Underground Sewer Networks Renewal Complexity Assessment and Trenchless Technology: A Bayesian Belief Network and GIS Framework

Abstract

Significant investment is required to upgrade deteriorating underground sewer networks. Sewer failure and the subsequent rehabilitation process can have economic, social, and environmental impacts. It can disrupt critical urban function and adjacent utilities, such as telecom, electric, gas, and water supply lines. This paper identifies 48 indicators to assess the renewal complexity and the failure consequence of buried sewer pipes. A Bayesian belief network (BBN) model is used to capture dependencies among indicators, quantify uncertainty, and update belief when new information becomes available. Geographic information system (GIS) applications are used to collect and process model input data as well as visualize analysis results. The framework can identify locations where trenchless rehabilitation may be cost effective. Finally, the proposed method is demonstrated on a storm sewer network in the city of Vernon, Canada.