FUZZY APPROACH FOR ESTABLISHING THE PAVEMENT CONDITION QUALITY INDEX Gwo-Hshiung Tzeng Institute of Technology Management and Institute of Traffic and Transportation, College of Management, National Chiao Tung University

INTRODUCTION Pavement management system – a systematic process that provides, analyzes and integrates pavement information for use in selecting and implementing pavement construction, rehabilitation and maintenance activities Pavement conditions evaluation is important – be evaluated from both the functional and structural viewpoints

The Pavement Distress Classification

Flexible pavement visual condition survey The type of distress – 18 kind of types The levels of distress severity – Slight, Medium, Heavy The levels of distress coverage – Rare, Discrete, Continuous

Pavement Condition Indexes AASHO Road Test(1960) – Present Serviceability Index (PSI) pavement functional condition relative to riding quality divided into the five levels from 0 for a poor serviceability to 5 for an excellent serviceability U.S. Army Corps of Engineers – Pavement Condition Index (PCI) the results of a visual condition survey ranging from 0 for a failed pavement to 100 for a pavement in perfect condition

How to develop the Index The pavement deterioration modeling – derived by regression techniques Predicting future indexes – the Markov processes In fact – visual condition survey is conducted by engineers – affected by the subjective and objective factors – there are no crisp criteria to follow in conducting visual rating

Flowchart Used for the Study

ESTABLISHMENT OF FUZZY NUMBER OF VARIOUS DISTRESS TYPES Part 1 – Acquisition of the relative weighting values of various distress types through pairwise comparisons. Part 2 – Acquisition of the relative weighting values of distress severity factors through the pairwise comparisons. Further acquisition of the membership frequency of various factors of three severity levels (slight, medium and heavy). Part 3 – Acquisition of the membership frequency of three coverage levels (rare, discrete and continuous).

Relative Weighting Values of 18 Distress Types The relative weighting of top five – Utility Cut Patching ( ) – Rutting ( ) – Corrugation ( ) – Potholes ( ) – Alligator Cracking ( ) The finding reveals the serious effect of pipeline excavation on pavement condition quality in Taiwan.

Relative Weighting Values and Fuzzy Number of Various Distress Severity Levels Structure integrity - Integrity of pavement structure Riding safety - Riding safety distance, sight distance and driving maneuverability Riding comfort - Effect of roughness on riding comfort Maintenance activity - Difficulty in maintenance activities, maintenance planning and workload

Relative weighting values of distress severity factors

The Fuzzy Number of distress Severity Level

Fuzzy Number of Various Distress Coverage Levels

Fuzzy Number of Distress Ratio Distress ratio – The product of distress severity and distress coverage – The fuzzy numbers of distress severity and coverage obtained in previous analyses are derived from the subjective judgement and processed through fuzzy statistics

Fuzzy Number of Distress Ratio

ESTABLISHMENT OF THE PAVEMENT CONDITION QUALITY INDEX (PCQI) Conventional regression mode – The residual between the estimator and the observation as a measurement error Fuzzy regression mode – The residual is caused by the uncertainty of the parameters in the model – Handle actual observations by way of possibility distribution without any requirement for statistical properties

The concepts of fuzzy regression Linearity Fuzzy parameter Inclusion relationship between the observation and estimator Minimization of fuzziness

Establishment of PCQI Defuzzification – centroid method Inspect a total of lane-kilometers by units of 100 meter, and obtained 3993 pieces of valid data Fuzzy regression model of PCQI (Let h=0)

Non-fuzzy number of distress ratio

Sensitivity Analysis- Relationship between the center of parameter and h

Sensitivity Analysis- Relationship between the spread of parameter and h

CONCLUSION The certainty and clarity required by conventional numerical processing no longer meet the needs of complex systems today. Factors that affect the system effectiveness such as the fuzzy feature existing in the subjective judgement and the flexibility of index are not considered

CONCLUSION The fuzzy number of distress ratio is obtained directly from the statistical results of manual judgement. The PCQI model allows corrections based on different geographical environment and evaluation criteria.