RAPID: Structural Assessments for Buildings Exposed to a Corrosive Environment: Data Collection at the Blue Heron Paper Mill, Historical Industrial Site at Willamette Falls, Oregon

这个快速反应研究拨款(RAPID)项目将收集和分析有关历史建筑的易腐烂数据。位于俄勒冈州威拉米特瀑布附近的蓝鹭造纸厂遗址有着非常有趣的历史，最近被大隆德邦联部落购买，目的是将瀑布恢复到自然状态，并保护一些最古老的建筑。该网站提供了一个独特的机会，可以进行快速调查，收集和分析这些历史建筑的易腐烂数据，并在腐蚀性环境下的建筑评估领域发展新的知识。这个工业场地包含各种各样的结构类型(钢框架、混凝土框架、木框架、砖墙和大体积混凝土墙)，这些结构建于150年前，并采用了许多在旧结构中常见的建筑细节。收集的数据和研究结果将适用于全国沿海社区的许多建筑物。从这些建筑物收集的激光雷达数据集将支持开发新的方法来分析和合成大数据集，并将视觉观测和材料测试相结合来量化结构劣化破坏。在开发人工智能(AI)技术以使用激光雷达数据发现和量化结构系统中的损伤时，面临的挑战是需要在显示广泛损伤状态的现有数据集上训练方法。将从该网站收集的数据将提供与旧建筑常见的结构部件相关的广泛培训数据集。开发这种用于快速识别和量化损害的人工智能技术将对自然灾害研究界产生变革，并将扩大从存档的激光雷达数据集学习的能力。收集的数据集将提供给研究人员，作为算法开发中的高质量培训数据。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。

This Grant for Rapid Response Research (RAPID) project will collect and analyze perishable data on historical buildings. The Blue Heron Paper Mill Site located by the Willamette Falls in Oregon City, Oregon, has a very intriguing history and was recently purchased by the Confederated Tribes of Grand Ronde with the intent to restore the falls to their natural state and preserve some of the oldest structures. The site presents a unique opportunity to perform rapid investigations to collect and analyze perishable data on these historical buildings and develop new knowledge in the area of building assessments in corrosive environments. This industrial site contains a wide range of structure types (steel frames, concrete frames, timber frames, masonry walls and massive concrete walls) that were built over a period of 150 years and that employ many construction details that are common in older structures. The data collected and the results of the research will be applicable to many buildings in coastal communities throughout the country.Lidar data sets collected from these buildings will support the development of new methods to analyze and synthesize large data sets as well as integrate visual observations and material testing to quantify structural deterioration damages. The challenge in developing artificial intelligence (AI) technologies to find and quantify damage in structural systems using lidar data is the need to train the methods on existing data sets that show a wide range of damage states. The data to be collected from this site will provide an extensive training data set relevant to structural components common to older buildings. Development of such AI technologies for fast identification and quantification of damage would be transformative for the natural hazards research community and would expand the ability to learn from archived lidar datasets. The collected dataset will be available to researchers to serve as high quality training data in algorithm development.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.