Spatiotemporal Dimensions of Network Density-based Clustering for Water Pipe Maintenance

Abstract

In the U.S., many drinking water assets operate beyond their designed lifetimes, and constrained resources necessitate optimizing repair and replacement. For pipe infrastructure, clustering tools can characterize their susceptibility to failure by identifying relationships among descriptive or measured features. In these complex systems, algorithmic learning approaches can provide a first insight before expert knowledge is applied, reducing time and labor. The state-of-the-art techniques often rely upon static characteristics. In this work, pipe maintenance records are analyzed through the network OPTICS (“Ordering Points To Identify the Clustering Structure”), which forms a hierarchal density-based clustering structure. The study compares and extends OPTICS to a temporal context; exploring the evolution of clustering structure provides additional insight. The findings suggest this spatiotemporal approach is applicable for improved asset management.