Hierarchical asphalt pavement deterioration model for climate impact studies

Quantification of the impacts of projected climate change on road pavement performance is possible using predictive models that correctly consider key causal factors of pavement deterioration. These factors include climate, traffic, properties of materials and the design of pavements. This paper presents a new model developed to predict rutting in asphalt surfacing. In addition to the key causal factors of road deterioration, the developed model takes into account several sources of uncertainties, particularly those inherent in future climate change predictions and model input parameters. The asphalt surfacing rut depth progression model was developed from a hierarchical road network data structure using a Bayesian regression approach resulting in a model for each surfacing group. The model was applied within a Monte Carlo simulation framework to derive probabilistic outputs of pavement rut depth progression and maintenance costs under the pre-determined future climate scenarios. This model is useful for application at both the network and project levels to develop road management strategies and policies.     

Rutting progression models for light duty pavements

Deterioration models allow road managers to assess current condition and to predict future conditions of their road networks. Due to heavy vehicle axle repetitions and the effect of environmental factors, permanent deformation (rutting) develops gradually in the wheel paths and impacts on structural and surface performance of flexible pavements. This paper reports the approach adopted to develop absolute deterministic models for permanent deformation of low volume roads. A representative large sample network (23 highways) of light duty pavements was selected. For each section, time series data from four consecutive condition surveys were collected. Multiple regression analysis was carried out to develop models to predict pavement rutting progression over time as a function of a number of contributing variables. They include traffic loading, pavement strength, climate and drainage condition. For more powerful prediction, family group data-fitting approach was utilised to estimate future rutting progression based on the average rut depth curve for a series of pavements with similar characteristics. This study highlighted that separate family deterioration models are preferred and needed for more realistic results. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data with low standard error values. Also, study results show that higher traffic loading, lower pavement strength, poor drainage and climates with high seasonal variation contribute to increasing rutting progression rate.     

Back-calculation of transition probabilities for Markovian-based pavement performance prediction models

This paper presents a new technique to estimate the transition probabilities used in the Markovian-based pavement performance prediction models. The proposed technique is based on the ‘back-calculation’ of the discrete-time Markov model using only two consecutive cycles of pavement distress assessment. The transition probabilities, representing the pavement deterioration rates, are the main elements of the Markov model used in predicting future pavement conditions. The paper also presents a simplified procedure for evaluating the pavement state of distress using the two major pavement defect groups, namely cracking and deformation. These two defect groups are to be identified and evaluated for pavement sections using visual inspection and simple linear measurements. The extent of these two major defect groups is measured using the defected pavement areas (or lengths) and the defect severity is measured based on the average crack width and average deformation depth. A case study is presented to demonstrate the ‘back-calculation’ of transition probabilities. In particular, the impacts of the pavement section length on the distress rating and on the estimation of the transition probabilities have been investigated. The results have indicated that the estimated transition probabilities become highly unstable as the section length gets larger and the sample size becomes smaller.     

The application of a three-surface kinematic hardening model to repeated loading of thinly surfaced pavements

This paper examines the application of kinematic hardening to modelling the behaviour of thinly surfaced pavements dominated by the clay subgrade. An existing three-surface kinematic hardening model has been found to predict too much shear strain and therefore too much settlement under repeated loading for certain stress conditions. Under some stress conditions, the model also predicts an accumulation of negative shear strain with increasing number of cycles of load, leading to a pavement rut depth which decreases with increasing numbers of cycles. Consequently, a new model has been developed by modifying the flow rule and hardening modulus. The new model requires 10 parameters, most of which can be determined directly from simple triaxial tests. The new model is validated against drained cyclic triaxial results in order to determine model parameters, and it is shown that the new model predicts better the accumulation of shear strain and the problem of accumulation of negative shear strain is eliminated. This new model is applied to the repeated loading of a thinly surfaced pavement and is seen to predict realistic resilient and permanent deformations.This revised version was published online in September 2005 with a corrected sequence of authors.     

