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

RAPID：暴露于腐蚀环境的建筑物的结构评估：俄勒冈州威拉米特瀑布历史工业遗址蓝鹭造纸厂的数据收集

基本信息

批准号：
2228113
负责人：
Erzhuo Che
金额：
$ 11.99万
依托单位：
Oregon State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228113&HistoricalAwards=false
关键词：
RAPID Structural Assessments Buildings Exposed

项目摘要

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.

该快速响应研究资助 (RAPID) 项目将收集和分析历史建筑的易腐烂数据。位于俄勒冈州俄勒冈市威拉米特瀑布旁的蓝鹭造纸厂遗址有着非常有趣的历史，最近被大龙德联盟部落购买，旨在将瀑布恢复到自然状态并保护一些最古老的瀑布结构。该地点提供了一个独特的机会来进行快速调查，以收集和分析这些历史建筑的易腐烂数据，并开发腐蚀性环境中建筑评估领域的新知识。该工业场地包含多种结构类型（钢框架、混凝土框架、木框架、砖石墙和大体积混凝土墙），这些建筑历时 150 年建成，并采用了许多旧建筑中常见的建筑细节。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方法来分析和综合大数据集以及整合视觉观察和材料测试以量化结构恶化损坏。开发人工智能 (AI) 技术以使用激光雷达数据查找和量化结构系统中的损坏的挑战是需要在显示各种损坏状态的现有数据集上训练方法。从该站点收集的数据将提供与旧建筑常见结构部件相关的广泛培训数据集。开发此类用于快速识别和量化损害的人工智能技术将为自然灾害研究界带来变革，并将扩大从存档激光雷达数据集中学习的能力。收集到的数据集将可供研究人员用作算法开发中的高质量训练数据。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。