The problem: Pavement preservation and the multiple criteria considered in the decision making process A helpful tool: The Analytic Hierarchy Process

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2 The problem: Pavement preservation and the multiple criteria considered in the decision making process A helpful tool: The Analytic Hierarchy Process (AHP), what it is and how it works Application to a District network: Weight the criteria and the data associated with a network Additional possibilities: What other applications can the process help with

3 Truck Traffic Future Traffic Prioritized List Decision Support Method Flexibility

4 Input Parameters Current ADT TxDOT Admin. PMIS Distress Score input/involvement Additional Field Testing Population Density Current Truck ADT Ride Quality (From PMIS or other) Political input/involvement Pavement Prediction Models Projected 18-kip Equivalent Future ADT Rutting Sections that receive the most RM Economic Development Date Since Last M&R Action Future Truck ADT Visual Distress (Site Visit) Effectiveness of RM actions Condition of Adjacent Sections Functional Classification PMIS Condition Score Public Structural Strength input/involvement Index (SSI) Evacuation Route PMIS Individual Distresses

5 Example Criteria 1. Visual Distress 2. Current ADT 3. Current Truck ADT 4. Future ADT 5. Future Truck ADT 6. Condition Score 7. Ride Quality 8. Sections receiving Dollars ($) most maintenance How can these be compared and related? Distress Density Vehicles per Day Trucks per Day Vehicles per Day Trucks per Day Rating Scale (numeric value) Inches per Mile

6 2 Failures IRI = veh/day $20,000 Maint 15% Alligator IRI = veh/day 20% Trucks VS

7 Focus on the District decision making process Capitalize on field knowledge and expertise within the District Create a method to weight decision criteria Criteria weights should correspond with the district decision makers thought process Apply the weighted criteria to the District network to create a prioritized list Be versatile enough to move beyond 1/2 mile sections Help District decision makers explain the components associated with preservation decisions and utilize existing information

8 What is the Analytic Hierarchy Process (AHP)? A multi-criteria decision making tool created by Thomas L. Saaty Used by decision makers in manufacturing, engineering, industrial, political, and social sectors How does the AHP work? Captures how people actually think Forms the problem in a hierarchy Utilizes pairwise comparisons (competition) between components Pairwise comparisons create a matrix Components are compared on a scale of 1 to 9

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10 Weight of Importance Definition (from Saaty 1990a) 1 Equal Importance Explanation (from Saaty 1990a) Two activities contribute equally to the objective 9 Extreme importance Experience and judgment strongly favor one activity over another Experience and judgment strongly favor one activity over another An activity is strongly favored and its dominance demonstrated in practice The evidence favoring one activity over another is of the highest order of affirmation Weight 3 Moderate importance of one over Definition another 1 Each variable is equally as important as the other 5 Essential or strong impor tance 3 One variable is minimally more impor tant than the other 5 One variable is moderately more important than the other 7 Very strong impor tance 7 One variable is significantly more impor tant than the other 9 One variable is drastically more important than the other 2, 4, 6, 8 Intermediate values between the two adjacent judgments When compromise is needed Reciprocals If activity i has one of the above numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with i

11 Weight Definition 1 Each variable is equally as important as the other 3 One variable is minimally more impor tant than the other 5 One variable is moderately more important than the other 7 One variable is significantly more impor tant than the other 9 One variable is drastically more important than the other Visual Distress Visual Distress Current ADT Current Truck ADT Condition Score Ride Quality Sections that receive most Maintenance Current ADT 1/7 1 1/3 1/7 1/5 1/3 Visual Distress is significantly more important than Current ADT Current Truck ADT Condition Score 1/ /7 1/5 1/ Ride Quality 1/ /7 1 1 Sections that receive most Maintenance 1/ /7 1 1 Current ADT is moderately less important than ride

12 Decision Parameter Max Eigenvector Priority Vector (Weigths) Visual Distress Currnt ADT Current Truck ADT Condition Score Ride Quality Section Receiving most Maintenance Sum

