| 基因表达式编程及其在混凝土徐变分析中应用 |
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| 引用本文： | 何鲜峰,顾冲时,谷艳昌,陈睿.基因表达式编程及其在混凝土徐变分析中应用[J].土木建筑与环境工程,2008,30(4):82-86. |
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| 作者姓名： | 何鲜峰 顾冲时 谷艳昌 陈睿 |
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| 作者单位： | 河海大学水电学院,南京,210098;黄河水利科学研究院,郑州,450003;水利部堤防安全与病害防治工程技术研究中心,郑州,450003;河海大学水电学院,南京,210098;水资源高效利用与工程安全国家工程研究中心,南京,210098 |
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| 基金项目： | 国家自然科学基金;基础研究发展计划项目;江苏省高等学校研究生科技创新项目;国家科技支撑计划;中国水电工程顾问集团公司科技项目;黄科院院所长基金项目 |
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| 摘 要： | 混凝土徐变分析在混凝土结构应力计算和变形分析中有着重要意义。由于影响混凝土徐变的因素较多，徐变函数呈现高度非线性，传统回归方法难以取得令人满意结果。基因表达式程序设计方法，吸取了遗传算法和基因编程两者的优点，在解决非线性拟合问题方面表现出非凡的数据挖掘能力与优势。在基因表达式编程理论分析基础上，利用该方法建立了混凝土徐变函数知识挖掘模型。实例表明，该模型比常规方法得到的函数有更高的精度。
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| 关 键 词： | 基因表达式编程(GEP) 混凝土徐变 知识挖掘 |
The Application of Gene Expression Programming in Concrete Creep Analysis |
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| Affiliation: | College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, P. R. China; Yellow River Institute of Hydraulic Research, Zhengzhou 450003, P. R. China;Research Center on Levee Safety & Disaster Prevention Ministry of Water Re;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, P. R. China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Nanjing, 210098, P. R. China;;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, P. R. China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Nanjing, 210098, P. R. China;;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, P. R. China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Nanjing, 210098, P. R. China; |
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| Abstract: | The creep analysis of concrete is of great importance in stress calculation and deformation analysis of concrete structures. Since many factors affect concrete creep, the creep function is highly nonlinear, making it difficult to obtain satisfactory results using the traditional regression method. The gene expression programming (GEP) method, which combines the advantages of GA and gene programming, shows great promise and capability of data mining in nonlinear fitting. Based on theoretical analysis of GEP, the data mining model of the creep function was established by using GEP. Examples indicate this model is more accurate than functions obtained by conventional methods. |
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| Keywords: | Gene Expression Programming (GEP) concrete creep knowledge mining |
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