Digital Representation of Structural Response for the Reliability Assessment of Complex Systems

复杂系统可靠性评估结构响应的数字表示

• 批准号: 0218594
• 负责人: michel ghosn
• 金额: --
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218594&HistoricalAwards=false
• 关键词: Digital; Representation; Structural; Response; Reliability

DIGITAL REPRESENTATION OF STRUCTURAL RESPONSE FOR THE RELIABILITY ASSESSMENT OF COMPLEX SYSTEMSABSTRACTCurrent research efforts in structural engineering are geared toward the development of performance based design and safety evaluation criteria that take into consideration the various uncertainties in estimating system behavior and future loading conditions. System reliability methods provide the means to address these important points. However, most existing analytical reliability techniques have one or more limitations in their ablity to: a) accurately model structural behavior at high loads, b) consider different performance criteria, c) identify multiple equally important failure modes, and d) account for load combinations. The application of simulation techniques in conjunction with general purpose finite element packages provides methods with a strong potential for resolving many of these outstanding issues. The purpose of this research is then to develop a simulation-based method for the reliability assessment of structural systems, which would realistically model their behavior at high loads, be implementable in practical situations, and provide accurate solutions for complex structures using efficient algorithms. The first tool required to perform a simulation-based reliability analysis of a structural system consists of an accurate and efficient nonlinear analysis program capable of modeling the behavior of the structure for a specific (deterministic) set of conditions. The second tool is a systematic search algorithm that can identify probabilistically dominant failure modes accounting for the randomness of loads and material properties. Closed-form solutions for the response of complex nonlinear structures are difficult to obtain and only a digital representation of their behavior is possible through the application of the finite element method. Point estimates of the response under different load intensities and material properties are usually obtained from variations on the Newton-Raphson algorithm. These point estimates may often misidentify the ultimate capacity and may not accurately model the softening part of the loading curve due to the accumulation of numerical errors and because of the properties of the stiffness matrix in these ranges. In this study, the Singular Value Decomposition, SVD, method in combination with the Lanczos algorithm will be used to accurately trace the response of a structure at high loads. The efficiency, robustness, and stability of the proposed method will be demonstrated. Due to the random nature of the problem, the safety assessment of a structure can only be established using reliability techniques. Since the behavior of a structure with several failure modes is best represented in digital form, modern heuristic techniques may provide the most appropriate tools to assess its reliability. In particular, Genetic Algorithms, GA, have been shown to provide robust techniques for the reliability analysis of structures with multiple failure modes but may be inefficient due to the shotgun search strategy that they are based upon. To improve the efficiency of GA, a filtration operator will be introduced based on the principle of genetic elitism. The modified GA will provide an efficient method to estimate the reliability of complex structures, as well as identify its dominant failure modes and controlling random variables. This project will introduce advanced tools of computational mathematics into the field of structural mechanics. The study will stress the application of the proposed methods for the simulation based design of civil engineering structures although they will be applicable to fields as varied as electronic circuit design and Micro-Electro-Mechanical-Systems. Training of students in the subjects of matrix computational methods, artificial intelligence, and statistical computing will be a primary goal. Such training will provide future generations of structural engineers with the well-rounded education needed to make decisions and provide solutions to real life complex problems under uncertainty.

摘要当前结构工程研究的重点是基于性能的设计和安全评估准则的发展，这些准则考虑了在估计系统行为和未来荷载条件时的各种不确定性。系统可靠性方法提供了解决这些重要问题的方法。然而，大多数现有的分析可靠性技术在以下方面存在一个或多个限制：a)准确模拟高载荷下的结构行为，b)考虑不同的性能标准，c)识别多个同等重要的失效模式，以及d)考虑载荷组合。模拟技术与通用有限元软件包的结合应用，为解决许多这些突出问题提供了强大的潜力。本研究的目的是开发一种基于仿真的结构系统可靠性评估方法，该方法将真实地模拟其在高载荷下的行为，可在实际情况下实现，并使用高效的算法为复杂结构提供准确的解决方案。对结构系统进行基于仿真的可靠性分析所需的第一个工具包括一个准确有效的非线性分析程序，该程序能够对特定（确定性）条件下的结构行为进行建模。第二个工具是一个系统搜索算法，它可以识别概率上占主导地位的失效模式，考虑到载荷和材料特性的随机性。复杂非线性结构响应的封闭解很难得到，只能通过应用有限元方法来表示其行为。在不同载荷强度和材料性能下的响应点估计通常由牛顿-拉夫森算法的变体获得。由于数值误差的累积和这些范围内刚度矩阵的特性，这些点估计可能经常错误地识别极限承载力，并且可能无法准确地模拟加载曲线的软化部分。在本研究中，奇异值分解（SVD）方法将与Lanczos算法相结合，用于精确跟踪结构在高荷载下的响应。本文将证明该方法的有效性、鲁棒性和稳定性。由于问题的随机性，结构的安全评估只能采用可靠度技术来建立。由于具有多种失效模式的结构的行为最好以数字形式表示，现代启发式技术可能提供最合适的工具来评估其可靠性。特别是，遗传算法（GA）已被证明为具有多种失效模式的结构的可靠性分析提供了强大的技术，但由于它们所基于的散弹枪搜索策略可能效率低下。为了提高遗传算法的效率，将引入一种基于遗传精英原理的过滤算子。改进的遗传算法将为复杂结构的可靠度估计、主要失效模式识别和控制随机变量提供一种有效的方法。这个项目将把先进的计算数学工具引入结构力学领域。本研究将着重于土木工程结构模拟设计中所提出方法的应用，尽管它们将适用于电子电路设计和微机电系统等各种领域。在矩阵计算方法、人工智能和统计计算方面训练学生将是一个主要目标。这样的培训将为未来几代结构工程师提供全面的教育，使他们能够在不确定的情况下做出决策，并为现实生活中的复杂问题提供解决方案。