Network-level flexible pavement structural evaluation

The Kansas Department of Transportation has a comprehensive pavement management system known as network optimisation system (NOS). Annual condition surveys are conducted for NOS. Currently, the structural number (SN) of flexible pavements is computed using the American association of state highway and transportation officials equation based on the centre and fifth sensor deflections of a falling weight deflectometer (FWD). However, a rolling wheel deflectometer (RWD) can be used to collect deflection data at the network-level. This study was conducted to see whether the SN of flexible pavements can be obtained from this RWD deflection and NOS condition survey results. In this study, FWD deflection data, collected from 1998 to 2006, were analysed. Multiple regression analysis was done. The results showed that there is a negative relationship between SN and centre deflection. Equations can be used to calculate SN based on FWD (or RWD) centre deflections and network-level condition survey results. The SN is more sensitive to centre deflection than the total pavement thickness.     

Sensitivity of flexible pavement design to Michigan’s climatic inputs using pavement ME design

Climate condition is an important factor that affects the performance of pavements and distress predictions using mechanistic-empirical analyses. This study aims to analyse the sensitivity of flexible pavement distress predictions to climatic inputs using the AASHTOWare Pavement ME Design software in the state of Michigan. Typical traffic parameters, pavement structures and material properties for the state of Michigan were used as inputs for the analysis of flexible pavement performance. Six representative sites geographically distributed throughout Michigan and two typical traffic levels (high and medium) were incorporated in a comprehensive analysis of the effects of climate on flexible pavement performance predictions. A normalised sensitivity index was adopted to quantitatively evaluate the sensitivity of distress predictions to the five individual climatic variables: temperature, wind speed, precipitation, percent sunshine and relative humidity. The results of this study showed that the prediction of flexible pavement performance in Michigan is most sensitive to changes in temperature with other climatic factors such as wind speed, percent sunshine, precipitation and relative humidity impacting predictions to a lesser extent. Higher temperature and percent sunshine at a given location increased rutting and International Roughness Index (IRI) predictions, but reduced the likelihood of fatigue cracking. An increase in wind speed or precipitation reduced rutting and IRI predictions, but increased fatigue cracking predictions. Ambient relative humidity had a negligible effect on all flexible pavement distress predictions. These findings provide insights into the sensitivity of flexible pavement designs under different climate conditions. The sensitivity results are also beneficial for the Michigan Department of Transportation as they seek to improve the existing climatic files in PMED through evaluation of new climatic data sources.     

Mechanistic–empirical analysis of the results of finite element analysis on flexible pavement with geogrid base reinforcement

A finite-element response model was developed using ABAQUS software package to investigate the effect of geogrid base reinforcement on the response of a flexible pavement structure. Finite-element analyses were then conducted on different unreinforced and geogrid-reinforced flexible pavement sections. In this analysis, the base course (BC) layer was modelled using an elasto-plastic bounding surface model. The results of the finite-element analyses showed that the geogrid reinforcement reduced the lateral strains within the BC and subgrade layers, the vertical strain and shear strain at top of subgrade, and the surface permanent deformation. The higher tensile modulus geogrid resulted in larger reduction of surface permanent deformation. Based on the response parameters computed from the finite element analysis, the improvement of using geogrid for BC reinforcement was then evaluated using the damage models for rutting in the mechanistic–empirical method developed through NCHRP Project 1-37a. The results of mechanistic–empirical analyses showed that the traffic benefit ratio values can reach as high as 3.7 for thin base pavement section built over weak subgrade using high tensile modulus geogrid.     

Effects of mean annual temperature and mean annual precipitation on the performance of flexible pavement using ME design

The purposes of this study were to establish the difference between empirical and mechanistic–empirical approaches in the flexible pavement design and to quantify the effects of mean annual precipitation and temperature on the flexible pavement distresses using the Mechanistic-Empirical Pavement Design Guide (MEPDG) software. Seventy-six specific locations from 13 states throughout the USA were selected based on different climate conditions using virtual climate stations based on the interpolation from the nearest weather stations prior to meeting the objectives. Subsequently, analysis was conducted based on the predicted distresses, including longitudinal cracking, transverse cracking, alligator cracking, asphalt concrete rutting and total pavement permanent deformation. Generally, the pavement structure and materials have been set as constant to control the effects of material on the results. On the basis of the MEPDG analysis, the longitudinal cracking of flexible pavement is significantly affected by both factors (temperature and precipitation), particularly in wet climatic regions. The mean annual temperature has a great influence on the alligator cracking, transverse cracking and permanent deformation of flexible pavement. However, neither factors demonstrated a significant impact on the predicted International Roughness Index of flexible pavement surfaces.     

