CAREER: Remote Sensing for Enhanced Understanding of Tornado Actions and Broadened STEM Education in Rural West Texas

这个教师早期职业发展计划（Career）奖的目标是通过使用小尺度和大尺度的遥感技术来促进对龙卷风对建筑物影响的理解。虽然人们知道龙卷风会产生地球上最强的风，但对这些强风的强度、频率和结构的充分了解仍然是难以捉摸的。为了有效地设计保护生命和财产免受龙卷风伤害的结构，有必要更好地了解龙卷风中的风速、龙卷风风与结构的相互作用以及龙卷风发生的可能性。在小规模上，这项研究将采用尖端技术，如激光扫描和数字成像，来创建高度详细的龙卷风破坏结构的3D数字模型。该研究还将通过使用地球观测卫星图像来检测大规模龙卷风现象，以帮助发现“失踪”（未被观测到的）龙卷风。对区域龙卷风气候、风结构和风速特征的改进将改进龙卷风预报模型和基于风险的设计方法。提高对龙卷风危害的认识将提高社会对灾害进行规划、快速反应和恢复的能力，从而促进国家福利和繁荣。该项目还将提供一个平台，通过鼓励本科生与美国和国际研究机构进行研究合作，鼓励他们追求研究生教育，并通过广泛的自然灾害研讨会/研讨会系列，将来自学术界、政府、并将工业带到校园，以激励学生追求具有挑战性的自然灾害缓解问题的研究和创新。这个项目的数据将在nsf支持的自然灾害工程研究基础设施（NHERI）设计安全数据仓库（https://www.DesignSafe-ci.org）中存档和共享。CAREER奖的目标是通过遥感实现快速和全面的多尺度风害评估，提高对龙卷风-结构相互作用的理解，并通过最大限度地减少风害造成的生命和财产损失，激励和培训学生在确保国家福利和繁荣方面发挥重要作用。通过战略研究合作，首席研究员和本科生研究人员将通过遥感技术在多个尺度上探索龙卷风的行动。在小尺度上，利用三维现实捕捉平台（摄影测量和激光雷达）对工程钢结构龙卷风引起的故障进行增强建模，并对这些平台进行优化，以平衡对受损结构进行详细法医分析的广度和深度（与德克萨斯理工大学国家风力研究所合作）。然后，该模型将用于通过测量偏转和研究破坏模式来研究龙卷风对结构的影响。在大范围内，该项目将研究一种方法，通过多光谱图像分析来检测“失踪”的龙卷风，以增强人口稀少和森林地区的龙卷风气候学（与加拿大西部大学合作）。该研究将通过安全、可靠、高效和尖端的遥感技术，在从微小的结构变形到整个龙卷风路径水平的空间尺度上，推进复杂的龙卷风-结构相互作用、龙卷风强度估计和龙卷风发生检测的知识。该研究将为物理和数值龙卷风荷载模拟提供验证方法，促进对增强藤田尺度风速估计的必要调整，并探索高分辨率成像平台的优化，以最有效地促进对龙卷风对建筑环境影响的理解。发现“失踪”的龙卷风将加强气候学研究，为改进预报、警报和风险模型提供基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this Faculty Early Career Development Program (CAREER) award is to advance understanding of the impacts of tornadoes on structures through the use of remote sensing technologies at both small and large scales. Although tornadoes are known to produce some of the strongest winds on earth, sufficient understanding of the magnitude, frequency, and structure of these violent winds remains elusive. To effectively design structures that provide protection for life and property against tornadoes, it is necessary to better understand the wind speeds in tornadoes, the interaction of tornado winds with structures, and the likelihood of tornado occurrences. At the small scale, this research will employ cutting-edge technologies, such as laser scanning and digital imaging, to create highly detailed, 3D digital models of tornado-damaged structures. The research will also examine large-scale tornado phenomena by using earth-observing satellite images to help detect "missing" (unobserved) tornadoes. Improved characterization of regional tornado climates, wind structures, and wind speeds will improve tornado forecast models and risk-based design methodologies. Heightened understanding of tornado hazard will advance society's ability to plan for, and rapidly respond and recover from, disasters, thus promoting national welfare and prosperity. This project also will provide a platform to enhance STEM education in a large rural portion of West Texas by encouraging the pursuit of graduate education through engaging undergraduate students in research collaborations with U.S. and international research institutions and an expansive Natural Hazards Workshop/Symposium series that will bring researchers from academia, government, and industry to campus to inspire students to pursue research and innovation for challenging natural hazard mitigation problems. Data from this project will be archived and shared in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) DesignSafe Data Depot (https://www.DesignSafe-ci.org). The objectives of this CAREER award are the achievement of rapid and comprehensive multi-scale wind damage assessments through remote sensing, advancement of understanding of tornado-structure interaction, and inspiration and training of students for vital roles in securing national welfare and prosperity through minimizing life and property losses from wind hazards. Through strategic research collaborations, the Principal Investigator and undergraduate student researchers will explore tornado actions via remote sensing technologies at multiple scales. At small scales, reality-capture-enhanced modeling of tornado-induced failures of engineered steel structures will utilize 3D reality-capture platforms (photogrammetry and lidar) and optimization of these platforms to balance the breadth and depth of detailed forensic analyses of damaged structures (in collaboration with the Texas Tech University National Wind Institute). This modeling will then be used to investigate tornado impacts on structures through the measurement of deflections and study of failure patterns. At the large scale, the project will investigate a methodology to detect "missing" tornadoes via multi-spectral image analysis to enhance tornado climatology in sparsely populated and forested areas (in collaboration with Western University in Canada). The research will advance the knowledge of complex tornado-structure interactions, estimation of tornado intensity, and detection of tornado occurrences via safe, reliable, efficient, and cutting-edge remote sensing technologies at spatial scales ranging from minute structural deformations to the overall tornado-path level. The research will provide validation methods for physical and numerical tornado load simulations, facilitate necessary adjustments to wind speed estimates in the Enhanced Fujita Scale, and explore optimization of high-resolution imaging platforms to most effectively advance the understanding of tornado effects on the built environment. Detection of "missing" tornadoes will enhance climatology studies, providing a basis to improve forecasts, warnings, and risk models.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.