NCHRP - Guide for Pavement Friction

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Guide for Pavement Friction

Author: Houston, William N | Size: 2.67 MB | Format: PDF | Quality: Original preprint | Publisher: Transportation Research Board | Year: 2009 | pages: 257 | ISBN: -

Performance objectives for achieving durable bridge deck concrete and the properties of locally available concrete raw materials, particularly supplementary cementitious materials (SCMs) like fly ash, GGBFS and silica fume, vary by geographic region. Because of this variation, the optimum concrete mixture proportions for a given application must be determined by experiment. Since durability-related experimental programs investigating the performance of concrete mixtures are expensive and time-consuming, a methodology for designing and conducting an investigation using statistical experimental design concepts has been developed to efficiently identify the optimum concrete mixture proportions for a specific set of conditions. The approach implemented is based on fractional orthogonal experimental design, which supports modeling for a large number of factors (input variables) based on a minimum number of tests. The Methodology, presented in NCHRP Report 566: "Guidelines for Concrete Mixtures Containing Supplementary Cementitious Materials to Enhance Durability of Bridge Decks," consists of six steps: (1) definition of performance requirements, (2) selection of durable raw materials, (3) construction of an experimental design matrix, (4) testing of concrete mixtures, (5) analysis and empirical modeling to determine the Best Tested and Best Predicted Concretes, and (6) confirmation of predictions and selection of the Best Concrete. This Methodology is flexible and may be applied to a range of performance demands. It was developed to identify optimum contents of SCMs but is also able to select between sources of raw materials. A case study was conducted based on a hypothetical set of service conditions and using concrete raw materials from the Midwest. The performance predictions based on the case study experimental design were verified by confirmation testing. Finally, a computational tool (SEDOC) was developed to support the implementation of this Methodology.

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