基于贝叶斯网络的一种新型的结构选型方法研究(Ⅱ)
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摘要
最佳高层建筑的结构型式的选取与确定是结构工程初步设计的重要内容,然而,结构型式的选择受到多种因素的影响与制约,是一个具有高度白噪声、非线性的复杂问题。针对影响结构型式选择的各种因素,首先分别构建了影响因素矩阵向量与结构型式矩阵向量,引入灰色关联理论对各影响因素的重要程度进行了从定性到定量的分析。根据获取的影响因子的重要性程度,确定了简化的关键影响因素矩阵。在此基础上,引入贝叶斯网络理论,建立结构选型的贝叶斯网络预测模型。以3种情况为实例,分别验证了该模型的有效性与计算精度。试验结果表明,与其他方法相比,该模型简单实用,且具有较高的预测精度,为结构选型提供了一种新的预测方法。
The optimum structure system selection is the important task in the preliminary design of structural engineering.However,the selection of structure system was influenced by many factors,and is a highly white noise and non-linear complex problems.Considered these various factors,first of all the influencing factors vector matrix and structure system vector matrix were constructed.The grey relational theory was introduced to analysis the importance of influencing factors from qualitative to quantitative.According to obtained the importance degree of the influencing factors,the key influencing factors matrix was constructed.On this basis,the Bayesian networks theory was introduced to set up the structure system selection prediction model.As an example to the three situations,the validity of the model and calculation accuracy had been verified.The calculated results showed that compared with other methods,the model is simple and practical,and had a higher prediction accuracy for the structure of selection,also provided a new prediction method.
The optimum structure system selection is the important task in the preliminary design of structural engineering.However,the selection of structure system was influenced by many factors,and is a highly white noise and non-linear complex problems.Considered these various factors,first of all the influencing factors vector matrix and structure system vector matrix were constructed.The grey relational theory was introduced to analysis the importance of influencing factors from qualitative to quantitative.According to obtained the importance degree of the influencing factors,the key influencing factors matrix was constructed.On this basis,the Bayesian networks theory was introduced to set up the structure system selection prediction model.As an example to the three situations,the validity of the model and calculation accuracy had been verified.The calculated results showed that compared with other methods,the model is simple and practical,and had a higher prediction accuracy for the structure of selection,also provided a new prediction method.
引文
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[16]张世海,刘晓燕,涂庆,欧进萍.基于决策树的高层结构智能选型知识发现[J].哈尔滨工业大学学报,2005,(4):451-454.
[17]刘海卿,康珍珍,张丙军.基于模糊综合评判法的高层建筑结构体系优选[J].科学技术与工程,2006,(8):2495-2498.
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[19]张世海,欧进萍,王光远.基于实例基因库与遗传算法的高层结构智能选型知识发现[J].工程力学,2006,(1):87-92.
[2]Bailey S F,Smith I F C.Case-based preliminary building design[J].J Computing in Civ.Engrg.ASCE,1994,8(4):454-468.
[3]Soibelman L.The exploration of an integratedrepresentation for the conceptual design phase of structural design for tall buildings through distributed multi-reasoning algorithms[D].PhD thesis,Massachusetts Institute of Technology,Cambridge,Mass,1998.
[4]Lucio Soibelman,Feniosky Pena-Mora.Distributed Multi-Reason-ing Mechanism to Support Conceptual Structural Design[J].Jour-nal of Structural Engineering,2000,126(6):733-742.
[5]何广乾,林少培,等.高层建筑结构初步设计专家系统[R].国家自然科学基金重大项目,工程建设中的智能辅助决策应用研究,1991.
[6]刘西拉,李楚舒.基于神经网络的高层结构体系选择[J].建筑结构学报,1999,20(5):36-41.
[7]王光远.结构智能选型理论及应用[M].北京:中国建筑工业出版社,2003.
[8]王光远,张世海,刘晓燕,等.高层结构方案实例库系统及其在结构智能选型中应用[J].工程力学,2003,20(4):1-7.
[9]王光远,吕大刚,张世海.论结构选型的若干关键问题[J].哈尔滨建筑大学学报,2000,33(1):1-7.
[10]王光远,欧进萍,刘晓燕,张世海.高层建筑基础抗震选型评价指标与智能优选方法[J].地震工程与工程振动,2002,(4):28-35.
[11]陶忠,张耀春,韩林海,王光远.关于高层建筑结构选型设计的初步探讨[J].哈尔滨建筑大学学报,2000,33(1):20-25.
[12]欧进萍,刘晓燕.高层钢筋混凝土结构抗震选型的模糊专家系统[J].地震工程与工程振动,1997,(2):82-91.
[13]张世海,刘正保,张世忠.高层建筑结构选型需求分析[J].四川建筑科学研究,2004,(6):1-4.
[14]张世海,王力,欧进萍,王光远.高层建筑结构智能型式优化的现状[J].哈尔滨工业大学学报,2006,(2):194-198.
[15]张世海,刘晓燕,欧进萍.高层结构智能选型知识发现及方法比较[J].四川建筑科学研究,2005,(5):19-23.
[16]张世海,刘晓燕,涂庆,欧进萍.基于决策树的高层结构智能选型知识发现[J].哈尔滨工业大学学报,2005,(4):451-454.
[17]刘海卿,康珍珍,张丙军.基于模糊综合评判法的高层建筑结构体系优选[J].科学技术与工程,2006,(8):2495-2498.
[18]王经建,刘伯权.结构选型的模糊多属性决策方法[J].长安大学学报(自然科学版),2004,24(3):1-4.
[19]张世海,欧进萍,王光远.基于实例基因库与遗传算法的高层结构智能选型知识发现[J].工程力学,2006,(1):87-92.
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