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 23
23 in existing practices. Sustainable practices include increased Table 1. Grouping structure of the alternative- use of recycled materials, industrial by-products, and local preference screening matrix. materials; decreased use of energy-intensive materials and General Version Example construction processes; improvements in material produc- Group A Economic factors tion and processes; techniques that preserve or increase the Factor A1 Initial costs Factor A2 Future rehabilitation costs longevity of pavements; and eco-friendly design alternatives. ... ... Life-cycle assessment (LCA) methods typically are used in Factor An User costs Group B Construction factors evaluating the environmental impact of materials, equip- Factor B1 Continuity of adjacent lanes ment, and processes used in pavements. The LCA-based Factor B2 Traffic during construction environmental impacts can be incorporated qualitatively or ... ... Factor Bn Lane geometrics quantitatively in the pavement-type selection process. Group C Local factors Factor C1 Availability of local materials Factor C2 District/local preferences 5.4 Weighing of Economic and ... ... Factor Cn Stimulation of competition Noneconomic Factors Using Group D Other factors Alternative-Preference Factor D1 Noise Factor D2 Subgrade soils Screening Matrix ... ... Factor Dn Experimental features The pavement-type selection process should weigh both economic and noneconomic factors to ensure that the agency goals and policies are incorporated in decision making. An Table 2. Group weights of the screening matrix. alternative-preference screening matrix is suggested for this General Version Example purpose. The screening matrix is a decision support tool that is Group Weight Group Weight A WA% Economic factors 50% designed to help agencies determine whether there are advan- B W B% Construction factors 25% C W C% Local factors 10% tages in selecting one alternative over others and whether these D WD% Other factors 15% alternatives should be evaluated more closely. Total score = Total score = 100% 100% The following sections describe how to set up the screening Note: W = weight. matrix and evaluate the results obtained. Appendix A illus- trates the application of the screening matrix and includes an example of its use. tors and groups may vary from project to project within an agency. Step 1: Identify and Group Evaluation Factors Step 2: Assign Group and Individual Factor Weights The economic and noneconomic factors that have a Next, weights must be assigned to each of the factor potential impact on the pavement-type selection groups and each factor within a group to reflect their process for a given project are identified and grouped. importance to the pavement-type selection process for The factors identified in Sections 5.2 and 5.3 are sug- a given project. Table 2 and Table 3 illustrate the group gested. A suggested grouping structure and sample and factor weighing scheme, respectively. The factor factors are illustrated in Table 1. The factor groups groups and factors within a group can be assigned could include economic factors, construction fac- equal or unequal weights, but the sum of all group tors, local factors, maintenance factors, traffic and weights and all the factor weights within each group safety factors, environmental factors, and others. must equal 100 percent. Agencies are expected to modify the contents of Step 3: Assign Preference Rating of Individual Factors Table 1 as necessary to best suit their goals, expecta- To facilitate a comparative evaluation of alternatives, tions, and project requirements. The evaluation fac- the evaluation factors are assigned with preference Table 3. Factor weights of the screening matrix. General Version Example Group A Weight Economic Factors Weight A1 WA1% Initial costs 30% A2 WA2% Future rehabilitation costs 25% A3 WA3% Road-user costs 20% An WAn% Future maintenance costs 25% Group total = Group total = 100% 100%
OCR for page 23
