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38 csengineermag.com august 2019 Municipal and investor-owned utilities often rely on subjective criteria to determine which water mains in their system need replacing. To add to the inaccuracy, the decisions are often made when opportunity strikes, like where street paving will happen in the near future. This process leads to inefficient spending of limited resource dollars, leav- ing utilities extremely vulnerable to financial and structural risk. This is unwanted pressure for utilities, as they're facing increased state and federal regulatory pressures to create efficient, comprehensive as- set management plans. Asset management practices combined with Artificial Intelligence, specifically Machine Learning, provide a new method for assessing the condition of buried water mains. Specifically, AI and Machine Learn- ing allow utilities to properly align maintenance, rehabilitation, and replacement strategies to better allocate limited resources. Digital Condition Assessment Using AI and Machine Learning Machine Learning-based condition assessment tools are relatively new, but are now commercially available. Machine Learning, a category of AI, provides computers the ability to learn without being programmed. It uses automated and iterative models to learn about patterns in big data, detecting anomalies and identifying a structure that may be new and previously unknown. Through this capability, Machine Learning supports a new way of aligning maintenance and, in turn, asset man- agement planning by creating more accurate analysis despite using less data. Fracta offers a fast, accurate and affordable digital condition assess- ment solution to predict the Likelihood of Failure (LOF) of water distribution mains. Fracta is also fully integrated with Esri's market-leading ArcGIS soft- ware. The integrated Fracta and Esri platforms provide an architectural framework to readily integrate with other important software appli- cations used by water utilities such as Enterprise Asset Management (EAM), Computerized Maintenance Management Systems (CMMS), and Hydraulic Modeling. Many utilities, public utility commissions, and consulting engineers still view the Fracta Machine Learning and GIS approach as a "black box," as happens with any new technology, with the primary concern being accuracy. Balanced Accuracy for LOF Predictions The data that comes out of a Machine Learning model is only as ac- curate as the data that goes into the model (i.e., "garbage in, garbage out"). Fracta uses a Supervised Machine Learning model with input variables (x) and an output variable (Y). The model uses an algorithm to learn the mapping function from the input to the output, Y = f(x). The goal is to approximate the mapping function so well that for new input data (x) the algorithm can predict the output variables (Y) for that data. It is supervised learning because the process of algorithm learning from the training dataset is similar to a teacher supervising the learning process. By using a training data set with the correct answers known, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance. Generally, 80% of the historical data are used to train, and 20% are used to validate the Machine Learn- ing model. Assessing a water distribution system with a Machine Learning model requires an understanding of both failures and non-failures. True Posi- tive Rate (TPR) measures the proportion of correctly identified real positives. In the Fracta solution, that means correctly identifying the high probability failures. True Negative Rate (TNR) measures the proportion of correctly identified actual negatives. This methodology focuses on correctly identifying the segments that have low LOF. The accuracy is a balance between high LOF and low LOF results. Because the training and validation of the model are based on 80% of the data, the maximum Balanced Accuracy that it can achieve is 80%. Predicting Output Variables The Accuracy and Business Value of Machine Learning to Assess Water Main Infrastructure By Doug Hatler

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