Smoothness Models for Hot-Mix Asphalt-Surfaced Pavements: Developed from Long-Term Pavement Performance Program Data

Abstract

The results of a study conducted to determine the relationship between changes in the surface distress of flexible pavements and incremental changes in the international roughness index (IRI) or ride quality by using Long-Term Pavement Performance (LTPP) program data are presented. The results of the regression analyses completed to identify those distresses found to be important and related to incremental changes in the IRI were obtained under the sponsorship of NCHRP Project 1-37A (Development of the 2002 Guide for the Design of New and Rehabilitated Pavement Structures). The results from the study have shown that selected distresses do have a significant effect on incremental changes in IRI with time and traffic. The results summarized can be used for the management, design, or evaluation of pavement structures.

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

“…Furthermore, in deterministic techniques, expert-based subjective assessments play a major role in the recognition of patterns in the data, which might impact the accuracy of the outcomes. Hence, to increase the reliability of the models, several studies reported the use of a combination of subjective engineering evaluation and statistical testing to find the relationships [32][33][34]. However, the subjectivity in making assumptions and simplifications are challenges of deterministic techniques.…”

“…Linear and non-linear regression is commonly used in empirical methods. In contrast with linear regression, nonlinear approaches have a better estimation because of their capability of testing multiple arrangements of condition curves [32]. When regression analysis is conducted with one variable, it is called the univariable regression method.…”

Effects of Milling and Other Repairs on Smoothness of Overlays on Asphalt Pavements

“…Characterize pavement surfaces before overlay projects begin and after they are completed. 4. Analyze the data collected to determine the effects of the explanatory variables on overlay smoothness and the benefit-to-cost ratio of different pre-overlay treatment strategies.…”

“…IRI is defined as the accumulated vertical movement of the sprung mass of one quarter of a standard vehicle divided by the distance traveled, as calculated by a computer simulation of the quarter-car operating in the wheelpath on the longitudinal profile of the measured pavement per ASTM E1926-08 ("Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements"). Von Quintus et al [4] pointed out that IRI is significantly affected not only by the initial pavement smoothness but also by pavement distresses such as fatigue cracking, transverse cracking, block cracking, and rutting, particularly when they are severe and widespread. IRI is typically expressed in the units inches per mile (in./mile) or meters per kilometer (m/km) [5].…”

Comparison of Machine Learning Techniques for Developing Performance Prediction Models

“…Due to the large number of variables and the complex ways in which they affect one another, use of simple statistical approaches such as linear regression is not a viable means to develop pavement roughness prediction models. In addition, the shape of pavement performance curve is not known beforehand and multiple arrangements have to be tested in order to develop a model using nonlinear regression (Von Quintus et al 2001). Hence, studies have attempted to use computational intelligence techniques such as artificial neural networks (ANN) to develop more accurate models (Attoh-Okine 1994;Kargah-Ostadi et al 2010).…”