five

Detailed 3D point cloud data at Tumwata Village in Oregon City, OR (2021-2022)

收藏
DataCite Commons2025-06-02 更新2025-04-16 收录
下载链接:
https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3668/#detail-fb695b39-174b-431b-9f28-f290e1592ed9
下载链接
链接失效反馈
官方服务:
资源简介:
This Grant for Rapid Response Research (RAPID) project will collect and analyze perishable data on historical buildings. The Tumwata Village (formerly known as 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.
提供机构:
Designsafe-CI
创建时间:
2024-08-19
二维码
社区交流群
二维码
科研交流群
商业服务