Erdem Coleri

Pavement Rutting Performance Prediction by Integrated Weibull Approach

This paper demonstrates the applicability of the integrated Weibull approach to simulate the in situ rutting performance of asphalt concrete mixes by applying appropriate correction factors to laboratory models. The goal was to verify and calibrate the laboratory models according to accelerated pavement test results. The results of the repeated simple shear test at constant height (RSST-CH) were used to estimate the permanent deformation accumulation mechanisms. General regression equations were shown to successfully represent the Stage I and Stage II permanent deformation accumulation phases for mixes containing different binder types according to the results of RSST-CH. Correction factors were used to calibrate the laboratory equations according to the deflection data from four heavy vehicle simulator test sections to estimate in situ rutting performance. The results indicate that phase separation occurs at higher repetition values with increasing shear stress. Moreover, only Stage I of the laboratory models was actually valid at the high shear stress levels used for in situ rutting performance prediction. The simulations further confirm that the integrated Weibull approach is a successful and reliable method for prediction of the in situ rutting performance of flexible pavements and provides high coefficient of determination values.

In-pavement wireless weigh-in-motion

Truck weight data is used in many areas of transportation such as weight enforcement and pavement condition assessment. This paper describes a wireless sensor network (WSN) that estimates the weight of moving vehicles from pavement vibrations caused by vehicular motion. The WSN consists of: acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicles arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The paper also describes a novel algorithm that estimates a vehicles weight from pavement vibration and vehicle detection data, and calculates pavement deflection in the process. A prototype of the system has been deployed near a conventional Weigh-In-Motion (WIM) system on I-80 W in Pinole, CA. Weights of 52 trucks at different speeds and loads were estimated by the system under different pavement temperatures and varying environmental conditions, adding to the challenges the system must overcome. The error in load estimates was less than 10% for gross weight and 15% for individual axle weights. Different states have different requirements for WIM but the system described here outperformed the nearby conventional WIM, and meets commonly used standards in United States. The system also opens up exciting new opportunities for WSNs in pavement engineering and intelligent transportation.

Short-Term Laboratory Conditioning of Asphalt Mixtures

This report develops procedures and associated criteria for laboratory conditioning of asphalt mixtures to simulate short-term aging. The report presents proposed changes to the American Association of State Highway and Transportation Officials (AASHTO) R 30, Mixture Conditioning of Hot-Mix Asphalt (HMA), and a proposed AASHTO practice for conducting plant aging studies. The report will be of immediate interest to materials engineers in state highway agencies and the construction industry with responsibility for design and production of hot and warm mix asphalt.

Prediction of Subgrade Resilient Modulus Using Genetic Algorithm and Curve-Shifting Methodology: Alternative to Nonlinear Constitutive Models

This paper demonstrates the applicability of the genetic algorithm and curve-shifting methodology to the estimation of the resilient modulus at various stress states for subgrade soils by using the results of triaxial resilient modulus tests. This innovative methodology is proposed as an alternative to conventional nonlinear constitutive relationships. With the genetic algorithm, laboratory curves for different deviator stress levels at different confining pressures are horizontally shifted to form a final gamma distribution curve that can represent the stress–strain behavior of subgrade soils with the corresponding predicted shift factors. Resilient modulus values for a given stress state can be estimated on the basis of this curve and another gamma function that represents the variation of the shift values for different confining stresses. To compare the effectiveness of these two approaches, coefficients for the Uzan constitutive model were also determined for each laboratory test and compared with those determined by the approach described in this paper. Predicted resilient modulus values from each approach are separately compared with artificial neural network (ANN) model predictions to evaluate their efficiency and reliability for resilient response prediction. The results of the analysis indicated that the curve-shifting methodology gave superior estimates and a coefficient of determination 14% higher than the Uzan model predictions when the results were evaluated with the ANN model outputs. Thus, although it is not a constitutive model, use of the genetic algorithm and curve-shifting methodology is proposed as a promising technique for the evaluation of the stress–strain dependency of subgrade soils.

Composite Pavement Systems Volume 1: HMA/PCC Composite Pavements

Composite pavements have proved in Europe and the United States to have long service life with excellent surface characteristics, structural capacity, and rapid renewal when needed. This project developed the guidance needed to design and construct new composite pavement systems. Volume 1 presents the state of the practice and guidelines for designing and constructing new hot-mix asphalt (HMA) concrete over a portland cement concrete (PCC) composite pavement that takes full advantage of using differing materials. Volume 2 provides guidance on the design and construction of two-layer, wet-on-wet PCC pavements where the upper layer is a thin high-quality layer (hard nonpolishing aggregate, higher cement content, higher quality binder) and excellent surface characteristics with the lower layer containing a higher percentage of local aggregates and recycled materials. Both volumes detail performance data on existing composite pavement systems and provide step-by-step guidance on the design of composite pavements using mechanistic-empirical design methods for both types of new composite pavements.

