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Alternate bidding strategies for asphalt and concrete pavement projects utilizing life cycle cost analysis (LCCA).

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English


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  • Abstract:
    Recent changes in pavement materials costs have impacted the competitive environment relative to the ; determination of the most cost effective pavement structure for a specific highway project. In response, State ; highway agencies have renewed their interest in using alternate pavement type bidding procedures to determine ; the appropriate pavement type. This research project developed an alternative bidding procedure for pavement ; projects. Not all highway projects are candidates for alternate bidding. A step by step procedure can help identify ; candidate highway projects for alternate bidding. In addition to developing a deterministic life cycle cost (LCC) ; analysis model using I40 interstate pavement data, the research team also developed a novel approach, namely, ; realistic LCCA model. This approach uses the association rule mining technique to find and probabilistically ; organize various treatment patterns applied to pavements in the past. A case study conducted in this project ; revealed that this realistic LCCA model is more reliable and is expected to provide better predictions on LCC. ; Moreover, since this approach is based upon the historical performance of pavements, it is expected to be easier to ; draw consensus between the asphalt and concrete industries in calculating life cycle costs. A spread sheet based ; program is developed for ODOT to calculate LCC for flexible and rigid pavement alternatives. This program ; enables ODOT to enter project information such as project scope, analysis period, estimated unit prices, ; miscellaneous items, mobilization, and construction added costs for asphalt and concrete pavement projects and ; obtain deterministic and realistic LCCs of both pavement types as well as LCC factor which should be used during ; the alternate bidding process.
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    urn:sha256:f552520a27b8aa159f46c8787eca0cf27b7fc2742adbe5dddc2222247c901032
File Language:
English
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