Universal Pavement Distress Evaluator Based on Fuzzy Sets

Setting priorities for pavement maintenance and rehabilitation depends on the availability of a universal scale for assessing the condition of every element in the network. The condition of a pavement section has traditionally been assessed by several condition indexes. The present serviceability index (PSI) is one common evaluator used to describe the functional condition with respect to ride quality. Pavement condition index is another index commonly used to describe the extent of distress on a pavement section. During the decision-making process, both classes of indexes are needed to evaluate the overall status of a pavement section in comparison to other sections in the network. Traditionally, regression techniques were used for the development of functions that relate condition indexes to the information recorded in the pavement management database. This approach produces mathematical functions that are limited to a particular database. The functions so developed may also suffer from inaccuracies due to errors in data collection and recording. There is a need for a more generalized approach for the evaluation of pavement conditions to enable efficient management of large transportation networks. The development of a universal measure capable of formally assessing the condition of a pavement section within the universe of pavement conditions is described. This is accomplished by the fusion of a set of fuzzy membership functions that describe different parameters in the database with the perception of each parameter’s significance. The model output is the fuzzy distress index (FDI), which combines the extent of structural distress with traditional performance parameters such as roughness to describe the overall status of the pavement section. The behavior of FDI over time is examined for a random sample of pavement sections and is compared with trends in the corresponding PSI values (PSI was used only because it was readily available in the database). The results indicate that the flexible, universal FDI is a consistent and accurate measure of the overall pavement condition. The set of generated membership functions describing the different extents of every distress type can be easily standardized over the 50 states, allowing the model to be implemented on any pavement at any location. Also, the parameter weights used in the assessment may be easily adjusted (increased or decreased) to reflect changes in maintenance policies or budget availability at the local, state, or national decision-making level. Moreover, the concept allows for the omission of any number of parameters that might not be available in a particular pavement management database.

[1]  Stephen G. Ritchie,et al.  DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR AUTOMATED PAVEMENT EVALUATION , 1991 .

[2]  Wayne J. Davis,et al.  Optimal maintenance decisions for pavement management , 1987 .

[3]  Kumares C. Sinha,et al.  THE DEVELOPMENT OF OPTIMAL STRATEGIES FOR MAINTENANCE, REHABILITATION AND REPLACEMENT OF HIGHWAY BRIDGES. FINAL REPORT. VOLUME 2: A SYSTEM FOR BRIDGE STRUCTURAL CONDITION ASSESSMENT. REVISED EDITION , 1989 .

[4]  Samuel H Carpenter,et al.  NEW TECHNIQUES FOR MODELING PAVEMENT DETERIORATION , 1987 .

[5]  Weng Tat Chan,et al.  PRIORITY RATING OF HIGHWAY MAINTENANCE NEEDS BY NEURAL NETWORKS , 1993 .

[6]  C. H. Juang,et al.  Unified Pavement Distress Index for Managing Flexible Pavements , 1992 .

[7]  X Huihua,et al.  The use of fuzzy-sets mathematics for analysis of pavement skid resistance , 1990 .

[8]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  K P George,et al.  MODELS FOR PREDICTING PAVEMENT DETERIORATION , 1989 .

[11]  M Y Shahin,et al.  COMPARING PAVEMENT PERFORMANCE AND ITS EFFECT ON MAINTENANCE AND REHABILITATION COST , 1994 .

[12]  W R Hudson,et al.  COMPREHENSIVE RANKING INDEX FOR FLEXIBLE PAVEMENT USING FUZZY SETS MODEL , 1993 .

[13]  Samuel H Carpenter,et al.  APPLICATION OF MARKOV PROCESS TO PAVEMENT MANAGEMENT SYSTEMS AT NETWORK LEVEL , 1994 .

[14]  Kelvin C. P. Wang,et al.  Probabilistic Behavior of Pavements , 1994 .

[15]  D J Elton,et al.  ASPHALT PAVEMENT EVALUATION USING FUZZY SETS , 1988 .

[16]  Dennis A. Randolph Application of Neural Network Technology to Highway Maintenance Management , 1996 .

[17]  Jean-Lou Chameau,et al.  THE USE OF FUZZY SETS MATHEMATICS TO ASSIST PAVEMENT EVALUATION AND MANAGEMENT. FINAL REPORT , 1986 .

[18]  E O Lukanen,et al.  PERFORMANCE HISTORY AND PREDICTION MODELING FOR MINNESOTA PAVEMENTS , 1994 .

[19]  Tien Fang Fwa,et al.  Routine Maintenance and Pavement Performance , 1986 .

[20]  Samuel H Carpenter,et al.  PAVEMENT PERFORMANCE PREDICTION MODEL USING THE MARKOV PROCESS , 1987 .