沥青结合料老化对路面功能层力学性能的影响

为考察沥青结合料老化对路面功能层力学性能的影响,运用Bisar和Ansys程序计算了不同老化程度的沥青路面的应力应变分布情况.通过计算分析可知:沥青层中产生的最大拉应力位于路表面双轮中心处;路面老化后,沥青层中产生的最大拉应力增加,最大拉应变与未老化路面相比,基本处于同一水平;在老化的前期(老化程度较轻时),沥青路面层内产生的最大剪应力远大于沥青路面层产生的最大拉应力;随着路面老化程度的加深,路面表面双轮中心处的拉应力(沥青层内产生的最大拉应力)接近甚至大于沥青面层的最大剪应力;这表明,路面在使用前期容易发生剪切破坏,随着路面老化程度的加深,路面更容易产生拉伸破坏.     

Permanent deformation characteristics of silty sand subgrades from multistage RLT tests

Rutting is one of the main forms of distresses in thin flexible pavement structures, often associated with accumulation of permanent deformation in unbound granular layers and subgrade soils under traffic loading. Realistic prediction of surface rutting requires models that can reliably capture the cumulative plastic deformation of pavement unbound layers under repeated loads. This study presents an evaluation of three models that incorporate the time-hardening concept for prediction of permanent deformation of silty sand subgrade materials. A series of multistage repeated load triaxial (RLT) tests, in which the material underwent a wide range of continuous stress conditions, were carried out on two silty sand subgrades. The RLT tests were conducted at four different moisture contents in which pore suctions were measured throughout the test. In the modelling of the permanent deformations, the effective stress approach was used taking into account the effects of soil suctions. The material parameters of the predictive models were optimised using the RLT test data and the effect of moisture content (matric suction) on the permanent deformation characteristics of the materials and the predictive model parameters were investigated. Generally, it was observed that the modified models that are based on the shakedown approach performed reasonably well in capturing the permanent deformation behaviour of the selected subgrade materials with minor discrepancies between the models. This indicates that using multistage RLT tests can be an efficient approach for characterising the permanent deformation behaviour of subgrade soils.     

Surface condition and safety at signalised intersections

The study reported herein focuses on assessing the effects and contributions of pavement surface condition parameters including roughness, rut depth and skid resistance on crash frequencies and rates at signalised intersections. The assessment has been performed using graphical trend analysis and negative binomial regression. For each signalised intersection in the sample, condition data are collected for the year before and after the year of surface treatment. Crash data, however, are collected for a minimum of 3 and maximum of 5 years before and after treatment years. Analyses results show that before treatment, light condition (day/night), road surface moisture condition (wet/dry) and skid resistance are the significant contributors to crash occurrence. For after-treatment; light condition, road surface moisture condition and the interaction between roughness and traffic volume are the significant contributors. The study aim and approach are presented herein together with a discussion of the main findings.     

Development of serviceability prediction model for county paved roads

This paper developed a pavement serviceability prediction model for county paved roads. Most county paved roads were built decades ago without following minimum design standards. The recent increase in industrial/mineral activities in the State of Wyoming required developing a pavement management system (PMS) for local paved roads. The developed PMS used the pavement serviceability index (PSI) as a pavement performance parameter. The proposed PSI model for local roads is based on: international roughness index, pavement condition index (PCI) and rut depth for flexible pavements only. Ten panellists from Wyoming rated 30 pavement sections that were randomly selected at different distresses’ levels; using two vehicles (SUV and Sedan). The statistical analysis indicated that the seating position, age and gender are not significant to the rating process. However, the vehicle’s type found to be significant. The newly developed model from this study explains 80% of the variations in the PSI values of county roads (adjusted R² = 0.80). In addition, the new model seems to provide more realistic representation of the conditions of county roads than the statewide model used on the state’s highway system.     

