Development of a Performance Index for Stormwater Pipe Infrastructure using Fuzzy Inference Method

Stormwater pipe infrastructure collects and conveys surface runoff resulting from rainfall or snowmelt to nearby streams. Traditionally, stormwater pipe systems were integrated with wastewater infrastructure through a combined sewer system. Many of these systems are being separated due to the impact of environmental laws and regulations; and the same factors have led to the creation of stormwater utilities. However, in the current ASCE Infrastructure Report Card, stormwater infrastructure is considered a sub-category of wastewater infrastructure. Stormwater infrastructure has always lacked attention compared to water and wastewater infrastructure. However, this notion has begun to shift, as aging stormwater pipes coupled with changes in climatic patterns and urban landscapes makes stormwater infrastructure more complex to manage. These changes and lack of needed maintenance has resulted in increased rates of deterioration and capacity. Stormwater utility managers have limited resources and funds to manage their pipe system. To effectively make decisions on allocating limited resources and funds, a utility should be able to understand and assess the performance of its pipe system. There is no standard rating system or comprehensive list of performance parameters for stormwater pipe infrastructure. Previous research has identified performance parameters affecting stormwater pipes and developed a performance index using a weighted factor method. However, the weighted performance index model does not capture interdependencies between performance parameters. This research developed a comprehensive list of parameters affecting stormwater pipe performance. This research also developed a performance index using fuzzy inference method to capture interdependencies among parameters. The performance index was evaluated and validated with the pipe ratings provided by one stormwater utility to document its effectiveness in real world conditions.