建筑基本周期多因素机器学习预测模型

MULTI-FACTOR MACHINE LEARNING PREDICTION MODEL FOR THE NATURAL PERIOD OF BUILDINGS

  • 摘要: 建筑物基本周期是其最重要的动力特性参数,影响因素众多。受限于曲线拟合的传统建模手段,目前的基本周期预测模型表达式中仅能包含高度或层数等单一因素,而忽略其他因素的影响。数据驱动机器学习方法的出现,为建筑周期多因素预测模型的建立提供了新思路。研究从大量文献中收集整理了2561条建筑周期的实测数据,形成了包含建筑高度、层数、材料、功能、地区等多因素的建筑周期实测数据库。建立了具有自学习能力的建筑基本周期多因素机器学习预测模型,避免了一般机器学习模型中繁琐的参数调节过程,提升模型的鲁棒性和适用性。与传统模型结果的对比表明:所提预测模型的适用结构类型范围广、准确性更高,配合云端服务器可形成一种全新的、开放式自学习的建筑周期预测模式。

     

    Abstract: The natural period of buildings is a very important parameter for structural dynamic characteristic analysis, which is influenced by many factors. Due to the limitations of the traditional modeling method of curve fitting, the current natural period prediction model only includes single factor such as the height or the number of storeys, while the influence of other factors is ignored. The emergence of data-driven machine learning method provides a new idea to establish a multi-factor prediction model. A total of 2561 building period records of existing buildings are collected from a large number of published documents. A building period database is developed, including the building height, the number of floors, materials, functions, et al. A multi-factor machine learning prediction of building fundamental period with self-learning ability is established, which avoids the tedious parameter adjusting procedure. Comparisons with traditional prediction models show that the proposed prediction model has a wider prediction range of various structural types and higher accuracy. Combined with cloud server, it can form a new, publicly-open and self-learning building period prediction model.

     

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