Click for next page ( 22

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 21
21 Table 19. Coefficients of multiple determinations (R2) between the rankings of evaluators. Evaluator I II III IV V I 1 0.57 0.58 0.37 0.3 II 1 0.9 0.41 0.57 Angularity III 1 0.41 0.46 IV 1 0.41 V 1 Evaluator I II III IV V I 1 0.91 0.92 0.89 0.82 II 1 0.95 0.79 0.84 Texture III 1 0.84 0.82 IV 1 0.74 V 1 Evaluator I II III IV V I 1 0.77 0.80 0.76 0.59 Surface II 1 0.95 0.69 0.79 Irregularity III 1 0.70 0.72 IV 1 0.71 V 1 in central laboratories and field laboratories, and applicability bility in specifying the objectives, ranking criteria, and relative of test method to measure different aggregate types and sizes. importance or priorities of the different criteria elements. Table 22 lists this information. Background on the Process Ranking of Test Methods Using AHP is a decision making process that transforms complex the Analytical Hierarchy Process decision making into a series of one-on-one comparisons and The Analytical Hierarchy Process (AHP) has been adapted to then combines the results to help arrive at the best, most jus- rank the test methods according to their repeatability, repro- tified decision. The process incorporates both subjective and ducibility, accuracy, cost, and operational characteristics. The objective evaluation measures such that the bias in decision process is presented as computational software to expedite making is reduced and has been used in several applications conducting the calculations and provides the user with flexi- dealing with the selection of alternatives, investment distri- bution, and energy allocation (21). Table 20. Average visual rankings of coarse The AHP method is based on decomposing the goal into aggregates by evaluators. its component parts, moving from the general to the specific (i.e., proceeding from the goal to objectives and criteria sub- Aggregate Texture Surface Irregularity CA-1 1.6 1.8 objectives down to the alternative courses of action). After CA-2 4.4 4.2 structuring the hierarchy of all criteria, the next step is to CA-3 6.8 8.6 assign a relative weight to each criterion. Weights are assigned CA-4 7.4 8.1 CA-5 12.8 9.8 CA-6 5.2 5.8 Table 21. Visual ranking of fine aggregate angularity CA-7 5.8 6.0 by evaluators. CA-8 1.4 1.2 CA-9 11.4 9.9 Aggregate Visual Ranking CA-10 11.6 10.4 FA-1 2 CA-11 9.0 10.3 FA-2 4 CA-12 3.6 4.2 FA-5 5 CA-13 10.0 10.7 FA-6 1 FA-9 3 Notes: 1- CA= coarse aggregate; 2- Higher rank is associated with higher angularity and/or texture. Notes: 1- FA= fine aggregate; 2- Higher rank is associated with higher angularity.