基于机器学习技术的多层剪力结构反应谱分析

Manolis Georgioudakis, V. Plevris
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引用次数: 3

摘要

为了在土木和结构工程应用中做出准确的抗震性能评估,结构的动力分析是一个必须考虑的计算密集型过程。为了避免这些计算要求很高的任务,工程师在实践中经常使用简化的方法来估计复杂结构在动荷载作用下的行为。本文对几种具有不同特征的机器学习(ML)算法进行了评估,这些算法旨在预测多层建筑的动态分析响应。通过标准抽样方法和常规的响应谱模态分析程序生成了大型动态响应分析结果数据集。为了获得最佳的算法性能,详细阐述了一个广泛的超参数搜索,然后是相应的特征重要度。将表现出最佳性能的ML模型部署到web应用程序中,目的是根据多层建筑的特征提供动态响应的预测。
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Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques
The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics.
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