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: Structural Assessments for Buildings Exposed to a Corrosive Environment: Data Collection at the Blue Heron Paper Mill, Historical Industrial Site at Willamette Falls, Oregon
批准号:
2228113
负责人:
Erzhuo Che
金额:
$11.99万
依托单位:
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
项目状态:
已结题
起止时间:
2022-07-01 至 2024-06-30
中文摘要
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英文摘要
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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1061/jccee5.cpeng-4979
发表时间:
2023-07
期刊:
J. Comput. Civ. Eng.
影响因子:
--
作者:
[E. Che;M. Olsen]
通讯作者:
E. Che;M. Olsen
国内基金
海外基金
Understanding structural evolution of galaxies with machine learning
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批准号:
-
项目类别:省市级项目
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资助金额:10.0万元
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批准年份:2022
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负责人:Nicola Rosario Napolitano
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依托单位: