Modelling flexible pavement response and performance

ERJ. Engineering Research Journal

In recent years, overinflated tire pressure and the consequence of increased heavy vehicles` axle loads on flexible pavements responses have become a major source of worry, because of the higher stress levels induced within the flexible pavement which leads to extra damage. As a result, this research aims to assess the performance of the flexible pavement under varied axle loads and tire pressures of different trucks in Egypt. The 3D-Move V2.1 analysis program is a tool used to calculate the stresses and strains within pavement layers. The main conclusions that can be drawn from the analysis of the results is that there is a direct relation between pavement responses in terms of vertical strain z-z (Ɛz-z), normal strain x-x (Ɛx-x), and vertical displacement (V d) with each of tire pressure and axle load. Furthermore, the pavement responses are affected more by load than tire pressure. The Ɛ z-z is influenced not only by vertical stresses, but also by normal and radial stresses and the elastic modulus of the layer. Also, vertical strain developed at the bottom of the asphalt layer and the subgrade is not affected significantly by tire pressure. In addition, the effects of tire pressure on the horizontal strain at the bottom of the asphalt layer is much more than on the compressive strain above the subgrade. The important conclusion is increasing of wheel loads have a greater impact on rutting deterioration than fatigue. However, increasing tire pressure has a greater impact on fatigue deterioration than rutting.

2008

The mechanistic empirical method of flexible pavement design/assessment uses a large number of numerical truck model runs to predict a history of dynamic load. The pattern of dynamic load distribution along the pavement is a key factor in the design/assessment of flexible pavement. While this can be measured in particular cases, there are no reliable methods of predicting the mean pattern for typical traffic conditions. A simple linear quarter car model which aims to reproduce the mean and variance of dynamic loading of the truck fleet at a given site is developed here. This probabilistic model reflects the range and frequency of the different heavy trucks on the road and their dynamic properties. Multiple Sensor Weigh-in-Motion data can be used to calibrate the model. Truck properties such as suspension stiffness, suspension damping, sprung mass, unsprung mass and tyre stiffness are represented as randomly varying parameters in the fleet model. It is used to predict the statistical distribution of dynamic load at each measurement point. The concept is demonstrated by using a predefined truck fleet to calculate a pattern of statistical spatial repeatability and is tested by using that pattern to find the truck statistical properties that generated it. r

KSCE Journal of Civil Engineering, 2014

Maintenance and repair of the highway network system are major expenses in the state budget. For this reason various concerned organizations are pointing out the need for developing an intelligent and efficient pavement performance model that can prioritize pavement maintenance and rehabilitation works. Such models can forecast the remaining pavement service life and pavement rehabilitation needs, and can help in the formulation of pavement maintenance and strengthening programmes which will reduce the road agency and road user costs. The flexible pavement performance or deterioration models involve the complex interaction between vehicles and the environment, and the structure and surface of the pavement. Performance models relating to the pavement distress conditions like, cracking, raveling, potholing, and roughness are analyzed and developed by various researchers. But most of these models are found applicable to a particular set of traffic or environment conditions, thus highlighting the need of model(s) that can work in varied conditions satisfactorily. The paper presents a detailed review of various pavement performance models to examine the role of factors related to pavement materials, environmental conditions, type of traffic and volume of traffic, and to identify the limitations and gaps in the present knowledge on such models.

Pavement structural design is a daunting task. Traffic loading is a heterogeneous mix of vehicles, axle types and loads with distributions that vary daily and over the pavement design life. Pavement materials respond to these loads in complex ways influenced by stress state and magnitude, temperature, moisture, loading rate, and other factors. Environment exposure adds further complications. It should be no wonder the profession has resorted to largely empirical methods. Developments over recent decades offered an opportunity for more rational and rigorous pavement design procedures. The latest of these accomplishments is the development of the mechanistic-empirical pavement design procedure in NCHRP Project 1-37A. This study presents a comparison of flexible pavement designs between the 1993 AASHTO guide and the NCHRP 1-37A methodology and a sensitivity analysis of the NCHRP 1-37A's input parameters. Recommendations for future studies involving the application and implementation of the new mechanistic-empirical pavement design guide concludes the study.

2006

Pavement structural design is a daunting task. Traffic loading is a heterogeneous mix of vehicles, axle types and loads with distributions that vary daily and over the pavement design life. Pavement materials respond to these loads in complex ways influenced by stress state and magnitude, temperature, moisture, loading rate, and other factors. Environment exposure adds further complications. It should be no wonder the profession has resorted to largely empirical methods. Developments over recent decades offered an opportunity for more rational and rigorous pavement design procedures. The latest of these accomplishments is the development of the mechanistic-empirical pavement design procedure in NCHRP Project 1-37A. This study presents a comparison of flexible pavement designs between the 1993 AASHTO guide and the NCHRP 1-37A methodology and a sensitivity analysis of the NCHRP 1-37A's input parameters.

