Danny X Xiao

Development of Truck Loading Groups for the Mechanistic-Empirical Pavement Design Guide

A key to the use of weigh-in-motion (WIM) traffic data for the Mechanistic-Empirical Pavement Design Guide (MEPDG) is to be able to successfully recognize the differences in loading patterns and to estimate the full axle-load spectrum data occurring under different conditions. However, how to identify these patterns on the basis of the large amount of WIM data remains a challenge. In this paper, WIM data collected in the state of Arkansas are analyzed by using cluster analysis methodologies to identify groups of WIM sites with similar traffic characteristics on the basis of the MEPDG-required traffic attributes. Case studies are presented and the cluster results are discussed. Combining the loading clusters, four long-term transportation planning factors currently adopted in Arkansas, including the modified Arkansas primary highway network (APHN) classification, demography, geography, and region attribute (rural or urban) of a highway under design, are adopted as the influencing criteria to develop the tr...

Mechanistic-empirical pavement design guide (MEPDG): a bird's-eye view

Past editions of the American Association of State Highway and Transportation Officials (AASHTO) Guide for Design of Pavement Structures have served well for several decades; nevertheless, many serious limitations exist for their continued use as the nation’s primary pavement design procedures. Researchers are now incorporating the latest advances in pavement design into the new Mechanistic-Empirical Pavement Design Guide (MEPDG), developed under the National Cooperative Highway Research Program (NCHRP) 1-37A project and adopted and published by AASHTO. The MEPDG procedure offers several dramatic improvements over the current pavement design guide and presents a new paradigm in the way pavement design is performed. However, MEPDG is substantially more complex than the AASHTO Design Guide by considering the input parameters that influence pavement performance, including traffic, climate, pavement structure and material properties, and applying the principles of engineering mechanics to predict critical pavement responses. It requires significantly more input from the designer. Some of the required data are either not tracked previously or are stored in locations not familiar to designers, and many data sets need to be preprocessed for use in the MEPDG. As a result, tremendous research work has been conducted and still more challenges need to be tackled both in federal and state levels for the full implementation of MEPDG. This paper, for the first time, provides a comprehensive bird’s eye view for the MEPDG procedure, including the evolvement of the design methodology, an overview of the design philosophy and its components, the research conducted during the development, improvement, and implementation phases, and the challenges remained and future developments directions. It is anticipated that the efforts in this paper aid in enhancing the mechanistic-empirical based pavement design for future continuous improvement to keep up with changes in trucking, materials, construction, design concepts, computers, and so on.

Calibration of the Mechanistic-Empirical Pavement Design Guide for Flexible Pavement Design in Arkansas

Because of potential differences between national and local conditions, the Mechanistic–Empirical Pavement Design Guide (MEPDG) should be calibrated to a local level. Arkansas has invested heavily in efforts to implement the MEPDG. This paper summarizes the initial local calibration of flexible pavement models in the MEPDG for Arkansas. Data from the Long-Term Pavement Performance (LTPP) database and local pavement management system (PMS) were used. The solver function in Microsoft Excel was used to optimize the coefficients for alligator cracking. Iterative runs of the MEPDG by means of discrete calibration coefficients were conducted to optimize rutting models. In general, the alligator cracking and rutting models are improved by calibration. However, a question remains about the suitability of the calibrated models for routine design. Many default values were used in the MEPDG because of a lack of data. It is recommended that additional sites be established and a more robust data collection procedure be implemented for future calibration efforts. The difference in the definitions of transverse cracking between the MEPDG and the LTPP may be critical to data collection and identification. Thermal cracking should be specifically identified in a transverse cracking survey to calibrate the transverse cracking model in MEPDG. The procedure using LTPP and PMS data for local calibration of the MEPDG in Arkansas is established. Additional development of database software for data manipulation, preprocessing, and quality control—under way in Arkansas—will significantly streamline the calibration process.

Cracking Classification Using Minimum Rectangular Cover–Based Support Vector Machine

AbstractCracking characterization is one of the most important tasks in automated pavement data analysis. Although cracking detection and segmentation algorithms have become more reliable in recent...

Reliability-Based Mechanistic-Empirical Pavement Design with Statistical Methods

“Reliability,” as defined in the Mechanistic–Empirical Pavement Design Guide (MEPDG), is an aggregated indicator defined as the probability that each of the key performance measures will be less than a selected critical level over the design period. Being such a complex system, the MEPDG—which is not a single closed-form design equation—cannot depend on classic reliability methods. Monte Carlo simulation is suitable for a robust reliability analysis but is impractical because of the extensive computation time required for any reasonable analysis with the MEPDG. The development of surrogate models for performance predictions becomes an option to represent the comprehensive modeling capability of the MEPDG efficiently. Improvements to the MEPDG reliability model based on several statistical methods are described. Five tasks are detailed. First, the MEPDG is calibrated to local conditions to reduce potential bias and variation from the national calibration. Then, risk analysis and screen analysis are conducted to determine the significant variables to include in surrogate models. Next, a comprehensive experimental design effectively plans MEPDG simulations. Then, response surface models of MEPDG are built through regression analysis. Finally, probabilistic design is achieved by Monte Carlo simulation. The result of these efforts is a state-specific MEPDG-based probabilistic pavement design tool kit named ReliME. This framework provides engineers more flexibility in data acquisition, design alternatives, optimization, and quality control. The frameworks successful application in Arkansas could easily be used in other states and incorporated into future mechanistic–empirical pavement design software.

