One Record Condition State Estimate (ORCSE) for Hot Mix Asphalt Pavements Using LTPP Data

Event Date/Time: 
June 14, 2018 - 9:09am
Event Location: 
3546D Engineering Building
Speaker: 
Michael Prohaska
Master's Defense

ABSTRACT

In order to provide safe and efficient surface transportation under ever increasingly constrained budgets, road owners and managers must utilize modern pavement planning methods based on cost-effective pavement management system (PMS). The methods must include long term record keeping to forecast future pavement conditions and distresses and to effectively plan future projects and budgets. Unfortunately, not all State Highway Agencies (SHAs) maintain a PMS database with sufficient records to adequately forecast pavement behavior. This study details a new non-parametric probabilistic method for forecasting the remaining service life (RSL) of pavement structures based on International Roughness Index (IRI) when limited time series data are available. This study is based on data from the Federal Highway Administration (FHWA) Long-Term Pavement Preservation (LTPP) program Special Pavement Study (SPS)-1.

The non-parametric probabilistic method, called herein "One Record Condition State Estimation (ORCSE)", is a potential method for local state highway agencies to estimate the RSL of specific pavement sections or the pavement network when limited historical pavement condition data are available to perform pavement condition forecasting. Further, as this new method is generated based on generalized LTPP data, it is applicable to roadway agencies who do not regularly collect condition and distress data and consequently cannot model pavement condition as a function of time. ORCSE may be improved by providing calibration to local practices and environmental factors enabling pavement managers to better estimate the pavement RSL using limited system data.