Development of network-level pavement deterioration curves using the linear empirical Bayes approach

Modelling the pavement deterioration process is essential for a successful pavement management system (PMS). The pavement deterioration process is highly influenced by uncertainties related to data acquisition and condition assessment. This paper presents a novel approach for predicting a pavement deterioration index. The model builds on a negative binomial (NB) regression used to predict pavement deterioration as a function of the pavement age. Network-level pavement condition models were developed for interstate, primary, and secondary pavement road families and were compared with traditional non-linear regression models. The linear empirical Bayesian (LEB) approach was then used to improve the predictions by combining the deterioration estimated by the fitted model and the observed/measured condition recorded in the PMS. The proposed approach can improve the mean square error prediction of the next-year pavement condition by 33%, 36% and 41% for Interstate, Primary, and Secondary roads, respectively, compared with the measured pavement condition without further modelling of the pavement deterioration

Measurement Error Models (MEMs) Regression Method to Harmonize Friction Values from Different Skid Testing Devices

Skid measurement errors are unavoidable for each kind of skid testing device. The simple linear regression (SLR), used worldwide to harmonize friction measuring devices, does not consider that measurement errors affect both devices. For this reason, its use provides a biased estimate of the relationship between devices. The measurement error models (MEMs) regression method is proposed as a better method to harmonize any two skid testing devices. Regression according to both the SLR and MEM approaches have been performed with repeated measurements (from the same device) and between measurements obtained from two different skid testing devices. A comparison of the results is shown; MEM regression appears to be a more appropriate tool to harmonize friction measuring devices than SLR

Enhancing pavement surface macrotexture characterization by using the effective area for water evacuation

Adequate macrotexture characterization is an essential objective for transportation practitioners because primary pavement surface characteristics like friction, tire-pavement noise, splash and spray, and rolling resistance are significantly influenced by pavement macrotexture. This paper proposes an enhanced macrotexture characterization index based on the effective area for water evacuation (EAWE) that better estimates the potential of the pavement to drain water and provides improved correlations with two properties of pavement surfaces that are predominantly affected by macrotexture: friction and noise. A three-step methodology is proposed to compute the index: (a) a spikeremoval procedure that assures the reliability of the texture profile data (b) an enveloping profile calculation, which is necessary to delimit the area between the tire and the pavement when contact occurs and (c) a definition of the EAWE, which serves as the index for characterizing macrotexture. Comparisons of current mean profile depth (MPD) and proposed EAWE macrotexture indexes by using 32 pavement sections confirmed that MPD overestimated the effective area for water evacuation between a tire and the pavement surface. Correlations for MPD and EAWE indexes with tire-pavement friction and noise were performed, and measurable improvements in correlations were achieved. Results show that it is possible to define a promising index on the basis of the EAWE that realizes advantages over MPD