Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics
Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics
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发表时间：
2010-06
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通讯作者：
T. Marwala
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作者：
T. Marwala
有限元模型（fem）被广泛用于理解各种系统的动态行为。FEM的更新使FEM能够更好地调整以反映测量数据，并且可以使用两种不同的统计框架：最大似然方法和贝叶斯方法。利用计算智能技术更新有限元模型将这两种策略应用于结构力学领域，这是一个对航空航天、土木和机械工程至关重要的领域。振动数据用于更新过程。在介绍之后，提出了一些用于促进更新过程的计算智能技术；它们包括：用于有限元实时更新的多层感知器神经网络；适应全局和局部优化模型需求的粒子群和遗传算法优化方法；模拟退火将方法纳入完善的统计基础；以及响应面方法和期望最大化算法，以演示如何以经济有效的方式进行有限元更新；并帮助管理计算复杂性。在此基础上，利用贝叶斯方法选择最合适的有限元更新，解决了传统有限元更新无法解决的问题。通过先验分布的公式，将工程判断系统地纳入有限元中。在整个文本中，案例研究，专门设计，以证明特殊原则包括在内。这有助于检验新方法在有限元修正中的可行性。利用计算智能技术对有限元模型更新进行了批判性分析，并基于这些发现，确定了新的研究方向，使其对结构动力学研究人员和使用有限元分析的实践工程师感兴趣。机械、航空航天和土木工程专业的研究生也会发现本文具有指导意义。
Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: multi-layer perceptron neural networks for real-time FEM updating; particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; simulated annealing to put the methodologies into a sound statistical basis; and response surface methods and expectation maximization algorithms to demonstrate how FEM updating can be performed in a cost-effective manner; and to help manage computational complexity. Based on these methods, the most appropriate updated FEM is selected using the Bayesian approach, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite elements systematically through the formulations of prior distributions. Throughout the text, case studies, specifically designed to demonstrate the special principles are included. These serve to test the viability of the new approaches in FEM updating. Finite Element Model Updating Using Computational Intelligence Techniques analyses the state of the art in FEM updating critically and based on these findings, identifies new research directions, making it of interest to researchers in strucural dynamics and practising engineers using FEMs. Graduate students of mechanical, aerospace and civil engineering will also find the text instructive.