Soft computing techniques in parameter identification and probabilistic seismic analysis of structures

Y. Tsompanakis; N.D. Lagaros; G.E. Stavroulakis

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Soft computing techniques in parameter identification and probabilistic seismic analysis of structures

机译：结构参数识别和概率地震分析中的软计算技术

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The objective of this paper is to investigate the efficiency of soft computing methods, in particular methodologies based on neural networks, when incorporated into the solution of computationally intensive engineering problems. Two types of applications have been considered, namely parameter (flaw) identification and probabilistic seismic analysis of structures. Artificial neural networks (ANNs) based metamodels are used in order to replace the time-consuming repeated structural analyses. The back-propagation algorithm is employed for training the ANN, using data derived from selected analyses. The trained ANN is then used to predict the values of the necessary data. The numerical tests demonstrate the computational advantages of the proposed methodologies.

机译：本文的目的是研究将软计算方法（特别是基于神经网络的方法）并入计算密集型工程问题的解决方案时的效率。已经考虑了两种类型的应用，即参数（缺陷）识别和结构的概率地震分析。为了取代耗时的重复结构分析，使用了基于人工神经网络（ANN）的元模型。反向传播算法用于训练ANN，使用从选定分析中得出的数据。然后，将训练有素的人工神经网络用于预测必要数据的值。数值测试证明了所提出方法的计算优势。

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来源
《Advances in Engineering Software》 |2008年第7期|p.612-624|共13页
作者
Y. Tsompanakis; N.D. Lagaros; G.E. Stavroulakis;
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作者单位

Department of Applied Sciences, Technical University of Crete, University Campus, Chania 73100, Greece;

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原文格式 PDF
正文语种 eng
中图分类计算机软件;
关键词
artificial neural networks; simulation; inverse problems; probabilistic seismic analysis; structural identification;

机译：人工神经网络;仿真;反问题;概率地震分析;结构辨识;

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Soft computing techniques in parameter identification and probabilistic seismic analysis of structures

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