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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
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    Filetype[PDF - 3.38 MB ]
File Language:
English
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