Long-Term Pavement Performance Data Research Articles

Detecting Flushing of Thin-Sprayed Seal Pavements Using Pavement Management Data

AbstractFlushing is a pavement surface defect that affects the structural integrity of thin-sprayed seal (chip seal) surfacings. Analysis was carried out on pavement performance data to determine the combination of factors that provides the best indication of flushing occurrence on in-service pavements. Data were sourced from the Long-Term Pavement Performance (LTPP) programme in New Zealand as well as from field testing. The LTPP data were analysed in four categories; pavement composition characteristics: traffic-related factors, climatic factors, and effects of other pavement defects. Factor and correlation analyses were used to determine and investigate the factors that showed significant relationships to flushing. Surface thickness, surface age, surfacing type, and rutting were found to have statistically significant correlations to flushing. Field testing provided data relating to the soil moisture environment under the pavement. The dry density and water content of soil were identified as having sig...

노후화된 시멘트 콘크리트 포장에 대한 콘크리트 및 아스팔트 덧씌우기의 포장성능 비교

More than sixty percentage of highway in South Korea were constructed by concrete pavements and more than half of the concrete pavements were twenty years or older. Maintenance and rehabilitation of aged cement concrete pavements required early transportation opening due to difficulty of preparing bypass roads, given South Korea`s transportation condition and so far, mostly asphalt concrete overlay has been used. However, asphalt concrete pavement maintenance and rehabilitation is costly because of early damage and at the same time, it causes inconvenience to the road users. Recently, as an effective method of rehabilitation for aged cement concrete pavement, bonded concrete overlay is being attempted. Therefore, utilizing various data on year-by-year basis is needed to rationally analyze of the damage on asphalt concrete overlay and bonded concrete overlay is necessary. However, in South Korea database of Serviceability damage on asphalt concrete overlay and bonded concrete overlay does not exist. In this research, performance is evaluated by the LTPP (Long Term Pavement Performance) Data of U.S.A, which accumulated various damage data of asphalt concrete overlay and bonded concrete overlay. However, the pattern distress of asphalt concrete overlay and bonded concrete overlay are different. Therefore, the pavement distress data of each section is collected into database and distress are calculated PCI(Pavement Condition Index) in order to compare life of asphalt concrete overlay and bonded concrete overlay.

Resilient Modulus Characterization of Alaska Granular Base Materials

The resilient modulus (MR) of base course material is an important material input for pavement design. Although extensive research efforts have been directed toward MRcharacterization and modeling, less effort has been spent on factors important for cold-region pavements. Most MRvalues of granular base materials available in the database of the Alaska flexible pavement design software were obtained from in-service roadways through nondestructive testing and backcalculation. The predictive models provided in the current Mechanistic–Empirical Pavement Design Guide cannot be confidently applied to Alaska materials because of the peculiar conditions for material properties and climatic differences. Hence, a systematic study was carried out to investigate the resilient behavior of granular base materials obtained from all three regions in Alaska, as presented in this paper. Effects of temperature, freeze–thaw cycle, and moisture and fines contents on the MRof Alaska base materials were experimentally evaluated, from which equations for MRprediction were developed and compared with those based on the Long-Term Pavement Performance data.

Calibration of the Mechanistic–Empirical Pavement Design Guidefor 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.

New Laboratory-Based Mechanistic–Empirical Model for Faulting in Jointed Concrete Pavement

Faulting is associated with many design- and performance-related factors of concrete pavement, such as traffic load, joint spacing, load transfer, drainage, subbase friction, and subbase erodibility. Subbase erosion is especially key to an understanding of the process of faulting distresses in jointed concrete pavement. However, subbase erosion has not been included explicitly in design-related analysis. As a consequence, design procedures have lacked a prerequisite sensitivity because of a lack of laboratory test procedures and material parameters to relate model prediction to subbase erosion performance. This paper presents a mechanistic–empirical faulting model. It is calibrated from the results of a new erosion test that involves the Hamburg wheel-tracking device and Long-Term Pavement Performance (LTPP) data. The calculated faulting depths from the proposed model match well with the trends of observed LTPP field faulting data.

