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 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.
