Proposed Pavement Performance Models for Local Government Agencies in Michigan

Abstract

The state of Michigan has 191 432 km (118,950 mi) of roadway (paved and unpaved), including highways, roads, and streets. Local government agencies, which are responsible for 176 270 km (109,529 mi) of these roads and streets, commonly use a pavement management system (PMS) called RoadSoft to assist in managing their pavement network. A key element of any PMS is its ability to predict future pavement performance. A study is described in which various deterministic and probabilistic models were evaluated using data from two Michigan counties. It was found that the logistic growth model and the Markov model provided the best combination of predictive ability and potential for applicability in Michigan counties. A comparison between these models found that their predictive ability for four pavement segments with different deterioration rates was good, with the Markov model offering the added advantage of representing future performance as a probability distribution, not as a single condition state. Current plans are to implement the logistic growth model in RoadSoft by the end of 1999 and to add the Markov model as local organizations gather sufficient pavement condition data over the next 5 years. It is hoped that these two pavement deterioration models can be implemented in the RoadSoft PMS to improve pavement performance prediction.

Developing Pavement Distress Deterioration Models for Pavement Management System Using Markovian Probabilistic Process

“…An accurate and reliable PDM is essential for the optimization model of the PMS [10]. Many researchers have attempted to develop a PDM using di erent techniques [5,6,8,9,11,12]. ese techniques can be divided into two main types-namely, deterministic and probabilistic models.…”

“…Deterministic modeling techniques are the most common because of their relative simplicity, ease of use, and familiarity [5,6]. ese techniques include straight-line extrapolation, S-shaped curves, polynomial constrained least squares, and logistic growth models [13].…”

“…Currently, the Markov process is popular among probabilistic techniques to develop a deterioration model. Michigan DOT already improved their PMS using the Markov process [5,9]. In this paper, the Markov process was applied to the low-volume roads of Colorado to develop deterioration models.…”

“…Moreover, the evaluation of distress indices exceeds the standard deviation of 10 for a signi cant number of roadway segments [4]. However, as the deterministic technique does not take into account the uncertainties in pavement behavior under variable tra c load and weather conditions [5][6][7], modeling uncertainty requires the use of probabilistic operation research techniques [5,6]. Key summaries from the study Butt [8] conducted about implementing a probabilistic technique in the pavement management system (PMS) are as follows:…”

Practical Approach for Pavement Treatment Decisions for Local Agencies

“…Wolters et al [12] also reported that Champaign County uses a treatment matrix based on the Pavement Condition Index [PCI] and Rolling Weight Deflectometer (RWD) to select an effective treatment method such crack sealing, chip seal, microsurfacing, asphalt overlay and reconstruction while the City of Macomb uses a customized rating system to select the appropriate maintenance or rehabilitation strategy. Similarly, Douglas [13] developed a pavement management system for local governments that overcomes local issues such as data collection limitation and lack of expertise. At the state highway agency level, many DOTs have developed and used a decision aid tool such as decision tree or a matrix-based method to facilitate the decision making process including some computerized tools as well.…”