Mechanistic-Empirical Pavement Design Guide-Based Pavement Design Catalog for Low-Volume Roads in Arkansas

Historically, low-volume roads in Arkansas were typically constructed by use of a standard section, that is, a double surface treatment over a specified thickness of granular base. Subsequent analysis indicated that these sections were structurally inadequate in many cases. In recent years, the Arkansas State Highway and Transportation Department has invested significant research dollars to implement the Mechanistic–Empirical Pavement Design Guide (MEPDG), which is widely believed to be a quantitative leap over the 1993 AASHTO design guide. However, the MEPDG research efforts mostly target high-volume roads. In this paper, a design catalog for low-volume roads (LVRs) in Arkansas was developed with MEPDG software, Version 1.10. The catalog offers a variety of feasible design alternatives for a comprehensive combination of site conditions. The factors considered include the five geographical regions in Arkansas and the typical Arkansas load spectrum for LVRs with three traffic levels, three subgrade types, and six potential aggregate types available in Arkansas that can be used as granular base and surface layer aggregates. All the MEPDG design inputs needed for the development of the design catalog are generated on the basis of the variety of previous MEPDG implementation research conducted in the state of Arkansas. It is anticipated that the design catalog will serve as a simplified and rational design process for the LVRs in Arkansas.

A Data-Driven Method for Comprehensive Pavement-Condition Ranking

AbstractState highway agencies need pavement-condition data to select candidates for pavement maintenance and rehabilitation. However, it is a challenge for pavement engineers to simultaneously assess a number of attributes that represent different aspects of pavement condition. In conventional practice, empirical comprehensive evaluation methodologies, such as fuzzy set theory and analytical hierarchy process, have been used to aggregate multiple distresses into integrated pavement-performance indices. These methodologies, however, are mostly based on experts’ or engineers’ judgment rather than data-driven approaches. In this paper, a framework of applying a data-driven approach to conduct comprehensive pavement evaluation and ranking is presented. The method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is introduced and applied to rank pavement sections with various evaluation attributes. Principal component analysis (PCA) is employed to combine distresses with high correla...

Risk Analysis of Mechanistic-Empirical Pavement Design Methods

Pavement design is moving from empirical methods to mechanistic-empirical methods, such as the Mechanistic-Empirical Pavement Design Guide (MEPDG) developed in the USA. Since it is a more complicated design and modeling tool, it is necessary to conduct a risk analysis of the new system. Moreover, it is well accepted that uncertainty and risk (i.e. the variation of materials, forecasting of future traffic) should be understood and considered in pavement design. Different from conventional sensitivity analysis on design software, this paper considers the design process as a system and investigates the risk and uncertainty in the system using the Hierarchical Holographic Modeling (HHM) and the Analytical Hierarchy Process (AHP). In total, 72 parameters are compared and ranked to find critical factors influencing the success of flexible pavement design. This paper could give a “out of box” view of pavement design using mechanistic-empirical methods and aid engineers in focusing on inputs that have the most effect on pavement design.

Analysis of Road Surface Heat Flux Based on Energy Balance Theory

Understanding the interaction between pavements and the ambient temperature is critical for mitigating urban heat islands. The objective of this paper is to investigate the variation of heat flux from different pavement materials based on a field experiment in Ningbo, China. One-year observation, including total surface solar radiation, reflective radiation and net total radiation, are analyzed. Quantitative variations of the heat flux are then determined using energy balance theory. Results show that the average daily heat flux (Qd) of asphalt pavements is higher than that of concrete pavements. Seasonally, summer has a larger Qd than spring has, and winter has the smallest Qd. On average, the daily heat flux varies from 4 to 12 W/m². Weather condition influences heat flux. Ranking from the maximum to the minimum are clear days (11.8 W/m²), cloudy days (7.9 W/m²), and rainy days (4.2 W/m²).

Preparation of Joint Faulting Data for the Local Calibration of AASHTO Pavement Mechanistic–Empirical Design in Louisiana

Louisiana utilized performance data from the pavement management system (PMS) to evaluate and calibrate the AASHTO Pavement Mechanistic–Empirical (ME) Design. Analysis of the PMS faulting data revealed that there were no records between 0 and 0.2 in. (5 mm); others over 0.2 in. (5 mm) appeared to be much greater than would be expected based on engineering experience. Therefore, several tasks were completed to validate the PMS faulting data and prepare them for local calibration. This paper presents details of the problem, approach, results, and lessons learned. First, faulting data from the PMS and Long-Term Pavement Performance database were analyzed to have an overview of the common range of joint faulting. To validate the PMS faulting data, 43 representative projects across Louisiana were selected for further analysis. Longitudinal profiles were collected with high-speed profilers and analyzed with the AASHTO R36 automated faulting measurement (AFM) algorithms. Manual measurements were also conducted during site visits. The comparison of faulting from different methods showed that the PMS data extremely overestimated faulting compared with the AFM estimation or the manual measurement. Results from the AFM algorithm were much closer (in the same magnitude) to the manual measurements. Therefore, faulting data from the AFM algorithm were used, and the faulting model was successfully calibrated. It is recommended to evaluate PMS faulting data carefully before applying them to calibrate the AASHTO Pavement ME Design software. Automated faulting measurement based on high-speed profiles is a feasible approach.