Impact of pavement conditions on crash severity

Pavement condition has been known as a key factor related to ride quality, but it is less clear how exactly pavement conditions are related to traffic crashes. The researchers used Geographic Information System (GIS) to link Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) data and Pavement Management Information System (PMIS) data, which provided an opportunity to examine the impact of pavement conditions on traffic crashes in depth. The study analyzed the correlation between several key pavement condition ratings or scores and crash severity based on a large number of crashes in Texas between 2008 and 2009. The results in general suggested that poor pavement condition scores and ratings were associated with proportionally more severe crashes, but very poor pavement conditions were actually associated with less severe crashes. Very good pavement conditions might induce speeding behaviors and therefore could have caused more severe crashes, especially on non-freeway arterials and during favorable driving conditions. In addition, the results showed that the effects of pavement conditions on crash severity were more evident for passenger vehicles than for commercial vehicles. These results provide insights on how pavement conditions may have contributed to crashes, which may be valuable for safety improvement during pavement design and maintenance. Readers should notice that, although the study found statistically significant effects of pavement variables on crash severity, the effects were rather minor in reality as suggested by frequency analyses.     

Approximate simulation of storm water runoff over pervious pavement

The approximate simulation of storm water run-off over pervious pavement is carried out using both experimental method and numerical simulation. The slope and flow rate changeable flume and uniform porous media are used to approximately simulate the pervious pavement. A 3D computational fluid dynamics – discrete element method is used for numerical simulation of interaction between pervious pavement and fluid. The effects of variation of parameters, including inflow rate, infiltration outflow rate and slope on surface run-off are analysed. The average flow velocity within the surface run-off region and shear velocity increases with the increasing permeability of pervious pavement. The turbulent kinetic energy distribution along depth in the free-flow region is more uniform than empirical relationship for flow over impermeable surfaces. Equations for flow depth and velocity over pervious pavement have been deduced. The results of this study are helpful for the hydraulic design of pervious concrete pavement.     

Investigation of seasonal variations of Beijing pavement condition data using unevenly spaced dynamic panel data model

Beijing municipal highway administration started to collect pavement condition data on its major expressways since 2006. It is advised in the Chinese practice standard that data collection shall be conducted on annual basis. However, pavement data are usually collected at different seasons of the year, which may cause significant seasonal variations in the observed condition. Moreover, for some reasons, data are missing for some of the pavement sections at certain years, which could bring up difficulties in performance model estimation and inference. These concerns have been simply neglected in past practice. This study proposed an unevenly spaced dynamic panel data model to investigate the seasonal patterns of a performance indicator called Ride Quality Index (RQI). A quasi-differencing approach was adopted for the estimation. Data collected from the 5th Ring Road of Beijing were used in the case study. It was found that RQI data collected during the fall season are expected to be lower than that collected during the spring or summer seasons. Findings from this research would be helpful to pavement engineers in using unevenly spaced pavement condition data for future condition estimation.     

An ultrawide-band microwave radar sensor for nondestructive evaluation of pavement subsurface

A new ultrawide-band (UWB) microwave radar sensor operating from 0.6 to 5.6 GHz has been developed using microwave integrated circuits for pavement subsurface characterization. UWB antennas operating from 0.5-10-GHz have been designed and tested for use in the sensor. A new simple, yet effective, accurate procedure was also developed to compensate for the common amplitude deviations and nonlinear phase errors produced by the inherent imperfection of the system. The developed compensation method is applicable to other systems and effectively reduces the potential masking of adjacent targets as well as facilitating and increasing the accuracy for target identification of the sensor. The sensor has been used to assess a pavement sample with less than 0.1 in of error in the pavement's layer thickness. The developed system represents the first UWB stepped-frequency radar sensor completely realized using microwave integrated circuits over the frequency range of 0.6-5.6 GHz for subsurface sensing applications.     

