PFI TT: Intelligent quality assurance and integration tool for sewer inspection data
PFI TT: Intelligent quality assurance and integration tool for sewer inspection data
批准号:
2141184
负责人:
Yongwei Shan
金额:
$25.0万
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:
未结题
起止时间:
2022-02-01 至 2025-01-31
中文摘要
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英文摘要
The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is conduct quality monitoring of sewers for clear public benefit. Current practices in handling data incompatibility and quality issues are manual and time-consuming. In addition, these practices are customized for individual projects, a process hard to adapt to different projects. Moreover, the sewer inspection data remains fragmented within the utilities due to the lack of proper data management solutions, which makes data less accessible for asset management. The envisioned technology will help to combine sewer inspection data with standard PACP (Pipeline Assessment Certification Program) format, ensure data quality, and integrate historical data for easy and secured access so that cost-effective asset management plans can be developed based on true conditions. Each utility could save up to 80% of the time (on the order of months) for manual data quality assurance. With accurate and accessible sewer condition data, it is anticipated that at least 20% (approximately $600 million) of the annual spending on pipe rehabilitation can be saved for the entire industry. More importantly, improved sewer infrastructure condition can greatly reduce gastrointestinal (GI) illness emergency room visits induced by sanitary sewer overflow events. The proposed project is to 1) develop an artificial intelligence (AI)-enabled algorithm to resolve data incompatibility issues to maintain data fidelity and facilitate data exchange among different software platforms; 2) establish a holistic AI-enabled methodological framework to automatically detect and treat data quality issues, including data records inconsistency, incompleteness, and duplicates, caused by the interruption of inspection operations due to pipe defects or human errors; and 3) standardize municipal utilities’ practices in managing sewer condition data using blockchain-enabled technologies. This PFI-TT project will tailor the emerging AI/machine learning and blockchain technologies to develop an efficient data format translation algorithm, effective data quality assurance framework, and a blockchain-enabled sewer data management tool, with the objective to save engineers’ effort in data processing and render them more time for engineering decision making.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(6)
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DOI:
10.1080/24725854.2023.2193257
发表时间:
2023-03
期刊:
IISE Transactions
影响因子:
2.6
作者:
[Yuxuan Li;Zhangyue Shi;Chenang Liu]
通讯作者:
Yuxuan Li;Zhangyue Shi;Chenang Liu
Developing a Data Quality Evaluation Framework for Sewer Inspection Data
开发下水道检查数据的数据质量评估框架
DOI:
10.3390/w15112043
发表时间:
2023
期刊:
Water
影响因子:
3.4
作者:
[Khaleghian, Hossein, Shan, Yongwei]
通讯作者:
Shan, Yongwei
A Generative Adversarial Network (GAN)-Assisted Data Quality Monitoring Approach for Out-of-Distribution Detection of High Dimensional Data
用于高维数据分布外检测的生成对抗网络 (GAN) 辅助数据质量监控方法
DOI:
--
发表时间:
2023
期刊:
IISE
影响因子:
--
作者:
[Slater, K., Wang, Y., Shan, Y., Liu, C.]
通讯作者:
Liu, C.
DOI:
10.1109/case49997.2022.9926629
发表时间:
2022-08
期刊:
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)
影响因子:
--
作者:
[Yuxuan Li;Ayse Dogan;Chenang Liu]
通讯作者:
Yuxuan Li;Ayse Dogan;Chenang Liu
DOI:
10.1016/j.jmsy.2022.04.010
发表时间:
2022-07-01
期刊:
JOURNAL OF MANUFACTURING SYSTEMS
影响因子:
12.1
作者:
[Liu,Chenang, Tian,Wenmeng, Kan,Chen]
通讯作者:
Kan,Chen
共 6 条
I-Corps: Data Quality Assurance and Inventory Tool (One-Voice) for Sewer Inspection Data
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批准号:1849023
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项目类别:Standard Grant
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资助金额:$5.0万
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财政年份:2018
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负责人:Yongwei Shan
-
依托单位:
国内基金
海外基金
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