Optimum Time for Application of Slurry Seal to Asphalt Concrete Pavements

This study evaluated the field performance of asphalt pavements with and without slurry seal applications, developed performance models for asphalt pavements without slurry seals and asphalt pavements receiving slurry seals at various times following construction, and identified the optimum time for applying slurry seals on asphalt pavements in the Washoe County, Nevada, region. This determination was achieved by evaluating the long-term pavement performance data collected with the MicroPAVER system for the past 15 years and the cost-effectiveness of slurry seals applied to new and existing flexible pavements at Years 0, 1, 3, 5, 7, and 9 after construction. This study found that applying slurry seal immediately after or 1 year after construction of the asphalt layer is not effective in regard to the benefit to users and the benefit–cost ratio for the agency. The optimum time of applying slurry seal depended on the type of construction activity. For newly constructed pavements, the optimum time to apply slurry seal was 3 years after construction. For pavements subjected to overlays, the optimum time to apply slurry seal was between 3 and 5 years after construction. However, for uniformity purposes, it was recommended that the agency apply slurry seal 3 years after construction of the asphalt layer for both new and overlay constructions.

Comparative Field Performance of Asphalts from Multiple Crude Oil Sources

A comparative pavement performance site was built in 2001 in central Arizona with four unmodified PG 76-16 asphalts. The four asphalts from different crude oil sources and blends were used to build sections of pavement about three-quarters of a mile long. All other variables (e.g., aggregate source and gradation, geometric and structural design, traffic, climate, and compaction) were held as constant as possible. In a typical project, a construction contractor could have purchased any of the four asphalts because all met the project specifications. Before construction of this site, a search was conducted for existing projects for which the asphalt source was varied and location documentation and original construction materials were available. No such projects were found. The mission was to acquire real-world, long-term pavement performance data for asphalts from different crude oil sources so that the validity of existing and new long-term performance predictive test methods could be examined. The focus was on performance variations related to asphalt crude oil source and thus asphalt compositional features. Several chemical and rheological tests were performed on the asphalts to gain information to relate to field performance. The four asphalt sources showed remarkable differences in performance. Similar comparative pavement performance sites were located in Wyoming, Nevada, Arizona, Kansas, Minnesota, and the Yellowstone National Park east entrance. All sites are monitored annually.

Mechanistic-Empirical IRI Model Accounting for Potential Bias

The roughness prediction models in the International Roughness Index (IRI) currently incorporated into the Mechanistic-Empirical Pavement Design Guide (M-E PDG) have been developed by means of ordinary least squares (OLS). However, some of the variables used in predicting future IRI were previously estimated by means of separate performance models. This could cause bias because of the correlation between the previously estimated distress types and unobserved components on the IRI model. The bias can be corrected by considering additional variables that are correlated with the distress types causing the bias, thus eliminating the correlation to the unobserved terms in the model. Bias in the IRI model can also be generated by unobserved factors not included in the model. If these factors are section-specific, the bias can be removed considering variations in the performance history of different pavement sections. The writers have used updated Long-Term Pavement Performance (LTPP) data consistent with the data set originally used to fit the M-E IRI model for flexible pavements over thick granular bases contained in the M-E PDG. The data were then used in modeling IRI by means of OLS and instrumental variable (IV) regressions analyzing the data as pooled and as a panel data set (by random-effects, fixed-effects, and joint random-effects approach) to check for possible bias in the model. It was found that the current IRI model, as estimated by OLS, exhibits several types of biases attributed to heterogeneity and incorrect assumptions in the modeling process. It was identified that the preferred IRI model was the joint random-effects approach and, therefore, the model parameters were estimated by correcting the omitted-variable bias and simultaneous-equation bias. Estimating the model by accounting for possible bias in the data indicated considerable changes in the effects of different parameters affecting IRI through time, mainly rutting of the pavement structure.