One-dimensional temperature profile prediction in multi-layered rigid pavement systems using a separation of variables method

This paper presents an analytical solution for prediction of the one-dimensional (1D) time-dependent temperature profile in a multi-layered rigid pavement system. Temperature at any depth in a rigid pavement system can be estimated by using the proposed solution with limited input data, such as pavement layer thicknesses, material thermal properties, measured air temperatures and solar radiation intensities. This temperature prediction problem is modelled as a boundary value problem governed by the classic heat conduction equations, and the air temperatures and solar radiation intensities are considered in the surface boundary condition. Interpolatory trigonometric polynomials, based on the discrete least squares approximation method, are used to fit the measured air temperatures and solar radiation intensities during the time period of interest. The solution technique employs the complex variable approach along with the separation of variables method. A FORTRAN program was coded to implement the proposed 1D analytical solution. Field model validation demonstrates that the proposed solution generates reasonable temperature profile in the concrete slab for a four-layered rigid pavement system during two different time periods of the year.     

Simulation of unbound material resilient modulus effects on mechanistic-empirical pavement designs

The variability of resilient modulus (M _R) of unbound materials and subgrade due to laboratory test conditions affect pavement performance and designs. The performance-based mechanistic-empirical pavement design guide (MEPDG) is gaining more popularity in recent years for pavement design use. However, limited research efforts have quantitatively studied M _R effects based on ME models. This research targets to evaluate the influences of M _R variability on pavement performance and designs based on the MEPDG performance models. With a normal-distribution of M _R seed values, pavement responses were computed with a layer-elastic analysis model, pavement performance was then predicted using MEPDG models, and design variability was studied via Monte Carlo simulation. Results indicate that the relationship between layer design thickness and M _R varies from almost linear to nonlinear, which is highly dependent on the pavement structure and material properties. For the evaluated specific pavement structure and range of M _R values, the least susceptible is the HMA design thickness as a function of M _R under fatigue with a design Coefficient of Variance (CV) of 7.51 %, while the most susceptible is the base design thickness as a function of M _R also under fatigue with a CV of 54.32 %. The combined effect of both rut depth and fatigue life considering the variability of both base and subgrade results in a design CV of 22.58 % for asphalt layer and 26.08 % for base layer. When using a weaker base layer or a thinner HMA layer, the modeled thickness design CV has changed ?4.19 to 1.14 %.     

Evaluating pavement management treatment selection utilising continuous deflection measurements in flexible pavements

The objective of this study was to develop a new structural index based on rolling wheel deflectometer (RWD) deflection data to describe pavement structural capacity and to improve pavement management treatment selection. To achieve this objective, pavement conditions including surface cracking, rutting, roughness and asphalt layer thicknesses were categorised and sorted according to their AC layer thickness and divided into various subgroups. The cumulative distribution function of the new RWD index in each subgroup was generated so that various percentiles were calculated and used to define the boundary between structural and functional rehabilitation. Results showed that the Louisiana criteria may recommend structural rehabilitation for pavements with sound pavement structure (Type I error) and functional rehabilitation to pavements with weak pavement structure (Type II error). Therefore, the state pavement management system should consider both pavement structural indices and surface distress indices currently available when making recommendations for pavement preservation and rehabilitation. RWD testing technology and indices derived from its data are one of the most promising candidates to fulfil this need.     

动态傅里叶变换轮廓术实现路面形变快速检测

公路表面的三维结构对于判断公路路面破损状况具有重要的意义。基于光栅投影的傅里叶变换轮廓术将物体的高度信息编码在投影到待测物面上的正弦条纹的形变信息中,可以实现对物体的垂直测量。探讨了傅里叶变换轮廓术在高速公路路面面形测量-动态实时系统中的应用,提出了利用自适应标准模版匹配,基于特征图像识别的方法,分选破损的公路路面,从而实现对高速公路路面形变的快速实时检测。