弹性位移要求：系统参数的不确定性与地型数据的随机性-----外文翻译.doc
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弹性位移要求：系统参数的不确定性与地型数据的随机性-----外文翻译,摘要：本文的主要内容是：量 化成比例的源于(1)广泛承认记录的变化性，(2)内在随机性的系统参数，弹性的总位移比率模式主导结构相当名义上决定横向强度。随机系统参数处理均为：系统正常独立考虑横向屈服强度和系统粘性阻尼比。 monte carlo模拟技术是一套选自从20规模地震数据中总结出来被广泛用来取代sdof系统的数据...
内容介绍
此文档由会员 wanli1988go 发布
摘要：本文的主要内容是：量 化成比例的源于(1)广泛承认记录的变化性，(2)内在随机性的系统参数，弹性的总位移比率模式主导结构相当名义上决定横向强度。随机系统参数处理均为：系统正常独立考虑横向屈服强度和系统粘性阻尼比。 Monte Carlo模拟技术是一套选自从20规模地震数据中总结出来被广泛用来取代SDOF系统的数据模拟系统。各向主要倾向的措施是，变化性的分散系数，被认为是这位移的数率。被普遍认为，分散的数率在位移比率的准则下被认为随机性的系统参数要远小于人为记录数据时的多变性。估计这种被报道的复表面重力波的分解的以后很有可能实施于性能抗震设计新兴概率和评价方法。据还表明，在所产生的分散位移比率的可变性参数低于本身内在分散系统参数只有在极少数的情况或短时间内发生。
1 介绍
H. Rahamia, A. Kavehb,_, Y. Gholipoura
a Engineering Optimization Research Group, University of Tehran, Tehran, Iran
b Centre for Excellence for Fundamental Studies in Structural Engineering, Iran Universityof Science and Technology, Narmak, Tehran-16, Iran
Received 25 April 2007; received in revised form 1 January 2008; accepted 15 January 2008
Available online 10 March 2008
1. Introduction
For size/geometry optimization of structures with fixed topology, it becomes necessary to optimize structural crosssections and geometry simultaneously. For such optimization,usually large numbers of design variables will be encountered consisting of cross-sectional areas and nodal coordinates, thus resulting in design spaces with large dimensions. Selecting the cross-sectional areas from a list of profiles leads to a discrete design space, and due to the constraints on member stresses, buckling stresses, and nodal displacements, the possibility of being trapped in a local optimum increases.Goldberg is one of the pioneers in developing the Genetic algorithm [1]. Early papers on structural optimization using GA are due to Goldberg and Samtani [2], Jenkins [3], Adeli and Cheng [4] and Rajeev and Krishnamoorthy [5]. Many others
have published papers improving the results and increasing the speed of GA in the last decade.In the process of optimizing the geometry (shape) of a structure by the
1 介绍
H. Rahamia, A. Kavehb,_, Y. Gholipoura
a Engineering Optimization Research Group, University of Tehran, Tehran, Iran
b Centre for Excellence for Fundamental Studies in Structural Engineering, Iran Universityof Science and Technology, Narmak, Tehran-16, Iran
Received 25 April 2007; received in revised form 1 January 2008; accepted 15 January 2008
Available online 10 March 2008
1. Introduction
For size/geometry optimization of structures with fixed topology, it becomes necessary to optimize structural crosssections and geometry simultaneously. For such optimization,usually large numbers of design variables will be encountered consisting of cross-sectional areas and nodal coordinates, thus resulting in design spaces with large dimensions. Selecting the cross-sectional areas from a list of profiles leads to a discrete design space, and due to the constraints on member stresses, buckling stresses, and nodal displacements, the possibility of being trapped in a local optimum increases.Goldberg is one of the pioneers in developing the Genetic algorithm [1]. Early papers on structural optimization using GA are due to Goldberg and Samtani [2], Jenkins [3], Adeli and Cheng [4] and Rajeev and Krishnamoorthy [5]. Many others
have published papers improving the results and increasing the speed of GA in the last decade.In the process of optimizing the geometry (shape) of a structure by the
