Book Description

One of the main elements of any Pavement Management System is Pavement Performance Modeling. Accurate pavement performance models can save millions of dollars through proper maintenance of the transportation pavement infrastructure. Several pavement performance models have been developed over the years to predict pavement performance. However, in the development of pavement performance models the climatic parameters were often ignored. Climatic inputs, especially rainfall, affect pavement performances because material properties change with temperature and moisture conditions particularly in ACP (Asphalt Concrete Pavement). The modulus of the unbound materials is sensitive to the variation of moisture content. Rainwater can infiltrate into the unsaturated pavement layers though cracks, joints or edges of the pavement and can deteriorate the pavement structure by reducing structural capacity. This study investigates rainfall impacts on pavement performance and maintenance costs of asphalt concrete pavement on Texas highways. Performance models are developed to accurately predict the pavement condition and performance for the Texas Department of Transportation (TXDOT) Highway pavement network for San Antonio Districts. In addition, tools are developed to accurately estimate the future maintenance cost considering rainfall. TxDOT's PMIS data for the San Antonio Texas Department of Transportation (TxDOT) District was used for pavement conditions and NOAA data was used for historical rainfall information. One Way Analysis of Variance (ANOVA) was performed to determine the significant variables for the pavement performance model. The San Antonio District's road network broken into five pavement families following functional classes such as Interstate Highways (IH) main lane, Interstate Highways (IH) frontage lane, State Highways (SH), US highways (US) and Farm to Market Road (FM). The statistical modeling reported herein shows that rainfall had a significant impact on deterioration of pavement conditions of Interstate Highways (IH) for main lanes. For Interstate Highways (IH) frontage lane and Farm to Market (FM) pavement families combination of rainfall and traffic class had significant impact on the pavement performance model. Engineering knowledge supported the concept that increasing amount of rainfall will degrade the pavement structure at a faster rate. However, statistical analysis of the available data showed that rainfall did not have a significant statistical impact on the performance model of State Highway (SH) and US highways (US) pavement families. Other significant factors that affect the flexible pavement performance identified in this research for all pavement types are pavement age and previous year's distress scores. Previous maintenance and rehabilitation (M&R) activities performed on a pavement section will also have a significant impact on the pavement deterioration model for pavement families except for Interstate Highway (IH) main lanes and U.S Highways (US). In this research, an application was developed to estimate the maintenance cost of the network considering the rainfall and other significant factors. This tool will allow users to accurately predict future maintenance costs and allocate appropriate budgets.