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Closing the Gap between Pavement Design and Pavement Management for Better Pavements

Description:

Pavement management systems were initially conceived as frameworks for pavement design (project-level) but have since evolved and in many cases disassociated from pavement design, and are now primarily systems to optimally manage and maintain pavements (network-level). Many factors have contributed to this divergence, one of which was the different performance indicators used for pavement design in previous design procedures and pavement management. The development of the AASHTO’s Mechanistic-Empirical Pavement Design Guide (MEPDG) and the associated software AASHTOWare Pavement ME Design, which are based on similar performance indicators as those used for pavement management, has brought to forefront the need and the benefits of re-linking pavement design and pavement management.

Pavement management and pavement design share the following cross-cutting issues:

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Pavement performance models are an integral part of both pavement design and PMS yet historically these models have been different

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Pavement performance monitoring is a component of PMS for managing pavements but is also the feedback loop for pavement design

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Design of long-lasting pavements assumes and requires optimally scheduled maintenance and preservation activities recommended by PMS

Integrating pavement design and PMS is needed for the following purposes:

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Calibration / validation is not a one-time issue but an ongoing activity – the process should be integrated/automated as part of the PMS than done ad-hoc each time

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PMS can and will serve as a primary data source for pavement design inputs

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PMS is best suited to interface with traffic, laboratory and other information systems to provide a single source of data for pavement design

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PMS can and should serve as the storage medium for data used as pavement design inputs – integrate pavement design inputs within PMS, a live system that constantly evolves with technology

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Incorporate pavement design performance predictions (distress, rutting, IRI etc.) within PMS – becomes the default performance curve and adaptively calibrated with measured performance

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Incorporate or integrate as-built and QA/QC data within PMS – The recorded spatial variability can be used in determining the future reliability of new and rehabilitated pavement design.

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Make every new or rehab section designed with MEPDG an experimental section for future calibration and engineering analysis

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The spatial variability on each pavement section, and the variability among pavement sections with similar traffic-climate-materials can be used in transforming deterministic performance prediction models into probabilistic models

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Reconcile the maintenance and rehabilitation (M&R) treatments regime that is derived from life-cycle cost analyses (LCCA) during project-level design with the M&R regime that is the result of optimal budget allocation during network-level PMS

Objective:

The objective of this research is to develop good practices and guidance to integrate pavement design, material and construction (as-built and QA) information with PMS to best meet the needs identified in the previous section.

Benefits:

The following are the potential benefits of this study:

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Pavement design requires many assumptions and simplifications, each of which can contribute to inefficient pavement design. Creating a feedback mechanism between pavement designs and measured past performance will ultimately lead to more cost-effective pavement designs.

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Calibration of the Pavement ME can be a costly and time consuming exercise, and linking pavement design with pavement management can ultimately reduce the time and cost associated with calibration / recalibration.

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With the significant amount of investment in collection of the input data for Pavement ME and the calibration of the performance models in Pavement ME, the highest return on the investment made by State Highway Agencies can be realized by using the Pavement ME performance predictions in the pavement management system to make pertinent decisions regarding cost-effective maintenance and rehabilitation of the pavement structures

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Linking pavement management and pavement design creates a direct feedback loop for agencies to evaluate their design decisions (e.g., materials, gradations, etc.), which will ultimately lead to more cost-effective pavement designs.

Related Research:

The 2010 Pavement Management Roadmap documents the need to increasingly use pavement management system data to inform other pavement processes, such as design. Similarly, a 2017 synthesis highlighted the need to make better use of pavement management data (Zimmerman, 2017). However, limited work has been performed directly related to linking pavement design and management, with the primary relationship to the existing body of knowledge being the calibration of mechanistic-empirical models. As one example, Lea and Harvey (2002) describe mining California pavement management data, with one objective being to provide calibration data to mechanistic-empirical models for reflection cracking. As another example, Li et al. (2010) describe the use of historical performance data (among other sources) to update the Washington State DOT pavement design catalog.

Tasks:

The research will include the following tasks:

Phase 1:

Task 1: Conduct a review of the current practice for use of pavement management data to support design decisions, and the application of pavement design data and tools for use in pavement management decisions

Task 2: Conduct a survey of State DOTs to develop an understanding of the data stored within pavement management systems that can be used to inform the design process, and vice versa

Task 3: Identify key activities for linking pavement management and pavement design, and develop a work plan for validating the activities using State DOT data and systems

Task 4: Prepare an interim report documenting Phase 1 findings and clearly delineate the plan of work for Phase 2 of the project.

Phase 2:

Task 5: Execute the work plan developed in Phase I

Implementation:

Develop a guidance document with clear steps for integrating pavement management and design, as well as a final report that presents best practices and highlights case studies

Relevance:

State DOTs

Sponsoring Committee:AFD30, General and Emerging Pavement Design
Research Period:24 - 36 months
Research Priority:High
RNS Developer:Nadarajah Sivaneswaran and James Bryce
Source Info:1. Quality Assurance Data Analysis as a Leading Indicator for Infrastructure Condition Performance Management
2. Zimmerman, K., Pierce, L., Krstulovich, J., 2010. Pavement Management Roadmap. Federal Highway Administration Report No. FHWA-HIF-11-011, Washington, D.C.
3. Lea, J. and J. Harvey, 2002 (revised 2004). Data Mining of the Caltrans Pavement Management System (PMS) Database. California Department of Transportation, Sacramento, CA.
4. Li., J., Uhlmeyer, J., Mahoney, J. and S. Muench, 2010. Use of the AASHTO 1993 Guide, MEPDG and Historical Performance to Update the WSDOT Pavement Design Catalog. Paper presented at the 89th Annual Meeting of the Transportation Research Board. January 10-14, 2010, Washington, D.C.
Date Posted:02/05/2019
Date Modified:02/19/2019
Index Terms:Pavement design, Pavement management systems, Pavement maintenance, Pavements, Pavement performance, Performance measurement, Quality assurance, Data analysis,
Cosponsoring Committees: 
Subjects    
Highways
Design
Maintenance and Preservation
Pavements

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