PFI TT: Intelligent quality assurance and integration tool for sewer inspection data

Oklahoma State University, Stillwater OK

Investigators

Abstract

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.