Transportation Research Record: Journal of the Transportation Research Board, 2002

A dynamic load test study was performed on instrumented asphalt and concrete pavement test sections at the Minnesota Road Research facility. Test variables included different types of vehicles (featuring various axle groupings, load levels, and tire pressures) operating at various speeds over different structural sections. Four flexible pavement sections were selected for inclusion, and the primary structural response measured was horizontal strain at the bottom of the asphalt layer. The test data suggest that the structural response of the four sections to varying loads was linear when other test parameters were held constant. The pavement sections also exhibited a pronounced viscoelastic behavior in response to changes in vehicle speed and load rate. This behavior was attributed mainly to the asphalt concrete layer. Changes in tire pressure did not significantly affect pavement behavior. The test data were used to validate a multilayer linear elastic pavement structural model. Sub...

Transportation Research Record: Journal of the Transportation Research Board, 2009

The Mechanistic-Empirical Pavement Design Guide (MEPDG) uses performance models to predict cracking and rutting in flexible pavements. A unique mechanism controls the initiation and accumulation of each distress, but each mechanism can have several causes. Axle repetitions and loads are the main causes of all load-related distress types. MEPDG incorporates axle load spectra to characterize axle loading for a site and uses them to calculate pavement response and damage accumulation. These load distributions have a bimodal shape, and a mixture of two continuous distributions can be used to model them. In this paper, closed-form solutions are developed to estimate the characteristics of a mixture of bimodal axle load distributions. The observed axle load spectra from 14 sites in different states were used to relate load distribution characteristics to predicted flexible pavement performance. The overall mean and other characteristics of a bimodal axle load distribution explained the variations in expected flexible pavement performance. Cracking, surface rutting, and ride quality are related to the fourth root of the fourth moment of axle load distributions. Rutting in the hot-mix asphalt layer is strongly associated with the overall mean, but in base and subbase layers it is related to the 95th percentile load of axle load spectra. These findings imply that cracking, rutting, and roughness growth in flexible pavements are caused mainly by axle load distributions having heavier tails with infrequent extreme loads. Heavier loads appear to cause more cracking; a higher number of load repetitions is more critical in developing additional surface rutting in flexible pavements. Axle load spectra were used to develop the Mechanistic-Empirical Pavement Design Guide (MEPDG). Use of these load distributions provides a direct and rational approach for the analysis and design of pavement structures to estimate the effects of actual traffic on seasonal pavement response and distress. In the AASHTO Guide for Design of Pavement Structures, a mixed traffic stream of different axle loads and configurations is converted into a design traffic number by transforming each expected axle load into an equivalent number of 18-kip single-axle loads, known as equivalent single-axle loads (ESALs) (1). Load equivalency factors are used to determine the number of ESALs for each axle load and configuration. These factors are based on the present serviceability index (PSI) concept and depend

Journal of Civil Engineering and Architecture

The KDOT (Kansas Department of Transportation) is currently adopting MEPDG (mechanistic-empirical pavement design guide) to replace the 1993 AASHTO (American Association of State Highway and Transportation Officials) design method. The main objective of this study was to compare flexible pavement design using 1993 AASHTO design guide and MEPDG. Five newly built Superior PERforming Asphalt PAVEments (Superpave), designed using the 1993 AASHTO Design Guide, were selected as test sections for the design simulation study. Deflection data were collected approximately 8 to 10 weeks after construction using FWD (falling weight deflectometer). The FWD deflection data were used to back-calculate the pavement layer moduli using three different back-calculation programs. The existing pavement structures were analyzed for a 10-year analysis period. The maximum numbers of years the existing pavement structures will be in a serviceable condition as well as the minimum thicknesses of different layers to serve for 10-years were also determined. Effects of changing subgrade modulus, target distress, and reliability were also investigated. The MEPDG design analysis shows that the 1993 AASHTO Guide-designed flexible pavements do not show the distresses currently observed in Kansas for the 10-year design period. The MEPDG design simulation shows that the thinner the pavement sections, the higher the permanent deformation. The existing pavement structures can serve for more than 20 years as per the MEPDG design analysis if the default failure criteria and nationally-calibrated models are used.

2015

This thesis is lovingly dedicated to our anticipated baby. This thesis also dedicated to my husband, Abdul Motin for his support and encouragement. Figure 3.3: Locations of (a) starting and (b) ending positions for the influence line analysis with standard truck as shown in Fig. 3.2. The successive positions of axle load are given in Table 3. Figure 3.4: Locations of starting positions for the influence line analysis with Michigan trucks (MI-20, MI-14, and MI-13). The first axle of first tandem is placed at the right side of the left transverse joint of the mid slab. The successive and ending positions of the axles loading are given in Table 3. Figure 3.6: Tensile stresses at the top surface of mid-slab along the longitudinal edge with 14-feet joint spacing and asphalt shoulders for the standard truck.