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Title:

USING STATISTICAL METHODS TO OPTIMIZE HIGH-STRENGTH CONCRETE PERFORMANCE

Accession Number:

00605615

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/isbn/0309050634

Abstract:

Sound experimental design principles, rather than three-point curves, can be employed to determine high-strength concrete mixture proportions that maximize performance while minimizing cost. Important issues on experimental design and empirical modeling are discussed. Graphical methods illustrate how high compressive strengths can be obtained for a wide range of costs--and how to get the most for your money. An example is given using a classical experimental design. It greatly reduces the number of concrete mixes that otherwise would have been necessary to obtain information on five variables that change simultaneously. This experiment illuminates the effect of the silica fume addition percentage and the high-range water reducer dosage on 28-day compressive strength and raw material cost. Contour plots are provided to demonstrate the usefulness of a statistical approach.

Supplemental Notes:

This paper appears in Transportation Research Board Record No. 1284, Cement, Admixtures, and Concrete 1990. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved

Monograph Accession #:

01411065

Authors:

Luciano, John J
Bobrowski, Gregory S

Pagination:

p. 60-69

Publication Date:

1990

Serial:

Transportation Research Record

Issue Number: 1284
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

0309050634

Features:

Figures (8) ; References (5) ; Tables (3)

Old TRIS Terms:

Subject Areas:

Data and Information Technology; Finance; Highways; Materials; I32: Concrete

Files:

TRIS, TRB, ATRI

Created Date:

Mar 31 1991 12:00AM

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