Determination of Friction Performance of High Friction Surface Treatment Based on Alternative Macrotexture Metric

Surface friction is currently the most common metric for evaluating the performance of high friction surface treatment (HFST). However, friction test methods such as the locked wheel skid tester (LWST) commonly provide a spot measurement. Large variations may arise in the LWST testing on curves. Based on 21 actual HFST projects, a study was performed to use a macrotexture metric, i.e., the mean profile depth (MPD) to evaluate HFST’s performance and improve its quality control (QC)/quality assurance (QA) procedures. The material properties were presented to understand the aspects of HFST. The method for calculating MPD was modified to account for the variations of macrotexture measurements. A vehicle-based test system was utilized to measure MPD periodically over an 18-month period since HFST installation. Statistical analysis was performed on the MPD measurements to identify the effects of influencing factors. Compared with the friction from LWST, MPD was equally effective in evaluating HFST performance. However, the use of MPD eliminated the errors as arisen in LWST testing and made it possible to detect surface distresses, including aggregate loss, delamination, and cracking. The expected overall MPD may be calculated by combining the MPD measurements made three months after installation at different HFST sites and used as a metric for evaluating HFST performance and QC/QA.

Estimation of aggregate losses of secondary cancer cases using PH Panjer class (a,b,1) distributions,

This research in-cooperates phase type distributions of Panjer class \((a,b,1)\) in estimation of aggregate loss probabilities of secondary cancer. Matrices of the phase type distributions are derived using Chapman-Kolmogorov equation and transition probabilities estimated using modified Kaplan-Meir and consequently the transition intensities and transition probabilities. Stationary probabilities of the Markov chains represents $\vec{\gamma}$. Claim amount are modeled using OPPL, TPPL, Pareto, Generalized Pareto and Wei-bull distributions. PH ZT Poisson with Generalized Pareto distribution provided the best fit.

High Friction Surface Treatment Deterioration Analysis and Characteristics Study

High friction surface treatment (HFST) is used to improve friction on curved roadways, especially on curves that have a history of wet pavement crashes. Observations on the long-term performance monitoring of HFST sections at the National Center for Asphalt Technology (NCAT) Test Track showed friction (skid number, SN) dropped significantly at the end of service life of HFST, creating unsafe driving conditions. There is no clear, observed friction deterioration trend to predict the friction drop when using friction performance measures like SN. Therefore, there is an urgent need to explore and develop supplementary HFST safety performance measures (such as aggregate loss) that can correlate to friction deterioration and provide predictable, cost-effective, and easily measurable results. The objectives of this paper are to (i) analyze the correlation between HFST aggregate loss percentage area and friction value using a dynamic friction tester (DFT), and (ii) study the characteristics of HFST deterioration associated with aggregate loss, at the NCAT Test Track and at selected HFST curve sites in Georgia (using 2D imaging and high-resolution 3D laser scanning). Results show a strong correlation between HFST aggregate loss percentage area and DFT friction coefficient. Where friction measurement is used as the primary safety performance measure, it is recommended that HFST aggregate loss be used as a supplementary performance measure for monitoring the HFST safety performance deterioration. Aggregate loss can be easily identified by characteristics such as color and texture change. Preliminary texture analyses of 3D HFST surface profiles show lower mean profile depth (MPD) and ridge-to-valley depth (RVD) texture indicators can also identify loss of aggregate spots on HFST surface.

Analysis of High-Friction Surface Texture with Respect to Friction and Wear

High Friction Surfaces (HFS) are applied to increase friction capacity on critical roadway sections, such as horizontal curves. HFS friction deterioration on these sections is a safety concern. This study deals with characterization of the aggregate loss, one of the main failure mechanisms of HFS, using texture parameters to study its relationship with friction. Tests are conducted on selected HFS spots with different aggregate loss severity levels at the National Center for Asphalt Technology (NCAT) Test Track. Friction tests are performed using a Dynamic Friction Tester (DFT). The surface texture is measured by means of a high-resolution 3D pavement scanning system (0.025 mm vertical resolution). Texture data are processed and analyzed by means of the MountainsMap software. The correlations between the DFT friction coefficient and the texture parameters confirm the impact of change in aggregates’ characteristics (including height, shape, and material volume) on friction. A novel approach to detect the HFS friction coefficient transition based on aggregate loss, inspired by previous works on the tribology of coatings, is proposed. Using the proposed approach, preliminary outcomes show it is possible to observe the rapid friction coefficient transition, similar to observations at NCAT. Perspectives for future research are presented and discussed.

Welfare Effects of Capital Controls

This paper studies the effect of capital controls on misallocation and welfare in an economy with financial constraints. We build a general equilibrium model with heterogeneous firms, financial constraints and international trade and calibrate it to the Chilean economy. Since high-productivity and exporting firms need to borrow more to reach their optimal scale, capital controls that tax international borrowing hit them harder. As a result, misallocation increases relatively more for this group of firms, and for young firms that are still trying to reach their optimal scale. In terms of welfare, the model predicts a sizable aggregate loss of 2.39 percent when capital controls are introduced, with welfare decreasing twice as much for high-productivity firms. We empirically corroborate the main insights in terms of misallocation obtained from the model using Chilean manufacturing firm data from 1990 to 2007.