{"title":"基于适当正交分解和机器学习的超高层建筑风压预测","authors":"Jia‐Xing Huang, Qiu‐Sheng Li, Xu‐Liang Han","doi":"10.1002/tal.2174","DOIUrl":null,"url":null,"abstract":"Detailed wind pressure information plays a critical role in the accurate estimation of wind loads on high‐rise buildings, especially for complex‐shaped supertall buildings. However, owing to the limited internal space of a scaled building model and the capacity of data‐acquisition devices, it is often difficult to acquire the wind pressures at all positions of interest on the entire model in wind tunnel testing. To this end, a novel approach that combines the proper orthogonal decomposition (POD) and machine learning (ML) is presented in this paper for the prediction of wind pressure time series (WPTS) on supertall building models in wind tunnel testing. In this approach, the prediction of WPTS is converted into the estimation of several main eigenmodes and mean wind pressures by combining the POD with ML. This strategy can effectively reduce the computational effort compared to the direct prediction of WPTS. A combined ML model consisting of the Gaussian process regression (GPR), decision tree regression (DTR), and random forest (RF) (i.e., POD‐GPR‐DTR‐RF model) is utilized for the prediction of eigenmodes and mean wind pressures. Wind pressure records from a wind tunnel experiment of a 600‐m‐high building are employed to verify the accuracy and effectiveness of the presented approach. The results show that the combined ML model (i.e., POD‐GPR‐DTR‐RF model) developed based on the proposed approach performs satisfactorily in the prediction of WPTS and outperforms the conventional method that combines POD with backpropagation neural network model (i.e., POD‐BPNN model), demonstrating that the proposed approach is an effective tool for prediction of WPTS on supertall buildings.","PeriodicalId":501238,"journal":{"name":"The Structural Design of Tall and Special Buildings","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of wind pressures on supertall buildings based on proper orthogonal decomposition and machine learning\",\"authors\":\"Jia‐Xing Huang, Qiu‐Sheng Li, Xu‐Liang Han\",\"doi\":\"10.1002/tal.2174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detailed wind pressure information plays a critical role in the accurate estimation of wind loads on high‐rise buildings, especially for complex‐shaped supertall buildings. However, owing to the limited internal space of a scaled building model and the capacity of data‐acquisition devices, it is often difficult to acquire the wind pressures at all positions of interest on the entire model in wind tunnel testing. To this end, a novel approach that combines the proper orthogonal decomposition (POD) and machine learning (ML) is presented in this paper for the prediction of wind pressure time series (WPTS) on supertall building models in wind tunnel testing. In this approach, the prediction of WPTS is converted into the estimation of several main eigenmodes and mean wind pressures by combining the POD with ML. This strategy can effectively reduce the computational effort compared to the direct prediction of WPTS. A combined ML model consisting of the Gaussian process regression (GPR), decision tree regression (DTR), and random forest (RF) (i.e., POD‐GPR‐DTR‐RF model) is utilized for the prediction of eigenmodes and mean wind pressures. Wind pressure records from a wind tunnel experiment of a 600‐m‐high building are employed to verify the accuracy and effectiveness of the presented approach. The results show that the combined ML model (i.e., POD‐GPR‐DTR‐RF model) developed based on the proposed approach performs satisfactorily in the prediction of WPTS and outperforms the conventional method that combines POD with backpropagation neural network model (i.e., POD‐BPNN model), demonstrating that the proposed approach is an effective tool for prediction of WPTS on supertall buildings.\",\"PeriodicalId\":501238,\"journal\":{\"name\":\"The Structural Design of Tall and Special Buildings\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Structural Design of Tall and Special Buildings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/tal.2174\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Structural Design of Tall and Special Buildings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/tal.2174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
详细的风压信息对于准确估算高层建筑,尤其是形状复杂的超高层建筑的风荷载起着至关重要的作用。然而,由于缩尺建筑模型的内部空间和数据采集设备的能力有限,在风洞试验中往往难以获取整个模型上所有相关位置的风压。为此,本文提出了一种结合适当正交分解(POD)和机器学习(ML)的新方法,用于预测风洞试验中超高层建筑模型的风压时间序列(WPTS)。在这种方法中,通过将 POD 与 ML 相结合,将 WPTS 预测转换为几个主要特征模式和平均风压的估计。与直接预测 WPTS 相比,这种策略可以有效减少计算量。由高斯过程回归(GPR)、决策树回归(DTR)和随机森林(RF)组成的组合 ML 模型(即 POD-GPR-DTR-RF 模型)被用于预测特征模式和平均风压。为了验证该方法的准确性和有效性,我们使用了 600 米高建筑物风洞实验的风压记录。结果表明,基于所提方法开发的组合 ML 模型(即 POD-GPR-DTR-RF 模型)在预测 WPTS 方面表现令人满意,并且优于将 POD 与反向传播神经网络模型相结合的传统方法(即 POD-BPNN 模型),这表明所提方法是预测超高层建筑 WPTS 的有效工具。
Prediction of wind pressures on supertall buildings based on proper orthogonal decomposition and machine learning
Detailed wind pressure information plays a critical role in the accurate estimation of wind loads on high‐rise buildings, especially for complex‐shaped supertall buildings. However, owing to the limited internal space of a scaled building model and the capacity of data‐acquisition devices, it is often difficult to acquire the wind pressures at all positions of interest on the entire model in wind tunnel testing. To this end, a novel approach that combines the proper orthogonal decomposition (POD) and machine learning (ML) is presented in this paper for the prediction of wind pressure time series (WPTS) on supertall building models in wind tunnel testing. In this approach, the prediction of WPTS is converted into the estimation of several main eigenmodes and mean wind pressures by combining the POD with ML. This strategy can effectively reduce the computational effort compared to the direct prediction of WPTS. A combined ML model consisting of the Gaussian process regression (GPR), decision tree regression (DTR), and random forest (RF) (i.e., POD‐GPR‐DTR‐RF model) is utilized for the prediction of eigenmodes and mean wind pressures. Wind pressure records from a wind tunnel experiment of a 600‐m‐high building are employed to verify the accuracy and effectiveness of the presented approach. The results show that the combined ML model (i.e., POD‐GPR‐DTR‐RF model) developed based on the proposed approach performs satisfactorily in the prediction of WPTS and outperforms the conventional method that combines POD with backpropagation neural network model (i.e., POD‐BPNN model), demonstrating that the proposed approach is an effective tool for prediction of WPTS on supertall buildings.