Toward Monte Carlo Simulation-Based Mechanistic-Empirical Prediction of Asphalt Pavement Performance

The mechanistic-empirical study of pavement performance requires that immediate pavement responses due to tire loading be mechanistically computed for pavement structures, and the long-term pavement performance be related to the computed pavement responses. The problem becomes very complicated when variability is considered for loading, pavement and environmental conditions. A Monte Carlo simulation-based mechanistic-empirical pavement design/analysis procedure was verified in this study. The complex tire-pavement interaction was more realistically handled and computed using finite element models and measured tire-pavement contact stress data. The computation time problem involved in pavement response computations was resolved by using a computationally efficient method that relates critical pavement responses to tire loading and pavement structural conditions. In the Monte Carlo simulation, different truck classes were drawn from the truck population empirically based on actual traffic volume data. Axle load spectra were characterized by actual axle load data collected at a weigh-in-motion site. Results from a survey of truck configurations were used to describe the distribution of truck tire pressure. Pavement structural parameters and relationships between material moduli and environmental conditions were obtained from the Long-Term Pavement Performance data. The distress models developed in National Cooperative Highway Research Program Project 1–37A were employed to predict pavement performance. The simulation study estimates the effects of increased tire pressure and steering axle load on a typical pavement structure for 2 million truck passes. The Monte Carlo simulation method and models used in this study can provide useful experience for the development of future flexible pavement design and analysis procedures.

Necessary Assessment of Use of State Pavement Management System Data in Mechanistic–Empirical Pavement Design Guide Calibration Process

Calibration and validation of the Mechanistic–Empirical Pavement Design Guide (MEPDG) for state department of transportation (DOT) networks requires detailed information for a variety of pavement inputs. The biggest database resource that most states possess is their pavement management system (PMS). It is understandable for any DOT to maximize the use of this resource to the greatest extent possible. However, before any state uses PMS data in MEPDG calibration, it is important that a careful comparison be made of the equality of the measurements, data, and so forth in the PMS database with national Long-Term Pavement Performance (LTPP) values. The purpose of this paper is to review possible differences between several key data found in a state PMS database and the LTPP database. Arizona Department of Transportation (ADOT) PMS data are used for this purpose. The specific variables examined in this paper deal with distress type (rutting, cracking, and roughness) and nondestructive deflection testing (NDT) backcalculated moduli to predict in situ pavement layer properties. It was concluded that significant differences did exist between the ADOT PMS values and LTPP measurements. Differences were found between ADOT's NDT measurements and LTPP data in rut measurements, asphalt cracking, international roughness index, and all layer backcalculated moduli. Consequently, ADOT may incur significant expense to calibrate the MEPDG to Arizona conditions. The paper discusses several possible reasons for differences between the PMS data of a specific agency and those in the LTPP database.

Pavement Smoothness Prediction Based on Fuzzy and Gray Theories

Abstract: Pavement smoothness has been recognized as one of the measures of pavement performance. In the Mechanistic-Empirical Pavement Design Guide (MEPDG), pavement smoothness indicated by the International Roughness Index (IRI) was predicted based on various distresses using traditional regression analysis approaches. Recognizing the limitations of linear regression method, a Gray Theory-based technique was previously proposed by the authors for the development of pavement smoothness prediction models. In this article, instead of using the conventional least squares method to determine the coefficients for gray prediction models, fuzzy regression method is proposed to solve this gray problem. With pavement IRI and distresses data exported from the Long Term Pavement Performance (LTPP) database, Fuzzy and Gray Model (FGM)-based smoothness predictions are established using influencing factors similar to those in MEPDG. Based on the comparisons among results originated from MEPDG models, conventional GM models, FGM models, and actual LTPP data, it is shown that the Gray Theory-based prediction methods with fuzzy regression for estimating model coefficients provide promising results and are useful for modeling pavement performance.

