...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Earthquake Engineering & Structural Dynamics >Multi-objective framework for structural model identification
【24h】

Multi-objective framework for structural model identification

机译：结构模型识别的多目标框架

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Structural identification based on measured dynamic data is formulated in a multi-objective context that allows the simultaneous minimization of the various objectives related to the fit between measured and model predicted data. Thus, the need for using arbitrary weighting factors for weighting the relative importance of each objective is eliminated. For conflicting objectives there is no longer one solution but rather a whole set of acceptable compromise solutions, known as Pareto solutions, which are optimal in the sense that they cannot be improved in any objective without causing degradation in at least one other objective. The strength Pareto evolutionary algorithm is used to estimate the set of Pareto optimal structural models and the corresponding Pareto front. The multi-objective structural identification framework is presented for linear models and measured data consisting of modal frequencies and modeshapes. The applicability of the framework to non-linear model identification is also addressed. The framework is illustrated by identifying the Pareto optimal models for a scaled laboratory building structure using experimentally obtained modal data. A large variability in the Pareto optimal structural models is observed. It is demonstrated that the structural reliability predictions computed from the identified Pareto optimal models may vary considerably. The proposed methodology can be used to explore the variability in such predictions and provide updated structural safety assessments, taking into consideration all Pareto structural models that are consistent with the measured data.
机译：在多目标环境中制定了基于测得动态数据的结构识别，可同时最小化与测得数据和模型预测数据之间的拟合相关的各种目标。因此，消除了使用任意加权因子来加权每个目标的相对重要性的需要。对于冲突的目标，不再有一个解决方案，而是一整套可接受的折衷解决方案，称为Pareto解决方案，它们是最佳的，因为它们不能在任何目标上得到改善而不会导致至少另一个目标的退化。强度帕累托进化算法用于估计一组帕累托最优结构模型和相应的帕累托前沿。提出了用于线性模型和由模态频率和模态组成的测量数据的多目标结构识别框架。还讨论了该框架对非线性模型识别的适用性。通过使用实验获得的模态数据为规模化的实验室建筑结构识别帕累托最优模型来说明该框架。在帕累托最优结构模型中观察到很大的差异。结果表明，从确定的帕累托最优模型计算得出的结构可靠性预测可能会有很大不同。考虑到所有与测量数据一致的帕累托结构模型，所提出的方法可用于探索此类预测的可变性并提供更新的结构安全性评估。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号