Effect of Heterogeneous Terrain on Wind Loads on Buildings

飓风过后的破坏调查表明，建筑物附近的地形会对作用在建筑物上的风荷载产生重大影响，进而影响建筑物的性能。更好地模拟地形对风荷载的影响可以提高建筑物的抗风能力。研究人员和工程师通常依赖定性术语来描述地形，导致对地形效果的主观解释和不准确的建模。当地形不均匀时，例如郊区建筑、树木和道路的混合，地形的建模尤其具有挑战性。在本研究中，将针对不同类型的非均质地形进行风洞试验。将开发分析和计算工具来描述观测到的地形效应。为了获得真实的地形配置，将使用地形图像和相应的土地使用分类来开发实际地形的数据库。这项研究的结果将包括对具有各种不同地形配置的建筑物上的风荷载的测量；地形图像数据库、每幅图像中的土地使用分类以及相应的不同地形粗糙度；用于识别卫星或航空图像中的地形特征的深度学习神经网络；以及估计不同地形对风荷载影响的模型。这些成果将通过加强在飓风损失估计、基于性能的风能设计和风能评估方面的研究和教育，为国家福利和繁荣做出贡献，因为地形效应是这些研究领域的一个重要问题。该项目的数据将在美国国家科学基金会支持的自然灾害工程研究基础设施(NHERI)数据库中公开，网址为https://www.DesignSafe-ci.org/.这项研究的目的是获得关于不同地形如何影响建筑物风荷载的新知识。这项研究将使用佛罗里达大学的NHERI边界层风洞，该风洞可以自动模拟不同类型的地形粗糙度。将对三种不同类型的非均匀地形进行风洞测试：粗糙度过渡、小开口和实际的非均匀地形。将测量所有测试用例的风速分布。将测量一座低层建筑和一座中层建筑的风荷载。将开发分析和计算模型来解释观测到的地形效应。为了构建用于风洞测试的不同地形配置，本研究将创建一个由地形图像和相应的土地使用分类组成的实际地形数据库。将利用深度学习神经网络来改进现有的土地利用分类，特别是加强发达地区的粗糙度估计。在构建数据库后，将使用聚类算法来消除数据中的冗余。因此，可以系统地构建风洞试验的地形配置，并表示各种可能的地形而不重叠。这项研究还将解决以下问题：(1)上风地形的影响将在低层建筑和中层建筑之间进行比较；(2)人们经常假设在估计表面粗糙度时可以忽略小开口(如小型停车场)，但这一假设尚未得到实验证明。这项研究将研究不同地形类型的开放效应；(3)当假设非均匀地形为等效均质地形时，本项目将量化风荷载的不确定性。这些知识将有助于理解建筑规范和标准简化的影响；以及(4)本项目将制定能够解释观察到的实验数据的分析和计算模型。这些模型将有助于描述未包括在实验中的地形的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Post-hurricane damage surveys show that the terrain near a building can have a significant effect on the wind loads acting on the building, which in turn can influence the building performance. Better modeling of the effect of the terrain on wind loads can improve the wind resistance of buildings. Researchers and engineers generally have relied on qualitative terms to describe the terrain, leading to subjective interpretation and inaccurate modeling of the terrain effect. The modeling of the terrain has been especially challenging when the terrain is non-homogeneous, such as a mix of suburban buildings, trees, and roads. In this research, wind tunnel testing will be conducted for different types of heterogeneous terrains. Analytical and computational tools will be developed to characterize the observed terrain effect. To obtain realistic terrain configurations, a database of actual terrains will be developed using terrain images and corresponding land usage classifications. The outcomes of this research will include measurements of wind loading on buildings with various heterogeneous terrain configurations; a database of terrain images, land usage classification within each image, and corresponding heterogeneous terrain roughness; a deep-learning neural network to recognize terrain features in satellite or aerial images; and models to estimate the effect of heterogeneous terrain on wind loading. These outcomes will contribute to national welfare and prosperity by enhancing research and education in hurricane loss estimation, performance-based wind design, and wind energy assessment, as terrain effect is an important issue in these research areas. Data from this project will be made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot at https://www.DesignSafe-ci.org/. The objective of this research is to gain new knowledge on how heterogeneous terrains affect wind loads on buildings. The research will use the NHERI Boundary Layer Wind Tunnel at the University of Florida, which can simulate heterogeneous terrain roughness in an automated way. Wind tunnel testing will be conducted for three different types of heterogeneous terrains: roughness transitions, small openings, and actual heterogeneous terrains. The wind speed profile will be measured for all test cases. Wind loading will be measured for one low-rise building and one mid-rise building. Analytical and computational models will be developed to explain the observed terrain effect. To construct heterogeneous terrain configurations for wind tunnel testing, this research will create a database of actual terrains composed of terrain images and corresponding land usage classifications. A deep-learning neural network will be utilized to improve the existing land usage classifications, particularly for enhancing roughness estimation of developed areas. After constructing the database, clustering algorithms will be used to remove redundancy in the data. Therefore, terrain configurations for wind tunnel testing can be systematically constructed, and represent various possible terrains without overlap. This research will also address the following: (1) The effect of the upwind terrain will be compared between low-rise buildings and mid-rise buildings; (2) It is often assumed that small openings (such as a small parking lot) can be ignored in surface roughness estimation, but this has not been proven experimentally. This research will investigate the opening effect for various terrain types; (3) This project will quantify uncertainties in wind loading when an equivalent homogeneous terrain is assumed for a heterogeneous terrain. This knowledge will help in understanding the effect of simplifications in building codes and standards; and (4) This project will formulate analytical and computational models that can explain the observed experimental data. These models will be useful in describing the effect of terrain not included in the experiment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.