Wind surface mean pressure coefficient (C̄p) is an essential parameter for the assessment of wind induced forces that is a must input to all structural designs. An extensive experimentation is carried out to obtain pressure coefficient data over the surfaces of C-shaped building models of varying aspect ratio, corner curvature and angle of incidence in a sub-sonic wind tunnel. The studies also include models without corner curvature. In this study, a technique known as Gene-Expression Programming (GEP) is used to develop a model equation using experimental values of pressure coefficient data collected at the grid points of the frontal surface under varying conditions. And this developed model is used to predict surface mean pressure coefficients (C_p). The predicted values of C̄_p using the developed model are compared with the corresponding C̄_p values obtained by Swami and Chandra (S&C) equation and Muehleisen and Patrizi (M&P) equations. The prediction made by the developed GEP model is also validated with the actual building data of Tokyo Polytechnic University (TPU). The results signify the ability of the model to predict the C̄_p values for practical purposes. The error analysis of the results show that the predicted values of C̄_p using developed GEP correlation are more close to the experimental values than those obtained by using other two methods.

中文翻译：

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基因表达程序设计，用于评估建筑表面的表面平均压力系数

风表面平均压力系数（C̄p）是评估风诱导力的基本参数，这是所有结构设计的必填项。进行了广泛的实验，以获取亚音速风洞中纵横比，拐角曲率和入射角变化的C形建筑模型表面的压力系数数据。研究还包括没有拐角曲率的模型。在这项研究中，一种被称为基因表达编程（GEP）的技术被用来开发模型方程式，该模型方程式使用的是在各种条件下在正面表面的网格点处收集的压力系数数据的实验值。并使用此开发模型来预测表面平均压力系数（C _p）。的预测值Ç _p使用开发的模型与对应的比较Ç _p由斯瓦米和Chandra（S＆amp; C）方程和Muehleisen和柏德（M＆P）式求出的值。所开发的GEP模型所做的预测也得到了东京工业大学（TPU）的实际建筑数据的验证。结果表明该模型的预测能力Ç _p值的实际用途。结果的误差分析表明，预测值Ç _p使用发达GEP相关更接近比使用其它两种方法获得的那些的实验值。