The Central Contra Costa Sanitary District (the District) collects and treats wastewater for 410,000 people in various San Francisco Bay Area communities. The District maintains more than 1,400 miles of sewer pipelines, ranging from 4 inches to 106 inches in diameter. There are several
pipe materials of varying age and condition. A case study is presented of a project to develop a statistical model to forecast the future condition of selected small-diameter (4- inch to 10-inch) sewer pipes and to estimate necessary rehabilitation capital costs to assist the District in Capital
Improvement Program development. A condition model uses detailed knowledge of a small portion of a system to predict the condition of the entire system. The detailed knowledge consists of pipe attribute data such as condition, age, material, size, and location for all pipes within the system.
The condition of a small portion of the system is determined from detailed closed-circuit television (CCTV) inspection. A non-linear model that is linear in its parameters was developed to relate the “known” condition of selected pipes to the attributes of those pipes. The resulting
equations were used to predict the future condition of the entire system. Forecast costs of rehabilitation for predicted poor condition pipes are estimated using a costing model that integrates the predicted condition, material, size, location, and unit cost estimates for various rehabilitation
strategies. The costing model uses current bid tabulation data from the local area to provide realistic program cost estimates. The pipe inventory, CCTV condition data, the predictive modeling equations, and the costing model are integrated into a single Microsoft Access database. This
database forecasts the condition and estimated cost of rehabilitation for each pipe in the District's system of small diameter pipes for a user defined planning horizon. This study demonstrated that condition modeling provides an efficient means to plan future expenditures and staffing
requirements. Other models of infrastructure condition are described and compared to the results of this effort. Lastly, recommendations are made for further developing this technique for Capital Improvement Program development and how the District is currently using the model.
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