Development of Pavement Performance and Remaining Service Life Prediction Tools for Iowa Jointed Plain Concrete Pavement Systems
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 149, Issue 1
Abstract
Moving 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 Applications
In 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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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Data include model input and output parameters data and the ANN model.
Acknowledgments
The authors would like to thank the Iowa Highway Research Board and Iowa County Engineers Service Bureau for supporting this study. The authors also gratefully acknowledge the project technical advisory committee members from the Iowa County Engineers Association (ICEA), including Lee Bjerke, Zach Gunsolley, Todd Kinney, Mark Nahra, John Riherd, Brad Skinner, and Jacob Thorius for their guidance, support, and direction throughout the study. Special thanks are extended to Steve De Vries and Danny Waid, who developed the original concept of this study. The authors also sincerely acknowledge Brian P. Moore of ICEA for his guidance, support, and direction throughout the research. The authors would like to express their sincere gratitude to Mark Murphy from the Iowa Department Office of Analytics for his full support during data collection and processing models and tools. The authors would also like to sincerely thank other research team members from Iowa State University’s Program for Sustainable Pavement Engineering and Research (PROSPER) at InTrans for their assistance. The contents of this paper reflect the views of the authors who are responsible for the facts and accuracy of the data presented within. The contents do not necessarily reflect the official views and policies of the Iowa Highway Research Board, the Iowa Department of Transportation, or Iowa State University. This paper does not constitute a standard, specification, or regulation. This study was funded by the Iowa Highway Research Board (IHRB) through research project TR-740, Development of Iowa Pavement Analysis Technique (IPAT).
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 149 • Issue 1 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 1, 2022
Accepted: Sep 19, 2022
Published online: Nov 23, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 23, 2023
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