基于机器学习的方形截面高层建筑干扰风压预测
TU973.213%TU317.1; 为了预测干扰作用下方形截面高层建筑风荷载,进行了 576组工况的风洞干扰试验.应用3种机器学习方法对受扰建筑风荷载进行了预测模型的训练、测试和对比验证.预测结果表明:决策树回归(DTR)、随机森林(RF)和梯度提升回归树(GBRT)模型均能有效预测受扰建筑风荷载,且预测平均风荷载性能优于预测极值风荷载;GBRT模型在预测风荷载方面表现最佳,该模型预测极小值和平均风荷载得到的R2分别为0.994 0和0.999 7;经过超参数优化的GBRT模型,不论是内插还是外推,均能展现良好的预测性能;对比显示在迎风面及两侧面上预测风压分布较好,在背风面预测效果相对较弱....
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| Published in | 东南大学学报（自然科学版） Vol. 54; no. 6; pp. 1425 - 1437 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
华南理工大学亚热带建筑与城市科学全国重点实验室,广州 510641
01.11.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-0505 |
| DOI | 10.3969/j.issn.1001-0505.2024.06.011 |
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| Abstract | TU973.213%TU317.1; 为了预测干扰作用下方形截面高层建筑风荷载,进行了 576组工况的风洞干扰试验.应用3种机器学习方法对受扰建筑风荷载进行了预测模型的训练、测试和对比验证.预测结果表明:决策树回归(DTR)、随机森林(RF)和梯度提升回归树(GBRT)模型均能有效预测受扰建筑风荷载,且预测平均风荷载性能优于预测极值风荷载;GBRT模型在预测风荷载方面表现最佳,该模型预测极小值和平均风荷载得到的R2分别为0.994 0和0.999 7;经过超参数优化的GBRT模型,不论是内插还是外推,均能展现良好的预测性能;对比显示在迎风面及两侧面上预测风压分布较好,在背风面预测效果相对较弱.GBRT模型可为预测干扰作用下高层建筑风荷载提供一种经济有效的、可以部分替代传统风洞试验和数值模拟的机器学习方法. |
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| AbstractList | TU973.213%TU317.1; 为了预测干扰作用下方形截面高层建筑风荷载,进行了 576组工况的风洞干扰试验.应用3种机器学习方法对受扰建筑风荷载进行了预测模型的训练、测试和对比验证.预测结果表明:决策树回归(DTR)、随机森林(RF)和梯度提升回归树(GBRT)模型均能有效预测受扰建筑风荷载,且预测平均风荷载性能优于预测极值风荷载;GBRT模型在预测风荷载方面表现最佳,该模型预测极小值和平均风荷载得到的R2分别为0.994 0和0.999 7;经过超参数优化的GBRT模型,不论是内插还是外推,均能展现良好的预测性能;对比显示在迎风面及两侧面上预测风压分布较好,在背风面预测效果相对较弱.GBRT模型可为预测干扰作用下高层建筑风荷载提供一种经济有效的、可以部分替代传统风洞试验和数值模拟的机器学习方法. |
| Abstract_FL | To predict the wind load of the high-rise building with square section under interference,wind tun-nel interference tests are conducted under 576 working conditions.Three kinds of machine learning methods are used to train,test and verify the prediction model of wind load in the principal building.The prediction re-sults show that decision tree regression(DTR),random forest(RF),and gradient boosting regression tree(GBRT)models can predict the wind load of the principal building effectively,and the prediction perform-ance for the average wind load is better than that for the extreme wind load.The GBRT model has the best performance in predicting wind loads,and the R2 obtained by the model for predicting minimum and average wind loads are 0.994 0 and 0.999 7,respectively.The GBRT model with hyperparameter optimization,whether interpolated or extrapolated,can show good prediction performance.The comparison shows that the prediction performance for the wind pressure distribution is better on the windward side and the two sides,while the prediction effect is relatively weak on the lee side.GBRT model can provide an economical and ef-fective machine learning method for predicting wind loads of high-rise buildings under interference,which can partially replace traditional wind tunnel test and numerical simulation. |
| Author | 胡松雁 谢壮宁 杨易 |
| AuthorAffiliation | 华南理工大学亚热带建筑与城市科学全国重点实验室,广州 510641 |
| AuthorAffiliation_xml | – name: 华南理工大学亚热带建筑与城市科学全国重点实验室,广州 510641 |
| Author_FL | Hu Songyan Yang Yi Xie Zhuangning |
| Author_FL_xml | – sequence: 1 fullname: Hu Songyan – sequence: 2 fullname: Xie Zhuangning – sequence: 3 fullname: Yang Yi |
| Author_xml | – sequence: 1 fullname: 胡松雁 – sequence: 2 fullname: 谢壮宁 – sequence: 3 fullname: 杨易 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.3969/j.issn.1001-0505.2024.06.011 |
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| DocumentTitle_FL | Interference wind pressure prediction of high-rise buildings with square section based on machine learning |
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| Keywords | wind pressure coefficient interference effect 机器学习 gradient boosting regression tree 梯度提升回归树 干扰效应 高层建筑 high-rise buildings machine learning 风压系数 |
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| Snippet | TU973.213%TU317.1; 为了预测干扰作用下方形截面高层建筑风荷载,进行了 576组工况的风洞干扰试验.应用3种机器学习方法对受扰建筑风荷载进行了预测模型的训练、测试和对比验... |
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| Title | 基于机器学习的方形截面高层建筑干扰风压预测 |
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