【摘要】 建筑物沉降变形行为是一个复杂的非线性动力学演化过程,针对这一过程,引入进化算法的全局优化思想,结合时间序列分析的基本理论,提出了一种新的构筑物变形动态预测模型进化识别算法·该方法将复杂的模型结构与参数混杂的搜索空间简化为两个相对简单的模型结构进化过程和模型参数进化过程,分别由遗传规划和遗传算法完成·设计了模型结构和参数的共同进化方案,实现对非线性动力学演化模型结构和参数的全局最优搜索·实例分析结果表明该方法具有较好的预测精度和推广预测能力,并且显示出较高的自组织能力,为构筑物变形预测提供了一个有效的分析工具·更多还原

【Abstract】 The settlement behavior of constructions can be characterized as nonlinear dynamic evolution progress. Based on the theory of time series analysis and combining it with genetic evolutionary algorithms for global optimization, a new hybrid method is proposed to identify the dynamic evolution prediction model of building settlement due to the deformation data observed. According to the extremely complex search progress of model structure coupling with relevant parameters, this method thus divides the whole search progress into two relatively simple processes, for modeling, i.e., the structure evolution and parameters evolution, which are to be implemented by the symbol regression techniques of genetic programming and genetic algorithm, respectively. Then, an overall evolution scheme including both as above is designed to search the globally optimal nonlinear dynamic input/output model for predicting the building settlement. Applications to the settlement prediction of several high buildings showed that the method proposed is available to accurate prediction with extendible prediction, especially the new algorithm presents a high self-organizing ability during applications so as to provide an efficient analysis means for the prediction of settlement of high buildings.更多还原