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Estimation of wind pressure coefficients on multi-building configurations using data-driven approach

机译:利用数据驱动方法估算多建筑配置的风压系数

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Wind load acting on a standalone structure is different from that acting on a similar structure which is surrounded by other structures in close proximity. The presence of other structures in the surrounding can change the wind flow regime around the principal structure and thus causing variation in wind loads compared to a standalone case. This variation on wind loads termed as interference effect depends on several factors like terrain category, geometry of the structure, orientation, wind incident angle, interfering distances etc., In the present study, a three building configuration is considered and the mean pressure coefficients on each face of principle building are determined in presence of two interfering buildings. Generally, wind loads on interfering buildings are determined from wind tunnel experiments. Computational fluid dynamic studies are being increasingly used to determine the wind loads recently. Whereas, wind tunnel tests are very expensive, the CFD simulation requires high computational cost and time. In this scenario, Artificial Neural Network (ANN) technique and Support Vector Regression (SVR) can be explored as alternative tools to study wind loads on structures. The present study uses these data-driven approaches to predict mean pressure coefficients on each face of principle building. Three typical arrangements of three building configuration viz. L shape, V shape and mirror of L shape arrangement are considered with varying interfering distances and wind incidence angles. Mean pressure coefficients (Cp mean) are predicted for 45 degrees wind incidence angle through ANN and SVR. Further, the critical faces of principal building, critical interfering distances and building arrangement which are more prone to wind loads are identified through this study. Among three types of building arrangements considered, a maximum of 3.9 times reduction in Cp mean values are noticed under Case B (V shape) building arrangement with 2.5B interfering distance. Effect of interfering distance and building arrangement on suction pressure on building faces has also been studied. Accordingly, Case C (mirror of L shape) building arrangement at a wind angle of 45° shows less suction pressure. Through this study, it was also observed that the increase of interfering distance may increase the suction pressure for all the cases of building configurations considered.
机译:作用在独立结构上的风力负荷与作用在类似的结构上不同,其被紧密接近的其他结构包围。周围的其他结构的存在可以改变主结构周围的风流状态,从而导致与独立情况相比的风力载荷的变化。被称为干扰效应的风力载荷的这种变化取决于地形类别,结构的几何形状,方向,风入射角,干扰距离等,在本研究中,考虑了三种建筑配置,并且平均压力系数上主要建筑物的每张面部都是在两个干扰建筑物的存在下确定。通常,从风洞实验确定干扰建筑物上的风力载荷。计算流体动力学研究越来越多地用于确定风荷载。虽然,风洞测试非常昂贵,但CFD仿真需要高计算成本和时间。在这种情况下,人工神经网络(ANN)技术和支持向量回归(SVR)可以探索为替代工具,以研究结构上的风力载荷。本研究利用这些数据驱动的方法来预测原理建筑物的每张面上的平均压力系数。三个建筑配置viz的三个典型布置。 L形状布置的L形,V形和镜面被认为是不同的干扰距离和风入射角。平均压力系数(CP意味着)通过ANN和SVR预测45度风发生角度。此外,通过该研究鉴定了主要建筑物的关键面,主要构建,临界干扰距离和建筑装置,这些距离和建筑装置更容易通过该研究识别。在考虑的三种类型的建筑物布置中,CP平均值的最大值最多减少了3.9倍,在B(V形)建筑布置下,具有2.5B干扰距离。研究了干扰距离和建筑物布置对建筑面上吸力的影响。因此,在45°的风角处的建筑物布置的情况C(L形的镜子)显示出较少的吸力压力。通过这项研究,还观察到干扰距离的增加可能增加所考虑的构建配置的所有情况的吸力压力。

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