Model Development, Field Section Characterization, and Model Comparison for Excess Vehicle Fuel Use Attributed to Pavement Structural Response

In this study, consumption of energy attributed to pavement structural response through viscoelastic deformation of asphalt pavement materials under vehicle loading was predicted for 17 field sections in California by using three different models. Calculated dissipated energy values were converted to excess fuel consumption (EFC) to facilitate comparisons under different traffic loads (car, SUV, and truck) and speeds and different temperature conditions. The goal of the study was to compare the different modeling approaches and provide first-level estimates of EFC in preparation for simulations of annual EFC for different traffic and climate scenarios, as well as different types of pavement structures on the California state highway network. Comparison of the predicted EFC for test sections showed that all three models produced different results, which can be attributed to the differences in the three modeling approaches. However, predictions from the three models were generally of the same order of magnitude or an order of magnitude different, indicating that overall these models can be calibrated with data from field measurements, which is the next step in the research program.

Evaluation of Laboratory, Construction, and Performance Variability by Bootstrapping and Monte Carlo Methods for Rutting Performance Prediction of Heavy Vehicle Simulator Test Sections

This paper demonstrates an innovative reliability analysis approach for prediction of asphalt rutting performance. In this approach, reliability was evaluated by considering the variability in laboratory test results, layer thicknesses, stiffnesses, and measured in situ performance. The effects of input design parameters variability on predicted performance were determined using the calculated distributions of calibration coefficients. To assess the contribution of each input parameter’s precision to the precision of calculated calibration coefficients, various cases were created by including and excluding the variability in these parameters in the calibration process. These distributions were also used for rutting performance prediction and reliability evaluation of highway sections. In this way, rut depths for different reliability levels can be predicted without performing computationally intensive calculations within the design software. The results indicated that distributions of calibration coeffici...

Sensor Network for Pavement Performance Monitoring

Preventive maintenance of roadway pavements requires reliable estimates of pavement deterioration. Existing successful measurements of pavement deterioration rely on knowing the statistical distribution of traffic loads in the roadway. Existing systems are expensive to deploy. This paper describes a sensor network system for road pavement monitoring. It is capable of estimating the load of trucks in real-time and utilizes low deployment and maintenance cost wireless sensors. The approach relies on combining careful pavement models with data measured in real time and provides a reliable estimate of truck loads. Based on this estimates we are able to infer various pavement performance metrics under strict bounds.

RUTTING OF RUBBERIZED GAP GRADED AND POLYMER MODIFIED DENSE GRADED ASPHALT OVERLAYS IN COMPOSITE PAVEMENTS

The rutting performance of polymer-modified dense-graded and rubberized gap-graded asphalt mixes used in composite pavement was evaluated with full-scale accelerated pavement testing with the heavy vehicle simulator (HVS) and laboratory test results. The effect of asphalt layer thickness on measured surface deformation for both mix types was also investigated for recommendations for future design. In addition, the progression of the rutting failure mechanism for both mix types was evaluated with transverse cross sections measured with a laser profilometer at various HVS load repetitions. The polymer-modified dense-graded mix performed better than the rubberized gap-graded mix under both laboratory and HVS testing. The greater shear movement of the rubberized gap-graded mix under HVS loading caused larger humps, which consequently increased the maximum deformation and resulted in earlier failure. Larger aggregate size and denser gradation for the polymer-modified dense-graded mix resulted in more efficient dissipation of shear stresses and created greater permanent deformation resistance.

Uniaxial Shear Tester – new test method to determine shear properties of asphalt mixtures

The main focus of the paper is to present the concept of a newly developed Uniaxial Shear Tester (UST) and to investigate the correlation between results from the UST and the Superpave Shear Tester (SST), a tool broadly recognised for asphalt mix design and rutting susceptibility evaluation. In this study, the UST testing principles, finite element analysis of stresses and comparison of measured data are presented. The correlation was assessed on the basis of two tests, the repeated shear test and the small amplitude oscillation test also referred as the shear frequency sweep test. It was shown that the material characteristics determined from UST and SST are highly correlated. The dependencies are discussed in the sense of linear correlation and correlation coefficients. Test variability is discussed in the paper.