Abstract

FRP composites are finding increasing use in the civilian applications such as highways, bridges, pipes etc. This analysis focuses on the FRP piping systems used in the Petrochemical industries under extreme conditions. Due to the high operational and maintenance costs involved with pipes made from traditional materials, there is a need to develop a smart inspection system that replaces or eliminates the traditional inspection and maintenance techniques, providing continuous and reliable monitoring of the structure. Smart FRP pipes have an embedded smart sensor system incorporated in them, providing continuous and reliable monitoring of the pipe structure. This helps in preventing catastrophic failure of pipes thereby reducing the costs involved with the pipe failure. Smart FRP systems have a very high initial investment cost, and therefore a cost comparison model is needed in order to justify their use against traditionally used materials. A Life Cycle Cost (LCC) comparison model has been developed in this paper, which shows that despite high initial investment costs, large savings could be made in the operational and maintenance costs with the use of Smart FRP pipes. This cost model Calculates the life cycle costs of Steel, FRP and Smart FRP pipes, and determines the alternative with the lowest life cycle cost. To deal with an uncertainty associated with the cost factors, used in calculating the LCC of the three alternatives, an uncertainty analysis has been performed. An computer spreadsheet has been programmed in order to perform the LCC and Uncertainty Analysis. This analysis has laid down the basic foundations for a larger cost model, wherein; several other alternatives materials and factors could be included. This would further help in widening the scope of use of Smart Structures in various industries. Certain aspects of the data used in this analysis may be disputable, however for the purpose of modeling and procedural demonstration, the gathered and available information was used to perform our analysis. Therefore, use of this data outside of the scope and context of this report is not warranted.

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