基于神经网络的矩形钢管混凝土柱承载力分析及其可视化研究

项目名称： 基于神经网络的矩形钢管混凝土柱承载力分析及其可视化研究

项目编号： No.61272264

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 陈志华

作者单位： 天津大学

项目金额： 81万元

中文摘要： 矩形钢管混凝土是组合结构的主要形式，应用越来越广，其承载力计算是工程应用的关键。已有计算公式具有精度和理论假定问题，各方法之间存在较大分歧，探索更高效准确的承载力分析方法并将其承载过程可视化是目前工程亟待解决的关键问题。本项目基于统计数据分析进行试验设计，补充关键受力形式和不同参数的构件承载试验及数值模拟，在得到大量数据源的基础上，突破常规手段，基于智能计算方法中的神经网络技术，训练出成熟的矩形钢管混凝土柱承载性能神经网络模型，进行参数化分析得到承载力高精度计算方法。结合参数化有限元全过程精细分析，剖析不同破坏模式，构建变形演化规则，计算不同荷载阶段的网格节点位置变化，建立构件变形的三维动态图像，快速高效获得构件变形全过程的可视化仿真及破坏模式。本研究成果可拓展计算机科学在土木工程领域的应用范围，不仅得到适用性广、准确度高的承载力计算方法，还对其它工程的科学计算及可视化有借鉴意义。

中文关键词： 矩形钢管混凝土柱；神经网络；设计方法；破坏模态；可视化

英文摘要： As a main form of compostite structure, Concrete-filled Rectangular Steel Tube (CFRT) has been widely used in modern construction practice, and the carrying capacity calculation for CFRT column has become the key point for its engineering application. However, there are problems with both calculation accuracy and theoretical hypothesis of all the previous formulas, and the predictions of each calculation method show significant differences. Therefore,looking for a more precise carrying capacity prediction method for CFRT column and then displaying its loading process dynamically are the key problems that need to be solved in present civil engineering. Based on the analysis of previous experimental data, some new bearing capacity tests of CFRT columns will be designed and carried out in this project. Loading method,material property and cross-sectional dimention will be selected as the main parameters in the experimental study, and then lots of parametric numerical analysis will be performed. Based on the large quantity experimental and numerical simulation data, the artificial neural network, which is one of the intelligent computing techniques, will be adopted as a new method to establish an effective neural network model for predicting the CFRT column load-carrying capacity. Then, according to the detailed an

英文关键词： Rectangular CFT column；Artificial neural network；Design approach；Failure mode；Visualization