面自锚式悬索桥）为工程应用背景，验证了所提理论与方法的正确性和适用性。
Structural health monitoring is one of present research hotspots in the field of civil engineering. The research on structural health monitoring generally experiences two stages. The first stage, falling in the mature stage, is to install sensors on the structures and conduct much research on the data transition system, data acquisition technology, system integration technology and other aspects. The second stage is mainly the application of health monitoring information. Novel monitoring systems used in structural engineering contain sensors providing a large amount of monitored data. Proper handling of the continuously provided monitored data is one of the main difficulties in the field of structural health monitoring. In this dissertation, based on Bayesian updating and prediction theory, the structural inspection data (e.g. proof loads and proof load effects) and monitored data, systematic research on reliability updating and prediction of bridge members or bridge system was carry out.
The main research contents of this dissertation are described as follows:
This paper presents the reliability updating method based on proof loads and resistance degradation model of bridge members. With the truncated method and Bayesian method, the effects of proof loads, proof load effects and resistance degradation model on the reliability of bridge members are analyzed.
This paper presents the reliability prediction method of bridge members based on Bayesian dynamic linear model. With inspection data or monitored data, the1-order polynomial function, AR(1) model and ARMA(1,1) model are respectively adopted to build the corresponding Bayesian dynamic linear models (BDLM). And the model monitoring mechanism of BDLM is studied. Considering the diversity of BDLM, the combined BDLM is built based on the multiple BDLMs. Finally based on the single BDLM or combined BDLM, with first order second moment (FOSM) method, the reliability of structural member is predicted.
This paper presents the reliability prediction method of bridge members based on Bayesian dynamic nonlinear model (BDNM). Mainly with monitored data,2-order polynomial function and3-order polynomial function are respectively adopted to build the corresponding BDNM. The simulation processes of BDNM are handled with the following two methods. One method is to transform the built BDNM into approximate BDLM with Taylor series expansion technique, the monitoring mechanism of approximate BDLM is also studied. The other method is to directly simulate the processes with Markov Chain Monte Carlo (MCMC) method. Finally based on the built BDNM, with FOSM method, the reliability of structural members is predicted.
This paper presents the real-time on-line reliability prediction method of bridge members based on mixed Gaussian particle filter (MGPF). Firstly monitored data-based dynamic model is built, and then the MGPF is introduced. Based on the particle filter method and dynamic model, the distribution parameters of state variables and one-step prediction distribution are predicted. The MGPF solves the problem of particle simulation degeneracy through the resampling method. Finally with FOSM method, the reliability of structural members is predicted.
The paper presents the reliability updating and prediction method of bridge system based on proof load effects and monitored data. The proof load effects of structural members are obtained with MIDAS software. Firstly the distribution of stress threshold (genaralized resiatance distribution) is updated with proof load effects. Then the moinitored data(load effect data)-based mixed gaussian particle filter(MGPF) is built, and then based on the updated stress threshold distribution and load effect MGPF, the reliability updating and prediction of structural members is solved, finally with the system reliability method, the reliability of structural system is updated and predicted, and Tianjin Fumin bridge is provided to illustrate the feasibility and application of the given models and methods.
引文
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