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

Truck Traffic

Future Traffic • Prioritized List • Decision Support • Method

Flexibility

Input Parameters

Current ADT PMIS Distress Score TxDOT Admin.

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 input/involvement

Structural Strength Index (SSI) Evacuation Route

PMIS Individual Distresses

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

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 – Dollars ($)

2 Failures IRI = 118 5800 veh/day $20,000 Maint

15% Alligator IRI = 136 1800 veh/day 20% Trucks

VS

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

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

Weight of Importance Definition (from Saaty 1990a)

Explanation (from Saaty 1990a)

1 Equal ImportanceTwo activities contribute equally to the objective

3 Moderate importance of one over anotherExperience and judgment strongly favor one activity over another

5 Essential or strong impor tanceExperience and judgment strongly favor one activity over another

7 Very strong importanceAn activity is strongly favored and its dominance demonstrated in practice

9 Extreme importanceThe evidence favoring one activity over another is of the highest order of affirmation

2, 4, 6, 8Intermediate 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

Weight Definition1 Each variable is equally as important as the other3 One variable is minimally more impor tant than the other5 One variable is moderately more important than the other7 One variable is significantly more important than the other9 One variable is drastically more important than the other

Weight Definition1 Each variable is equally as important as the other3 One variable is minimally more impor tant than the other5 One variable is moderately more important than the other7 One variable is significantly more important than the other9 One variable is drastically more important than the other

Visu

al D

istre

ss

Cur

rent

AD

T

Cur

rent

Tru

ck

AD

T

Con

ditio

n Sc

ore

Rid

e Q

ualit

y

Sect

ions

that

re

ceiv

e m

ost

Mai

nten

ance

Visual Distress

1 7 5 1 7 7

Current ADT 1/7 1 1/3 1/7 1/5 1/3

Current Truck ADT

1/5 3 1 1/7 1/5 1/3

Condition Score

1 7 7 1 7 7

Ride Quality 1/7 5 5 1/7 1 1

Sections that receive most Maintenance

1/7 3 3 1/7 1 1

Visual Distress is significantly more important than Current ADT

Current ADT is moderately less important than ride

Decision Parameter

Max Eigenvector

Priority Vector

(Weigths)

Visual Distress 0.6711 0.3660

Currnt ADT 0.0546 0.0298

Current Truck ADT 0.0854 0.0466

Condition Score 0.6968 0.3801

Ride Quality 0.1839 0.1003Section Receiving most Maintenance 0.1417 0.0773Sum 1.8334 1

AHP Weight

Visual Distress (DN) Current ADT (veh/day)

Current FM Truck ADT (trucks/day

Current Non-FM Truck ADT (trucks/day)

1 DN = 0.2629 veh/day ≤ 1000 trucks/day ≤ 160 trucks/day ≤ 12252 0.2629 < DN ≤ 0.433 NA NA NA3 0.433 < DN ≤ 0.603 1000 < veh/day ≤ 2000 160 < trucks/day ≤ 320 1225 < trucks/day ≤ 24504 0.603 < DN ≤ 0.773 NA NA NA5 0.733 < DN ≤ 0.943 2000 < veh/day ≤ 7000 320 < trucks/day ≤ 480 2450 < trucks/day ≤ 36756 0.943 < DN ≤ 1.113 NA NA NA7 1.113 < DN ≤ 1.283 7000 < veh/day ≤ 10,000 480 < trucks/day ≤ 640 3675 < trucks/day ≤ 49008 1.283 < DN ≤ 1.45 NA NA NA9 1.45 < DN 10,000 < veh/day 640 < trucks/day 4900 < trucks/day

AHP Weight

Condition Score (CS) FM Ride Quality (IRI)

Non-FM Ride Qualtiy (IRI) Maintenance Cost ($)

1 90 to 100 1 to 119 1 to 59 Cost = $02 NA NA NA $0 < Cost ≤ $60003 70 to 89 120 to 154 60 to 119 $6000 < Cost ≤ $12,0004 NA NA NA $12,000 < Cost ≤ $18,0005 50 to 69 155 to 189 120 to 170 $18,000 < Cost ≤ $24,0006 NA NA NA $24,000 < Cost ≤ $30,0007 35 to 49 190 to 220 171 to 220 $30,000 < Cost ≤ $36,0008 NA NA NA $36,000 < Cost ≤ $42,0009 1 to 34 221 to 950 221 to 950 $42,000 < Cost

AHP Weight

Failures (EA)

Deep Rutting

(%)

Alligator Cracking

(%)Longitudinal Cracking (ft)

Transverse Cracking

(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 NA4 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%7 3 11% 8% 126' to 150' 7 22% < Patch ≤ 35%8 4 12% 9% and 10% 151' to 175' 8 35% < Patch ≤ 44%9 ≥ 5 ≥ 13% ≥ 11% ≥ 176' 9 44% < Patch

36.60%

2.98% 4.66%

38.01%

10.03%

7.73%

Decision Parameters

Visual Distress

Currnt ADT

Current Truck ADT

Condition Score

Ride Quality

Section Receiving most Maintenance 44.88%

(16.43%)

21.28% (7.79%)

2.64% (0.97%)

13.42% (4.91%)

4.52% (1.65%)

2.41% (0.88%) 10.85%

(3.97%)

Distresses Failures

Deep Rutting

Block Cracking

Alligator Cracking

Longitudinal Cracking Transverse Cracking Patching

Rank HWY BRM ERMLength

(mi) District Action

1 FM 488 0318+00.0 0323+00.5 5.5 Grading, Structure, Base, Surface Project Let in FY 2009

2 FM 1451 0344+00.5 0349+00.0 4.5Grading, Structure, Base, Surface Project Let in FY 2009

3 FM 1644 0389+00.0 0396+00.0 7 Restoration Project Let in FY 2009

4 IH 45 A 0186+00.2 0190+00.0 3.8 Contracted Rehab

5 FM 485 0605+00.5 0610+00.0 4.5

Restoration Project Let in FY 2007 - No data available in subsequent years, thus it sees what led to the FY 2007 project

6 FM 1848 0351+00.4 0354+00.2 2.8 Routine Maintenance Contract

7 FM 3 0380+00.0 0382+00.5 2.5 Routine Maintenance Contract

8 FM 2547 0331+00.5 0335+00.0 3.5 Contracted RMC Rehab

9 FM 416 0625+00.3 0627+00.0 1.7 In House Maintenance Forces

10 FM 80 0373+00.0 0376+00.7 3.2 In House Maintenance Forces

11 FM 2293 0607+00.0 0610+00.0 3Routine Maintenance Contract (FY 2007), then Seal Coat FY 2009

12 SH 75 K 0387+00.0 0389+00.5 2.5 Unknown

13 US 190 K 0662+00.0 0665+00.5 3.5 Unknown

14 FM 80 0351+00.0 0354+00.0 3Grading, Structure, Base, Surface Project Let in FY 2009

15 FM 416 0628+00.5 0632+00.2 3.7 Routine Maintenance Contract

16 FM 2485 0378+00.0 0380+00.0 2 Unknown

17 FM 488 0332+00.5 0335+00.5 3 Routine Maintenance Contract

18 FM 1940 0624+00.5 0626+00.5 2 In House Maintenance with Rehab let in Nov. 2010

19 FM 979 0614+00.7 0616+00.5 1.8 Unknown

20 FM 46 0605+00.0 0607+00.0 2 Unknown

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)

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

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

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)