24 Table 4. Sample rating guidelines for the alternative-preference screening matrix. Factor Low Medium High Initial costs Cost >10% Cost >5% and <10% Cost within 5% Life-cycle costs Cost >20% Cost >10% and <20% Cost within 10% User costs User cost >20% User cost >10% and <20% User cost within 10% Future rehabilitation costs Cost >10% Cost >5% and <10% Cost within 5% Future maintenance costs Cost >10% Cost >5% and <10% Cost within 5% Significant complexity Moderate complexity to Roadway/lane geometrics Easy to accommodate to accommodate accommodate Continuity of adjacent Some significant issues Significant issues No significant issues pavements possible Continuity of adjacent Some significant issues Significant issues No significant issues lanes possible Availability of local Lack of local Some experience Commonly used materials and experience experience Very difficult to Somewhat difficult to Traffic during construction Easy to accommodate accommodate accommodate Much higher noise Some increased noise No difference in noise Noise likely possible generated Significant issues for Some issues possible for No significant issues Subgrade soils construction construction for construction District/local preference No preference Some preference Significant preference Significant issues Some issues related to Safety considerations related to safety Better safety features safety features features Conservation of Much higher materials/ Somewhat higher No significant materials/energy energy use materials/energy use difference Very few capable Common experience Stimulation of competition Some experience contractors for each Little to no local Common experience Maintenance capability Some experience experience for each Very difficult to Somewhat difficult to Future needs Easy to accommodate accommodate accommodate National but no local New and unproven Experimental features Common technology experience technology ratings using predetermined criteria. The purpose of Initial Cost factor is assigned a preference rating of the ratings is to quantify the relative advantages and "high" when the initial cost value of an alternative disadvantages among the alternatives for each evalu- is within a 5 percent difference of the lowest values ation factor. When an alternative offers significant of all candidates or "low" if the initial cost difference advantages associated with a given evaluation factor, of the alternative exceeds 10 percent of the lowest then the alternative is rated with a high preference for value. that factor. Step 4: Score Pavement-Type Alternatives The rating scheme can be discrete or continuous. Upon assigning preference ratings, the numerical While a discrete rating scheme is simple to use, a con- weighted scores of evaluation factors and groups are tinuous rating scheme provides more flexibility for calculated for each alternative. Ratings of "low," users. As with factors, groups, and weights, it is pro- "medium," and "high," if used, should be converted posed that the agencies develop their own rating guide- to numerical scores. Table 5 presents example crite- lines that reflect their goals and expectations. As a first ria for converting these ratings to a numerical scale. step, each agency should ask decision makers about the factors they currently use in making pavement-type decisions and what additional factors should be con- Table 5. Example criteria for sidered in the process. Test runs should be made on preference rating. several older projects to determine if the proposed Preference Rating Numerical Score screening process results in acceptable pavement selec- No difference 0% tions. The pavement-type selection committee can Low 20% Medium low 40% help establish these guidelines for the agency's use. Medium 60% Table 4 provides sample guidelines on rating indi- Medium high 80% vidual factors on a discrete scale. For example, the High 100%
OCR for page 23
