彈性位移要求：系統(tǒng)參數(shù)的不確定性與地型數(shù)據(jù)的隨機(jī)性-----外文翻譯.doc
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彈性位移要求：系統(tǒng)參數(shù)的不確定性與地型數(shù)據(jù)的隨機(jī)性-----外文翻譯,摘要：本文的主要內(nèi)容是：量 化成比例的源于(1)廣泛承認(rèn)記錄的變化性，(2)內(nèi)在隨機(jī)性的系統(tǒng)參數(shù)，彈性的總位移比率模式主導(dǎo)結(jié)構(gòu)相當(dāng)名義上決定橫向強(qiáng)度。隨機(jī)系統(tǒng)參數(shù)處理均為：系統(tǒng)正常獨立考慮橫向屈服強(qiáng)度和系統(tǒng)粘性阻尼比。 monte carlo模擬技術(shù)是一套選自從20規(guī)模地震數(shù)據(jù)中總結(jié)出來被廣泛用來取代sdof系統(tǒng)的數(shù)據(jù)...
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此文檔由會員 wanli1988go 發(fā)布
摘要：本文的主要內(nèi)容是：量 化成比例的源于(1)廣泛承認(rèn)記錄的變化性，(2)內(nèi)在隨機(jī)性的系統(tǒng)參數(shù)，彈性的總位移比率模式主導(dǎo)結(jié)構(gòu)相當(dāng)名義上決定橫向強(qiáng)度。隨機(jī)系統(tǒng)參數(shù)處理均為：系統(tǒng)正常獨立考慮橫向屈服強(qiáng)度和系統(tǒng)粘性阻尼比。 Monte Carlo模擬技術(shù)是一套選自從20規(guī)模地震數(shù)據(jù)中總結(jié)出來被廣泛用來取代SDOF系統(tǒng)的數(shù)據(jù)模擬系統(tǒng)。各向主要傾向的措施是，變化性的分散系數(shù)，被認(rèn)為是這位移的數(shù)率。被普遍認(rèn)為，分散的數(shù)率在位移比率的準(zhǔn)則下被認(rèn)為隨機(jī)性的系統(tǒng)參數(shù)要遠(yuǎn)小于人為記錄數(shù)據(jù)時的多變性。估計這種被報道的復(fù)表面重力波的分解的以后很有可能實施于性能抗震設(shè)計新興概率和評價方法。據(jù)還表明，在所產(chǎn)生的分散位移比率的可變性參數(shù)低于本身內(nèi)在分散系統(tǒng)參數(shù)只有在極少數(shù)的情況或短時間內(nèi)發(fā)生。
1 介紹
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 介紹
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
