地下结构随机荷载反演与可靠性分析研究
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摘要
地下空间结构诸如地铁隧道、共同沟等都是城市生命线工程的重要组成部分,如何保证大型地下工程结构的安全性与功能运行要求,已成为城市地下工程防灾减灾研究中的一个重要课题。在这一背景下,地下结构的安全性监测与预警变得越来越重要。
本文分别对地下结构的安全性监测、荷载反演、可靠性分析作了相关研究,由于传统监测技术手段的不足,进行了新型传感器的应用研究:包括光纤光栅传感技术、应变膜监测技术、无线网络传感技术。它们可以实现大规模、分布式的监测,为现场监测的手段开辟一条新的道路。
针对地下结构荷载分布的复杂性,提出了用样条插值函数曲线来近似代替地下结构的实际荷载曲线,进行结构荷载反演的方法。讨论了遗传算法在优化反演当中的应用。相比于传统的优化算法,遗传算法具有群体搜索的功能,易于收敛到全局最优解或近似全局最优解。提出了基于概率密度演化的随机荷载反演方法,概率密度演化方法揭示了随机系统样本点概率信息之间的本质关系,从而,不仅实现了对客观系统的正确反映,也大大降低了问题求解的难度和工作量。利用所发展的方法,不仅可以获得反演荷载的统计特征值,同时可以得到地下结构荷载的概率密度函数曲面。
发展了基于概率密度演化思想的地下结构可靠性分析方法。在此方法中,通过求解概率密度方程,可以得到响应量的概率密度函数曲线,相比于其它随机分析方法,具有精确解答性,而且在计算效率上也得到大大提高。介绍了基于等价极值事件的结构体系可靠性分析方法,并将等价极值事件的基本思想推广应用到复杂失效准则下地下结构的可靠性分析之中。研究表明:采用概率密度演化分析,可以对地下结构可靠度给出较为准确的评价。同时,对前述方法实现了程序用户界面化。
最后,关于进一步工作的方向进行了简要的讨论。
Underground space structures such as metro, utility tunnel, etc., are important components of a city's lifeline system. So ensuring the safety and functional performance of large underground structures has already been an important subject of researches on disaster prevention and mitigation for underground engineering of cities. In this context, the safety monitoring and pre-warning of underground structure should be put more attention on.
In this thesis, safety monitoring, load identification and reliability analysis of underground structures are systematically studied in turn. Firstly, for the deficiency of traditional monitoring technologies, new types of sensors, e.g. fiber Bragg grating sensor, diaphragm pressure sensor, wireless network sensor, etc., are investigated in detail. Since all of the above mentioned technologies can realize large-scale and distributed monitoring, so a new path of in-situ monitoring can be created with their help.
Secondly, spline function approximation of actual complex distribution of loads on underground structures is introduced and the loads are identified with this approach as basis. Then the application of Genetic Algorithms (GA) to optimal inverse analysis is discussed. Compared with classical optimal algorithms, the colony searching characteristics of GA helps to find the global optimal solution more easily. Moreover, the stochastic load identification approach based on probability density evolution method (PDEM) is proposed. The recently developed PDEM can reveal the substantial probability information among sampling points in a stochastic system, so it can not only truly reflect the objective system, but also significantly lower the difficulty and work of problem solving. Utilizing this stochastic load identification approach, the statistical characteristic value and the probability density function of the identified loads can be obtained at the same time.
Thirdly, the PDEM based reliability analysis of underground structures is developed. Using this method to calculate the probability density surface of structure response is more accurate and more efficient than using traditional stochastic analysis methods. Furthermore, the system reliability analysis of structures based on equivalent extreme value event is introduced and applied to designs of underground structures under complex failure criteria. The results clearly prove the validity of the proposed method. Also a human-computer interaction interface of the load identification and stochastic reliability analysis codes is fulfilled for possible future users.
Finally, problems need further studies are discussed.
本文分别对地下结构的安全性监测、荷载反演、可靠性分析作了相关研究,由于传统监测技术手段的不足,进行了新型传感器的应用研究:包括光纤光栅传感技术、应变膜监测技术、无线网络传感技术。它们可以实现大规模、分布式的监测,为现场监测的手段开辟一条新的道路。
针对地下结构荷载分布的复杂性,提出了用样条插值函数曲线来近似代替地下结构的实际荷载曲线,进行结构荷载反演的方法。讨论了遗传算法在优化反演当中的应用。相比于传统的优化算法,遗传算法具有群体搜索的功能,易于收敛到全局最优解或近似全局最优解。提出了基于概率密度演化的随机荷载反演方法,概率密度演化方法揭示了随机系统样本点概率信息之间的本质关系,从而,不仅实现了对客观系统的正确反映,也大大降低了问题求解的难度和工作量。利用所发展的方法,不仅可以获得反演荷载的统计特征值,同时可以得到地下结构荷载的概率密度函数曲面。
发展了基于概率密度演化思想的地下结构可靠性分析方法。在此方法中,通过求解概率密度方程,可以得到响应量的概率密度函数曲线,相比于其它随机分析方法,具有精确解答性,而且在计算效率上也得到大大提高。介绍了基于等价极值事件的结构体系可靠性分析方法,并将等价极值事件的基本思想推广应用到复杂失效准则下地下结构的可靠性分析之中。研究表明:采用概率密度演化分析,可以对地下结构可靠度给出较为准确的评价。同时,对前述方法实现了程序用户界面化。
最后,关于进一步工作的方向进行了简要的讨论。
Underground space structures such as metro, utility tunnel, etc., are important components of a city's lifeline system. So ensuring the safety and functional performance of large underground structures has already been an important subject of researches on disaster prevention and mitigation for underground engineering of cities. In this context, the safety monitoring and pre-warning of underground structure should be put more attention on.
In this thesis, safety monitoring, load identification and reliability analysis of underground structures are systematically studied in turn. Firstly, for the deficiency of traditional monitoring technologies, new types of sensors, e.g. fiber Bragg grating sensor, diaphragm pressure sensor, wireless network sensor, etc., are investigated in detail. Since all of the above mentioned technologies can realize large-scale and distributed monitoring, so a new path of in-situ monitoring can be created with their help.
Secondly, spline function approximation of actual complex distribution of loads on underground structures is introduced and the loads are identified with this approach as basis. Then the application of Genetic Algorithms (GA) to optimal inverse analysis is discussed. Compared with classical optimal algorithms, the colony searching characteristics of GA helps to find the global optimal solution more easily. Moreover, the stochastic load identification approach based on probability density evolution method (PDEM) is proposed. The recently developed PDEM can reveal the substantial probability information among sampling points in a stochastic system, so it can not only truly reflect the objective system, but also significantly lower the difficulty and work of problem solving. Utilizing this stochastic load identification approach, the statistical characteristic value and the probability density function of the identified loads can be obtained at the same time.
Thirdly, the PDEM based reliability analysis of underground structures is developed. Using this method to calculate the probability density surface of structure response is more accurate and more efficient than using traditional stochastic analysis methods. Furthermore, the system reliability analysis of structures based on equivalent extreme value event is introduced and applied to designs of underground structures under complex failure criteria. The results clearly prove the validity of the proposed method. Also a human-computer interaction interface of the load identification and stochastic reliability analysis codes is fulfilled for possible future users.
Finally, problems need further studies are discussed.
引文
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