Machine learning‐based wind‐induced response analysis in rectangular building models with limbs

Prasenjit Sanyal, Rajdip Paul, Sujit Kumar Dalui
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Abstract

SummaryThis study investigates the impact of different positions of two limbs on the structural response of a rectangular building model to wind forces. The building's geometry assumes Z and + shapes based on specific limb configurations. Computational fluid dynamics (CFD) simulations are performed to quantify wind‐induced pressures, resulting in wind force coefficients. These coefficients are used to develop predictive machine learning models through Gene Expression Programming, Group Method of Data Handling‐combinatorial (GMDH‐Combi), Model Tree, and Artificial Neural Network (ANN) techniques. The ANN analysis explores various combinations of training algorithms, adaptation functions, activation functions, and performance functions to enhance model accuracy. Among these, the Levenberg–Marquardt (LM) with gradient descent with momentum (GDM) adaptation function and sigmoid activation function exhibit superior performance with high R‐squared values. These predictive models are then employed for a comprehensive comparative assessment of the maximum wind force coefficient (CF, max) concerning different limb positions and angles of attack (AOA). For CF, max vs Limb position, variations of limb position are examined for most critical cases of AOA. Similarly, the study of CF, max vs AOA involves an exhaustive investigation into the variation of AOA for the building with the worst limb position. The analysis reveals that changes in AOA have a more pronounced effect on CF, max compared to alterations in limb position. Interestingly, within the AOA range of 1.5 to 2.5, the CF, max consistently reaches a minimum across all models. However, the relationship between CF, max and the critical structural parameter ‘S' (representing limb position) remains less conclusive for the most significant AOAs.
基于机器学习的带肢体矩形建筑模型风致响应分析
摘要 本研究探讨了两个肢体的不同位置对矩形建筑模型的风力结构响应的影响。根据特定的肢体配置,建筑物的几何形状假设为 Z 形和 + 形。通过计算流体动力学(CFD)模拟来量化风引起的压力,从而得出风力系数。这些系数用于通过基因表达编程、数据处理组方法-组合(GMDH-Combi)、模型树和人工神经网络(ANN)技术开发预测性机器学习模型。人工神经网络分析探索了各种训练算法、适应函数、激活函数和性能函数的组合,以提高模型的准确性。其中,Levenberg-Marquardt(LM)与带动量梯度下降(GDM)的适应函数和 sigmoid 激活函数表现出卓越的性能和较高的 R 平方值。然后,利用这些预测模型对不同肢体位置和攻角(AOA)下的最大风力系数(CF,max)进行综合比较评估。对于最大风力系数与肢体位置的关系,研究了 AOA 最关键情况下肢体位置的变化。同样,最大 CF 值与 AOA 值的对比研究涉及到对肢体位置最差的建筑物的 AOA 值变化的详尽调查。分析表明,与肢体位置的变化相比,AOA 的变化对 CF 最大值的影响更为明显。有趣的是,在 1.5 到 2.5 的 AOA 范围内,所有模型的最大 CF 值都达到了最小值。然而,对于最显著的AOA,最大CF值与关键结构参数 "S"(代表肢体位置)之间的关系仍然不那么确定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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