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14 AHP Weight AHP Weight Visual Distress (DN) Current ADT (ve h/day) Current FM Truck ADT (trucks/day Current Non-FM Truck ADT (trucks/day) 1 DN = veh/day 1000 trucks/day 160 trucks/day < Deep DN Alligator NA NATransve rse NA < DN < veh/day < trucks/day < trucks/day 2450 Failures Rutting Cracking Longitudinal Cracking < DN NA NA NA (EA) < DN (%) (%) < veh/day 7000 Cracking 320 < trucks/day (ft) < trucks/day < DN NA NA NA < DN < veh/day 10, < trucks/day < trucks/day < DN 1.45 NA NA NA < DN 10,000 < veh/day 640 < trucks/day 4900 < trucks/day (EA) Patching (%) 1 0 0% to 4% 0% to 2% 0' to 25' 0 to 2 0% Patch 3% 2 1 5% and 6% 3% 26' to 50' 3 3% < Patch 7% 3 2 7% 4% 51' to 75' 4 NA 4 NA 8% 5% NA 5 7% < Patch 11% 5 NA 9% 6% 76' to 100' 6 11% < Patch 15% 6 NA 10% 7% 101' to 125' NA 15% Patch 22% % 8% 126' to 150' 7 22% < Patch 35% % 9% and 10% 151' to 175' 8 35% < Patch 44% % 11% 176' 9 44% < Patch AHP Weight Condition Score (CS) FM Ride Quality (IRI) Non-FM Ride Qualtiy (IRI) Maintenance Cost ($) 1 90 to to to 59 Cost = $0 2 NA NA NA $0 < Cost $ to to to 119 $6000 < Cost $12,000 4 NA NA NA $12,000 < Cost $18, to to to 170 $18,000 < Cost $24,000 6 NA NA NA $24,000 < Cost $30, to to to 220 $30,000 < Cost $36,000 8 NA NA NA $36,000 < Cost $42, to to to 950 $42,000 < Cost

15 Decision Parameters 7.73% Visual Distress 10.03% 38.01% 36.60% 4.66% 2.98% Currnt ADT Current Truck ADT Distresses Condition Score 2.41% Ride Quality (0.88%) 4.52% 10.85% (3.97%) Section Receiving (1.65%) most Maintenance 44.88% 13.42% (16.43%) (4.91%) Failures Deep Rutting Block Cracking Alligator Cracking 2.64% (0.97%) 21.28% (7.79%) Longitudinal Cracking Transverse Cracking Patching

16 Rank HWY BRM ERM Length (mi) District Action 1 FM FM Grading, Structure, Base, Surface Project Let in FY 2009 Grading, Structure, Base, Surface Project Let in FY FM Restoration Project Let in FY IH 45 A Contracted Rehab 5 FM Restoration Project Let in FY No data available in subsequent years, thus it sees what led to the FY 2007 project 6 FM Routine Maintenance Contract 7 FM Routine Maintenance Contract 8 FM Contracted RMC Rehab 9 FM In House Maintenance Forces 10 FM In House Maintenance Forces 11 FM Routine Maintenance Contract (FY 2007), then Seal Coat FY SH 75 K Unknown 13 US 190 K Unknown 14 FM Grading, Structure, Base, Surface Project Let in FY FM Routine Maintenance Contract 16 FM Unknown 17 FM Routine Maintenance Contract 18 FM FM Unknown 20 FM Unknown In House Maintenance with Rehab let in Nov. 2010

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18 Help quantify benefits and costs that do not have a direct monetary value Benefit of lane miles treated or life of a project Compare competing construction techniques Helps account for all variables, not simply project cost compared against a singular benefit metric Any situation where alternatives are competing for a limited resource (i.e. funds)

19 Either consciously or subconsciously many variables with several different units of measure contribute to the pavement decision making process The AHP provides a platform as a starting point to account for and weight these variables The AHP is a tool that can capture the thought process and capitalize on the expertise within a district A district s network and its needs can be structured into a hierarchy where each roadway section can compete against every other section Engineering data and empirical knowledge can be used to establish weights associated with criteria used in the competition

20 The method is versatile and flexible enough to account for changing information or a better understanding of the criteria Each district can tailor make the method to meet the specific needs and wants of its regional constituency The method can help provide justification for how and why things are being done The method can be applied to a wide variety of problems where multiple criteria are used with varying weights and many units of measure exist

21 Special Thanks to: Dr. Nasir Gharaibeh (TTI), Dr. Andrew Wimsatt (TTI), Darlene Goehl (TxDOT-Bryan), Tom Freeman (TTI), Dr. Stuart Anderson (TTI), Dr. Tim Lomax (TTI), Bryan Stampley (TxDOT-CST), Randy Hopmann (TxDOT-Tyler), Russell Lenz (TxDOT-CST)