RAPID/Collaborative Research: Performance of Low-Rise Large-Volume Buildings in Florida during 2018 Hurricane Michael

低层、大体积(LRLV)金属建筑是社区和国家复原力的关键组成部分，通常用作学校礼堂或体育馆、军用或民用飞机机库、配送中心、超市、教堂、工业和制造建筑以及保护高价值财产的储存设施。不幸的是，在飓风迈克尔于2018年10月10日作为4级飓风在佛罗里达州巴拿马城南部登陆后，其中许多建筑遭受了灾难性的倒塌或不可修复的破坏。NSF支持的结构极端事件侦察网络的初步评估发现，这些结构的破坏很常见，而且往往与周围建筑不成比例，这表明在了解这些结构上的动态荷载和/或结构在极端风事件期间对这些荷载的反应机制方面存在知识差距。这项快速反应研究补助金(RAPID)将支持现场部署，通过地面和机载激光雷达和摄影测量以及法医结构工程分析，以高分辨率3D点云的形式快速准确地捕获LRLV建筑的飓风后损坏状态。数据的后处理和分析将为这些建筑的风荷载和结构响应的高级计算模型提供信息，这最终将使设计更加安全和高效。通过更好地了解这些建筑在风荷载下的复杂动态行为，该项目的发现可以为未来的设计提供参考，以减少未来极端风事件中LRLV建筑故障的频率。现场调查和分析将培训本科生和研究生工程师法医工程方法，并提供可供工程教育界使用的高质量案例研究。从该奖项收集的数据将被存档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)数据仓库(https://www.DesignSafe-CI.org).在飓风期间，LRLV金属建筑物的尺寸可以超过湍流的整体长度尺度，在建筑物表面产生不连贯的阵风结构。这一现象对峰值结构力的影响，特别是对内部压力响应的影响，目前还没有很好的了解。该项目的目标是：1)在清理之前保存受迈克尔飓风影响的LRLV建筑的准确飓风后状况；2)评估LRLV建筑遭受的极端风力条件；3)对LRLV建筑故障的主要驱动因素进行初步评估；以及4)定位未来的研究工作，以进行先进的风荷载和结构分析模拟，以解决导致高故障率的关键知识空白。佛罗里达州的数据收集远足(3-5天)将使用多种传感技术来快速收集目标结构的详细结构信息，包括地面激光雷达和测量，以及基于无人机的激光雷达和图像。在将数据简化为3D模型后，分析过程将重点放在参数上，包括建筑体积、无保护洞口尺寸、相对于主风向的建筑方向、内部压力的共振、结构系统(重力和横向)、结构年龄和地震前状况。这项研究将解决飓风引起的风荷载对LRLV建筑的一个关键知识缺口，同时调查结构系统对这些荷载的响应。整体方法将促进对导致这些系统故障的现象的科学理解，并为未来事件提供更具弹性的建筑设计。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。

Low-rise, large-volume (LRLV) metal buildings are a critical component to community and national resilience, often functioning as school auditoriums or gymnasiums, military or civilian aircraft hangars, distribution centers, supermarkets, churches, industrial and manufacturing buildings, and storage facilities protecting high-value property. Unfortunately, many of these structures suffered catastrophic collapses or irreparable damage following Hurricane Michael, which made landfall south of Panama City, Florida on October 10, 2018, as a Category 4 hurricane. Preliminary assessments by the NSF-supported Structural Extreme Events Reconnaissance network found that damage to these structures was common and often disproportionate to surrounding buildings, indicating a knowledge gap in the understanding of the dynamic loads on these structures and/or the mechanisms of structural response to these loads during extreme wind events. This Grant for Rapid Response Research (RAPID) will support field deployments to quickly and precisely capture the post-hurricane damage state of LRLV buildings in the form of high-resolution 3D point clouds, by means of terrestrial and airborne LIDAR and photogrammetry, and forensic structural engineering analysis. Post-processing and analysis of the data will inform advanced computational models of wind load and structural response for these buildings, which will ultimately enable safer and more efficient designs. By enabling a better understanding of the complex dynamic behavior of these buildings under wind loads, the findings from this project can inform future designs to reduce the frequency of LRLV building failures in future extreme wind events. The field reconnaissance and analysis will train undergraduate and graduate engineers in forensic engineering methods and provide high quality case studies that can be used by the engineering education community. Data collected from this award will be archived in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-CI.org). During hurricane-force winds, the dimensions of LRLV metal buildings can exceed the integral length scales of the turbulent flow, producing incoherent gust structures over the surface of the building. The effects of this phenomenon on peak structural forces, and particularly the internal pressure response, are not well understood. The goals of this project are to: 1) preserve the precise post-hurricane condition of LRLV buildings impacted by Hurricane Michael prior to cleanup, 2) assess the extreme wind conditions to which the LRLV buildings were subjected, 3) conduct preliminary assessments of the primary drivers of LRLV building failures, and 4) position future research efforts for conducting advanced wind load and structural analysis simulations to address the key knowledge gaps that have led to large failure rates. Data gathering excursions (3-5 days) in Florida will use multiple sensing technologies to quickly gather detailed structural information on target structures including terrestrial LIDAR and measurements, and drone-based LIDAR and imagery. After reducing the data into 3D models, the analysis process will focus on parameters including building volume, unprotected opening size, building orientation with respect to primary wind direction, resonance of internal pressure, structural system (gravity and lateral), structure age, and pre-event condition. This study will address a key knowledge gap in hurricane-induced wind loads on LRLV buildings while simultaneously investigating the structural system response to these loads. The holistic approach will advance scientific understanding of the phenomena driving the failures of these systems and inform more resilient building designs for future events.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.