MEPDG Work Plan Task No. 8: Validation of Pavement Performance Curves for the Mechanistic-Empirical Pavement Design Guide, 2009

The objective of this research is to determine whether the nationally calibrated performance models used in the Mechanistic-Empirical Pavement Design Guide (MEPDG) provide a reasonable prediction of actual field performance, and if the desired accuracy or correspondence exists between predicted and monitored performance for Iowa conditions. A comprehensive literature review was conducted to identify the MEPDG input parameters and the MEPDG verification/calibration process. Sensitivities of MEPDG input parameters to predictions were studied using different versions of the MEPDG software. Based on literature review and sensitivity analysis, a detailed verification procedure was developed. A total of sixteen different types of pavement sections across Iowa, not used for national calibration in NCHRP 1-47A, were selected. A database of MEPDG inputs and the actual pavement performance measures for the selected pavement sites were prepared for verification. The accuracy of the MEPDG performance models for Iowa conditions was statistically evaluated. The verification testing showed promising results in terms of MEPDG’s performance prediction accuracy for Iowa conditions. Recalibrating the MEPDG performance models for Iowa conditions is recommended to improve the accuracy of predictions. ****************** Large File**************************

Guidance for Improving Foundation Layers to Increase Pavement Performance on Local Roads, TR-640, 2014

This document is the second of two deliverables for the project Optimizing Pavement Base, Subbase, and Subgrade Layers for Cost and Performance on Local Roads (TR-640). The first deliverable is the 454-page Final Field Data Report. The field data report describes test results and comparative analysis from 16 different portland cement concrete (PCC) pavement sites on local city and county roads in Iowa. At each site the surface conditions of the pavement (i.e., crack survey) and foundation layer strength, stiffness, and hydraulic conductivity properties were documented. The field test results were used to calculate in situ parameters used in pavement design methodologies for AASHTO (1993) and Iowa’s Statewide Urban Design and Specifications (SUDAS). Overall, the results of the study demonstrate how in situ and lab testing can be used to assess the support conditions and design values for pavement foundation layers and how the measurements compare to the assumed design values. This guide summarizes the study results and outlines general guidelines for applying them to optimize pavement bases, subbases, and subgrade layers of local roads with PCC pavements and thus their performance.

Fly Ash Soil Stabilization for Non-Uniform Subgrade Soils, Volume II: Influence of Subgrade Non-Uniformity on PCC Pavement Performance, TR-461, 2005

To provide insight into subgrade non-uniformity and its effects on pavement performance, this study investigated the influence of non-uniform subgrade support on pavement responses (stress and deflection) that affect pavement performance. Several reconstructed PCC pavement projects in Iowa were studied to document and evaluate the influence of subgrade/subbase non-uniformity on pavement performance. In situ field tests were performed at 12 sites to determine the subgrade/subbase engineering properties and develop a database of engineering parameter values for statistical and numerical analysis. Results of stiffness, moisture and density, strength, and soil classification were used to determine the spatial variability of a given property. Natural subgrade soils, fly ash-stabilized subgrade, reclaimed hydrated fly ash subbase, and granular subbase were studied. The influence of the spatial variability of subgrade/subbase on pavement performance was then evaluated by modeling the elastic properties of the pavement and subgrade using the ISLAB2000 finite element analysis program. A major conclusion from this study is that non-uniform subgrade/subbase stiffness increases localized deflections and causes principal stress concentrations in the pavement, which can lead to fatigue cracking and other types of pavement distresses. Field data show that hydrated fly ash, self-cementing fly ash-stabilized subgrade, and granular subbases exhibit lower variability than natural subgrade soils. Pavement life should be increased through the use of more uniform subgrade support. Subgrade/subbase construction in the future should consider uniformity as a key to long-term pavement performance.

Effects of Implements of Husbandry (Farm Equipment) on Pavement Performance, TR-563, 2012

The effects of farm equipment on the structural behavior of flexible and rigid pavements were investigated in this study. The project quantified the difference in pavement behavior caused by heavy farm equipment as compared to a typical 5-axle, 80 kip semi-truck. This research was conducted on full scale pavement test sections designed and constructed at the Minnesota Road Research facility (MnROAD). The testing was conducted in the spring and fall seasons to capture responses when the pavement is at its weakest state and when agricultural vehicles operate at a higher frequency, respectively. The flexible pavement sections were heavily instrumented with strain gauges and earth pressure cells to measure essential pavement responses under heavy agricultural vehicles, whereas the rigid pavement sections were instrumented with strain gauges and linear variable differential transducers (LVDTs). The full scale testing data collected in this study were used to validate and calibrate analytical models used to predict relative damage to pavements. The developed procedure uses various inputs (including axle weight, tire footprint, pavement structure, material characteristics, and climatic information) to determine the critical pavement responses (strains and deflections). An analysis was performed to determine the damage caused by various types of vehicles to the roadway when there is a need to move large amounts agricultural product.

