AbstractMoving Ahead for Progress in the Twenty-First Century (MAP-21) requires US state highway agencies (SHA) to utilize performance-based approaches in their pavement management decision-making processes, and use of a remaining service life (RSL) model would be one of such performance-based approaches for facilitating the pavement management decision-making process for SHAs. In this study, statistical and artificial neural network (ANN)-based pavement performance and RSL models were developed for Iowa jointed plain concrete pavement systems (JPCP) using actual pavement structural, traffic, construction history, and pavement performance records obtained from the Iowa Department of Transportation pavement-management information system database. While both models were found to be potentially useful for project and network level performance and RSL predictions, statistical and ANN-based models were respectively found to be more suitable for project and network level analysis. Using these models, efficient Microsoft Excel-based automation tools were created to predict future performance of a JPCP section and estimate RSL values based on predicted future performance and threshold limits for the performance indicators. Consequence analysis was also conducted to investigate the impact of traffic and preservation treatment (diamond grinding) on the RSL of a JPCP. The tool, also capable of estimating realistic pavement pretreatment and posttreatment performance and RSL, could be successfully used as part of performance-based pavement management strategies and helping decision-makers make better-informed pavement management decisions to properly allocate agency resource expenditures. Moreover, this study provides a better understanding of RSL and the factors that influence both the project and network level RSL.Practical ApplicationsIn this study, the authors developed models for potential use in predicting future conditions of Iowa jointed plain concrete pavements using advanced modeling techniques. Based on such predicted data in combination with a threshold value, the criterion defining when a JPCP section could no longer be structurally and/or functionally good enough for use, remaining service life of a JPCP section could be estimated. Using the models, a Microsoft Excel–based automation tool was created to automatically predict future pavement-condition, calculate RSL, and graphically show the results from user-entered inputs. The tool is also capable of re-estimating RSL either if (1) traffic levels unpredictably increase or decrease in the future, or if (2) pavement preservation (diamond grinding) is applied. Overall, the automation tool created in this study is capable of providing realistic pavement condition and RSL estimations and could be successfully used in developing pavement management strategies by incorporating it into pavement management systems and helping decision-makers make better-informed pavement-management decisions to properly allocate agency resource expenditures.

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