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Evaluation of MDOT’s Methodologies for both Quantifying Pavement Distress & Modeling Pavement Performance for LCC and RSL Estimation Purposes

项目名称：	Evaluation of MDOT’s Methodologies for both Quantifying Pavement Distress & Modeling Pavement Performance for LCC and RSL Estimation Purposes
摘要：	Since the inception of its pavement management system in the early 1990’s, the Michigan DOT has been using the Distress Index (DI) as a measure of surface condition for pavements. The DI is based on an assignment of increasing-value numeric “points” to specific distress type-and-severity observations obtained through detailed surveys; the more detrimental a distress type/severity observation is to pavement structural condition, the higher the assigned point value. The distress information is collected via digital images by vendors on roughly half the MDOT network every year. There appears to be a gap between what the state of the practice in the pavement data collection industry typically provides nationwide, and the complexity of the distress information MDOT asks for. MDOT has decided to suspend the collection of the full extent of the distresses typically requested, and to suspend the use of DI as the pavement condition measure. This research is expected to investigate and recommend a new condition measure, or revisions to the existing DI system, that MDOT can utilize moving forward. This new/revised measure is expected to be compatible with what the pavement data collection industry can deliver in an accurate and timely manner. It is also expected to have low impact on MDOT’s business practices and processes, including the remaining service life (RSL) estimation process and the life-cycle cost analysis (LCCA) process. MDOT’s Pavement Management Section oversees the Department’s implementation of the state life-cycle law. MDOT currently uses a logistic growth curve fit through historic DI data to estimate when a pavement or fix type will reach a remaining service life of zero. This initial life estimate produces a fix life estimate that is utilized in the RSL estimation process for network-level pavement condition monitoring. When life-extension benefits of preventive maintenance cycles are added in, the resulting estimation is a service life value that is used as the analysis period for LCCA calculations. The intent of the LCCA process is to pick the most economical pavement type for a given planned project location. RSL estimate values are used at the project level to assist with project type selection and at the network level to forecast network condition. Therefore, an accurate performance prediction/estimation methodology is critical to the LCCA, project planning, and budgetary decision-making processes for the effective use of transportation funding. Because the logistic growth curve was chosen several decades ago, MDOT feels it is time to re-evaluate whether or not the logistic growth curve is still the best method for predicting performance of individual projects and the various fix types (as a group). This research would evaluate and recommend improvements to the data modeling methodology by comparing the logistic growth curve to other pavement performance modeling methods that are being utilized across the country to determine the best choice for management of Michigan’s pavements. The model will, of course, utilize the new pavement condition parameter adopted by MDOT from the first part of this research project. This investigation would also look at ways to incorporate modeling of network level International Roughness Index (IRI), rutting, faulting, and cracking. This would assist with Michigan’s implementation of the new federal pavement performance rules which require each state to set targets for pavement condition based on a composite measure incorporating three of four condition metrics: IRI, cracking percent, and rutting-or-faulting.
资助组织：	Michigan Department of Transportation
执行机构：	Michigan State University, East Lansing
开始时间：	20210201
预计完成日期：	20230630
主题领域：	Pavements