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Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

机译:使用NLPCA-SVR在高层建筑中进行环境效果过滤

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摘要

The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.
机译:结构的模态频率受环境因素(例如温度,风速等)的连续变化影响。本研究将非线性主成分分析(NLPCA)与支持向量回归(SVR)结合在一起,以建立一个数学模型来反映两者之间的相关性。环境因素和模态频率。 NLPCA首先用于消除不同环境因素之间的高度相关性,并提取非线性主成分。提取的非线性主成分输入到SVR模型中进行训练和预测。广州新电视塔(GNTVT)基准中提供的测量数据验证了该方法的有效性。应用网格搜索法(GSM),遗传算法(GA)和果蝇优化算法(FOA)确定SVR模型的最优超参数。 FOA的优化结果最适合NLPCA-SVR模型。通过假设检验和拟合优度检验评估,结果表明该方法具有较高的泛化性能,可以很好地反映环境因子与模态频率之间的相关性。该方法可以有效消除环境因素对模态频率的影响。

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