Long Term Pavement Performance computed parameter : frost penetration /

"HRDI-13/11-08(1M)E"--P. 4 of cover.

Quantification of smoothness index differences related to Long-Term Pavement Performance equipment type /

"September 2005."

Improving pavements with Long-Term Pavement Performance : products for today and tomorrow : winning papers from the 2001-2002 International Contest on Long-Term Pavement Performance Data Analysis /

"HRDI-13/11-05(1M)E"--P. [4] of cover.

Long-term Pavement Performance Program (LTPP) data analysis support : National Pooled Fund Study TPF-5(013).

"HRDI-13/11-06(500)E"--P. [4] of cover.

Recycling old PCC pavement : performance evaluation of FAI 57 inlays /

"February 1993."

Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^

Investigation of AASHTOWare Pavement ME Design/DARWin-ME Performance Prediction Models for Iowa Pavement Analysis and Design, 2015

The Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A as a novel mechanistic-empirical procedure for the analysis and design of pavements. The MEPDG was subsequently supported by AASHTO’s DARWin-ME and most recently marketed as AASHTOWare Pavement ME Design software as of February 2013. Although the core design process and computational engine have remained the same over the years, some enhancements to the pavement performance prediction models have been implemented along with other documented changes as the MEPDG transitioned to AASHTOWare Pavement ME Design software. Preliminary studies were carried out to determine possible differences between AASHTOWare Pavement ME Design, MEPDG (version 1.1), and DARWin-ME (version 1.1) performance predictions for new jointed plain concrete pavement (JPCP), new hot mix asphalt (HMA), and HMA over JPCP systems. Differences were indeed observed between the pavement performance predictions produced by these different software versions. Further investigation was needed to verify these differences and to evaluate whether identified local calibration factors from the latest MEPDG (version 1.1) were acceptable for use with the latest version (version 2.1.24) of AASHTOWare Pavement ME Design at the time this research was conducted. Therefore, the primary objective of this research was to examine AASHTOWare Pavement ME Design performance predictions using previously identified MEPDG calibration factors (through InTrans Project 11-401) and, if needed, refine the local calibration coefficients of AASHTOWare Pavement ME Design pavement performance predictions for Iowa pavement systems using linear and nonlinear optimization procedures. A total of 130 representative sections across Iowa consisting of JPCP, new HMA, and HMA over JPCP sections were used. The local calibration results of AASHTOWare Pavement ME Design are presented and compared with national and locally calibrated MEPDG models.

Evaluation of the performance and cost-effectiveness of pavement sections containing open-graded base courses

Moisture induced distresses have been the prevalent distress type affecting the deterioration of both asphalt and concrete pavement sections. While various surface techniques have been employed over the years to minimize the ingress of moisture into the pavement structural sections, subsurface drainage components like open-graded base courses remain the best alternative in minimizing the time the pavement structural sections are exposed to saturated conditions. This research therefore focuses on assessing the performance and cost-effectiveness of pavement sections containing both treated and untreated open-graded aggregate base materials. Three common roadway aggregates comprising of two virgin aggregates and one recycled aggregate were investigated using four open-ended gradations and two binder types. Laboratory tests were conducted to determine the hydraulic, mechanical and durability characteristics of treated and untreated open-graded mixes made from these three aggregate types. Results of the experimental program show that for the same gradation and mix design types, limestone samples have the greatest drainage capacity, stability to traffic loads and resistance to degradation from environmental conditions like freeze-thaw. However, depending on the gradation and mix design used, all three aggregate types namely limestone, natural gravel and recycled concrete can meet the minimum coefficient of hydraulic conductivity required for good drainage in most pavements. Tests results for both asphalt and cement treated open-graded samples indicate that a percent air void content within the range of 15-25 will produce a treated open-graded base course with sufficient drainage capacity and also long term stability under both traffic and environmental loads. Using the new Mechanistic and Empirical Design Guide software, computer simulations of pavement performance were conducted on pavement sections containing these open-graded base aggregate base materials to determine how the MEPDG predicted pavement performance is sensitive to drainage. Using three truck traffic levels and four climatic regions, results of the computer simulations indicate that the predicted performance was not sensitive to the drainage characteristics of the open-graded base course. Based on the result of the MEPDG predicted pavement performance, the cost-effectiveness of the pavement sections with open-graded base was computed on the assumption that the increase service life experienced by these sections was attributed to the positive effects of subsurface drainage. The two cost analyses used gave two contrasting results with the one indicating that the inclusion of open-graded base courses can lead to substantial savings.

A methodological framework for modeling pavement maintenance costs for projects with performance-based contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-based Contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Iowa Calibration of MEPDG Performance Prediction Models, 2013

This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The required MEPDG inputs and the historical performance data for the selected sites were extracted from a variety of sources. The accuracy of the nationally-calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified to improve the accuracy of model predictions. The identified local calibration coefficients are presented with other significant findings and recommendations for use in MEPDG/DARWin-ME for Iowa pavement systems.