25 Table 6. Example of the calculation of weighing scores for individual factors. Individual Preference Numerical Weighted Economic Factors Factor Rating Rating Score Weight Initial costs 30% Medium 60% 18.0% Future rehab costs 25% High 100% 25.0% User costs 20% Low 20% 4.0% Future maintenance 25% Medium- 40% 10.0% costs low Total unweighted score for Economic Factors 57% For a given alternative, the numerical scores of each Agencies should determine their own criteria to evaluation factor are multiplied by their correspon- interpret the screening-matrix results. An agency can ding factor weights to calculate the weighted scores of develop a threshold value to determine how different factors. The sum of weighted scores of factors within the alternatives are. For instance, if the difference in each group is the unweighted score of that group. The the final scores of two alternatives is more than 10, the example in Table 6 calculates the weighted score for alternative with the higher score can be selected as the individual factors within the Economic Factors group preferred one. and the unweighted score for that group. The weighted Recognizing that the project goals and the choice of group scores are then calculated by multiplying their feasible alternatives are unique to each project, this unweighted score by their corresponding group weights guide recommends the application of informed judg- (see Table 7). The sum of weighted group scores is the ment and agency experience in the selection process, total score for that alternative; it should not exceed with or without a threshold criterion in place. The 100 percent. screening matrix provides a systematic framework Step 5: Interpret Results for practical decision making by setting "musts" and Based on the final scores of alternatives, the "best pos- "wants" of an ideal choice for the project, exploring sible" pavement-type alternatives are selected. and prioritizing alternatives based on their strengths When the final score of an alternative is higher than and weaknesses, and choosing the most-preferred that of other candidates, the alternative with the high- alternative(s). est score may be much better suited than others. How- Table 8 provides a template of the screening ever, when the final scores of multiple alternatives are matrix. Users can add or eliminate any number of comparable, any of these alternatives could be selected. alternatives, groups, and individual factors within a Such cases are well suited for alternate bidding. If no group, as appropriate. Appendix A illustrates the alternative appears to be satisfactory, further investi- application of the alternative-preference screening gation is needed. matrix. Table 7. Example of the calculation of weighted group scores. Group Unweighted Group Weighted Group Group Weight Score Score Economic factors 50% 57% 28.5% Construction factors 25% 45% 11.3% Local factors 10% 25% 2.5% Other factors 15% 15% 2.3% Total score of the matrix 44.6%
OCR for page 23
26 Table 8. Alternative-preference screening-matrix worksheet. Factor Alternative 1 (Alt1) Alternative 2 (Alt2) Factor Weight Rating Weighted Score Rating Weighted Score Group A Factor A1 WA1 RA1-Alt1 WA1*RA1-Alt1 RA1-Alt2 WA1 * RA1-Alt2 Factor A2 WA2 RA2-Alt1 WA2 *RA2-Alt1 RA2-Alt2 WA2 *RA2-Alt2 Factor A3 WA3 RA3-Alt1 WA3 *RA3-Alt1 RA3-Alt2 WA3 *RA3-Alt2 Factor An WAN RAN-Alt1 WAN *RAN-Alt1 RAN-Alt2 WAn *RAN-Alt2 Group A unweighted total 100% (WAi*RAi-Alt1) (WAi * RAi-Alt2) Group B Factor B1 WB1 RB1-Alt1 WB1*RB1-Alt1 RB1-Alt2 WB1*RB1-Alt2 Factor B2 WB2 RB2-Alt1 WB2 *RB2-Alt1 RB2-Alt2 WB2 *RB2-Alt2 Factor B3 WB3 RB3-Alt1 WB3 *RB3-Alt1 RB3-Alt2 WB3 *RB3-Alt2 Factor Bn WBN RBN-Alt1 WBN *RBN-Alt1 RBN-Alt2 WBn *RBN-Alt2 Group B unweighted total 100% (WBi*RBi-Alt1) (WBi*RBi-Alt2) Group C Factor C1 WC1 RC1-Alt1 WC1*RC1-Alt1 RC1-Alt2 WC1*RC1-Alt2 Factor C2 WC2 RC2-Alt1 WC2 *RC2-Alt1 RC2-Alt2 WC2 *RC2-Alt2 Factor C3 WC3 RC3-Alt1 WC3 *RC3-Alt1 RC3-Alt2 WC3 *RC3-Alt2 Factor Cn WCN RCN-Alt1 WCN *RCN-Alt1 RCN-Alt2 WCn*RCN-Alt2 Group C unweighted total 100% (WCi*RCi-Alt1) (WCi*RCi-Alt2) Group D Factor D1 WD1 RD1-Alt1 WD1*RD1-Alt1 RD1-Alt2 WD1*RD1-Alt2 Factor D2 WD2 RD2-Alt1 WD2 *RD2-Alt1 RD2-Alt2 WD2 *RD2-Alt2 Factor D3 WD3 RD3-Alt1 WD3 *RD3-Alt1 RD3-Alt2 WD3 *RD3-Alt2 Factor Dn WDN RDN-Alt1 WDN *RDN-Alt1 RDN-Alt2 WDn*RDN-Alt2 Group D unweighted total 100% (WDi*RDi-Alt1) (WDi*RDi-Alt2) Subtotals Group Group Group Weighted Group Group Weighted Weights Unweighted Total Unweighted Total Total Total Group A WA (WAi*RAi-Alt1) WA *(WAi*RAi-Alt1) (WAi*RAi-Alt2) WA *(WAi*RAi-Alt2) Group B WB (WBi*RBi-Alt1) WB *(WBi*RBi-Alt1) (WBi*RBi-Alt2) WB *(WBi*RBi-Alt2) Group C WC (WCi*RCi-Alt1) WC *(WCi*RCi-Alt1) (WCi*RCi-Alt2) WC *(WCi*RCi-Alt2) Group D WD (WDi*RDi-Alt1) WD *(WDi*RDi-Alt1) (WDi*RDi-Alt2) WD *(WDi*RDi-Alt2) Grand total 100% Final score- Alt 1 Final score- Alt 2