Expert Project Recommendation Procedure for Arizona Department of Transportation’s Pavement Management System

Author(s):  
Gerardo W. Flintsch ◽  
John P. Zaniewski

The Arizona Department of Transportation (ADOT) uses a network-level pavement management system to determine budget requirements for its annual pavement preservation program. Although this is a valuable tool for preservation programming, it does not assist the engineers with the selection of projects and rehabilitation treatments. The documented research was designed to enhance the capability of ADOT’s pavement management system to include project selection. An automatic project recommendation procedure was developed and implemented in a user-friendly, modular computer program. This automatic system is expected to reduce considerably the effort required to develop the preservation programs. It should improve the consistency of the decision process. The analysis starts with a section delineation procedure that delineates uniform roadway sections. It then computes the remaining service life of each uniform section by using linear performance equations and trigger points defined for each condition indicator. An artificial neural network simulator is used to screen and recommend roadway sections for the preservation program. The trained artificial neural network prepares a list of candidate sections, using the criteria learned from past selections and the current condition of all pavement sections. This preliminary list of candidate sections is further analyzed by a project recommendation procedure. This procedure recommends a preservation treatment, assigns a priority rating to each section in the list, and sorts the projects by priority. Funding is assigned to the highest-priority sections within each roadway group until the budget recommendation provided by the network optimization process is reached.

2015 ◽  
Vol 10 (4) ◽  
pp. 355-364 ◽  
Author(s):  
Andrzej Pożarycki

Results of research studies, the amount of input data available in pavement management system databases, and artificial intelligence methods serve as versatile tools, well-suited for the analysis conducted as a part of pavement management system. The key source of new and to be employed knowledge is provided. In terms of e.g. assessing thickness of bituminous pavement layers, the default solution is pavement drilling, but for the purposes of pavement management it is prohibitively expensive. This paper attempts to test the original concept of employing an empirical relationship in an algorithm verifying results produced by the artificial neural network method. The assumed multistage asphalt pavement layer thickness identification control process boils down to evaluating test results of the road section built using both, reinforced and non-reinforced pavement structure. By default, the artificial neural network training set has not included the reinforced pavement sections. Hence, it has been possible to identify “perturbations” in assumptions underlying the training set. Pavement test section points’ results are indicated in the automated manner, which, in line with implemented methods, is not generated by perturbations caused by divergence between actual pavement structure and assumptions taken for purposes of building pavement management system database, and the artificial neural network learning dataset is based on.


Author(s):  
Gerardo W. Flintsch ◽  
John P. Zaniewski ◽  
James Delton

The use of an artificial neural network simulator to develop and implement an automatic procedure for screening and recommending roadway sections for pavement preservation is described. This procedure is part of an automatic project recommendation procedure extension of the Arizona Department of Transportation's (ADOT's) pavement management system. The output of the recommendation procedure is a list of candidate projects for consideration in the 5-year pavement preservation program. The artificial neural network simulator was used to “learn” the knowledge from historical project selection. The neural network was trained with the pavement condition and characteristics and the sections selected for the ADOT's pavement preservation program for several years. The trained neural network predicted a correct output for 100 percent of the training facts and 76 percent of the testing examples. Further refinements of the artificial neural network architecture should result in better-performing networks. The artificial neural network analysis reduces the level of effort required to identify candidate sections for the pavement preservation program, reduces subjectivity, and minimizes the probability of missing sections that should be programmed.


1995 ◽  
Vol 20 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Francois Michaud ◽  
Ruben Gonzalez Rubio ◽  
Daniel Dalle ◽  
Steve Ward

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