Penn State University


Kaushik

AAAI Conferences

Ranking pipes according to their burst likelihood can help a water utility triage its proactive maintenance budget effectively. In the research literature, data-driven approaches have been used recently to predict pipe bursts. Such approaches make use of static features of the individual pipes such as diameter,length, and material to estimate burst likelihood for the next year by learning over past historical data. The burst likelihood of a pipe also depends on dynamic features such as its pressure and flow. Existing works ignore dynamic features because the features need to be measured or are difficult to obtain accurately using a well-calibrated hydraulic model. We complement prior data-driven approaches by proposing a methodology to approximately estimate the dynamic features of individual pipes from readily available network structure and other data. We study the error introduced by our approximation on an academic benchmark water network with ground truth. Using a real-world pipe burst dataset obtained from a European water utility for multiple years, we show that our approximate dynamic features improve the ability of machine learning classifiers to predict pipe bursts. The performance (as measured by the percentage of future bursts predicted) of the best forming classifier improves by nearly 50% through these dynamic features.


Cracks Under Pressure? Burst Prediction in Water Networks Using Dynamic Metrics

AAAI Conferences

Ranking pipes according to their burst likelihood can help a water utility triage its proactive maintenance budget effectively. In the research literature, data-driven approaches have been used recently to predict pipe bursts. Such approaches make use of static features of the individual pipes such as diameter,length, and material to estimate burst likelihood for the next year by learning over past historical data. The burst likelihood of a pipe also depends on dynamic features such as its pressure and flow. Existing works ignore dynamic features because the features need to be measured or are difficult to obtain accurately using a well-calibrated hydraulic model. We complement prior data-driven approaches by proposing a methodology to approximately estimate the dynamic features of individual pipes from readily available network structure and other data. We study the error introduced by our approximation on an academic benchmark water network with ground truth. Using a real-world pipe burst dataset obtained from a European water utility for multiple years, we show that our approximate dynamic features improve the ability of machine learning classifiers to predict pipe bursts. The performance (as measured by the percentage of future bursts predicted) of the best forming classifier improves by nearly 50% through these dynamic features.


Semantics for Digital Engineering Archives Supporting Engineering Design Education

AI Magazine

This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop Digital Engineering Archives. This work is part of an ongoing, multiuniversity, effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, workflows and processes. With these formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability.


Semantics for Digital Engineering Archives Supporting Engineering Design Education

AI Magazine

This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop Digital Engineering Archives. This work is part of an ongoing, multiuniversity, effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, workflows and processes. With these formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability. The article presents examples of how the techniques can be used to encode specific engineering information packages and workflows. These techniques are being integrated into a semantic wiki that supports the CIBER-U engineering education activities across nine universities and involving over 3500 students since 2006.