Stochastic Modeling for Pavement Warranty Cost Estimation

This paper presents a cost estimation model for long-term pavement warranties with multiple distress indicators. One application area for such warranties involves performance-based specifications (PBSs). In contrast to traditional approaches, PBS gives contractors the flexibility to select construction methods, materials, and even design. However, the contractors then must warrant the performance of their work for a specified period of time. Therefore, an accurate estimation of the risks associated with the warranty is a significant cost issue for any contractor to cover potential risks while still being competitive in bidding. Quantitative evaluation of the cost of risk incurred by the warranty has several difficulties. The deterioration of a highway project is a complex process, which is affected by pavement structure, material, traffic load, and weather conditions. Based on a probabilistic risk analysis of failures of performance indicators, the resulting model can estimate the warranty cost at a detailed level. The application of the model has been demonstrated via a numerical case study using long-term pavement performance data.

Subgrade resilient modulus prediction models for coarse and fine-grained soils based on long-term pavement performance data

Resilient modulus (M R) of subgrade soils is the elastic modulus based on the recoverable strain under repeated loads and depends on several factors such as soil properties, soil type and stress states. This paper presents the prediction equations to estimate M R from a set of soil physical properties for the unified soil classification system soil types namely coarse-grained and fine-grained subgrade soils. Data extracted from long-term pavement performance information management system database for 259 test specimens of reconstituted soil samples from 19 states in New England and nearby regions in the USA and two provinces in Canada were used in this study. Generalised constitutive model consisting bulk stress and octahedral shear stress was used to predict the M R of subgrade soils by developing equations for the regression coefficients (k-coefficients) in the constitutive model that relates them to various soil properties. Prediction models were developed by conducting multiple linear regression analysis using computer software SAS®. To verify the prediction models, a set of fresh laboratory M R tests were conducted on representative New England subgrade soils using AASHTO standards. The laboratory test results show that the developed models predict M R values fairly well for the soils with their properties values within the range used in developing the prediction models.

Robust Optimization for Managing Pavement Maintenance and Rehabilitation

A pavement management system should help a decision maker to select the best preservation program, decide which preservation treatment to use, and where and when to apply it to maximize the use of the available resources. One of the essential roles of pavement management is to provide a rational, cost-effective optimal funding planning and allocation strategy for highway agencies. Researchers have previously developed deterministic optimization methods for programming pavement maintenance and rehabilitation strategies. However, pavement infrastructure deterioration is a dynamic, complicated, and stochastic process affected by a variety of factors such as traffic loading, environmental conditions, and structural capacities, as well as certain unobserved factors. Ignoring these fundamental characteristics may limit the usefulness of an optimal solution. To take the uncertainties into consideration, some researchers have introduced stochastic programming techniques into pavement maintenance management. However, difficulties in characterizing the distribution of data and the substantial computational challenge have compromised the practical application of those techniques. A project-level robust optimization method for maintenance budget planning to overcome these difficulties is presented. The solutions from this proposed method are computationally tractable and not overly sensitive to any specific realization of the uncertainties. An application of this method is demonstrated by using long-term pavement performance data collected during the past 20 years, yielding promising preliminary results.

Experimentation with Gray Theory for Pavement Smoothness Prediction

Riding quality is a dominant characteristic of pavement performance. In the proposed mechanistic–empirical pavement design guide (MEPDG), the functional performance indicator is pavement smoothness as indicated by the international roughness index (IRI). The MEPDG IRI prediction models were developed on the basis of a general hypothesis in which changes in smoothness are caused in part by various distresses, which can be predicted. With pavement distress data from the Long-Term Pavement Performance (LTPP) database, traditional regression analysis was used statistically to establish the MEPDG prediction equations. The gray system theory was devised in the 1980s for modeling uncertain systems with the characteristics of partially known information. A pavement performance prediction system can fit the domain of the gray system. The gray theory–based prediction method was used to develop IRI prediction equations. With the data exported from the LTPP database, it was found that certain specific distresses significantly affect the accuracy of the predictions. Combined with the results of gray relational analysis and the gray prediction methodology, gray model–based smoothness predictions are established by using influencing factors similar to those used in MEPDG. From comparisons of results from the two prediction methodologies with actual LTPP data, it is shown that the gray model–based method provides promising results and is useful for modeling pavement performance.

Development of Pavement Performance Models to Account for Frost Effects and Their Application to Mechanistic–Empirical Design Guide Calibration

Although it has long been recognized that the presence of frost is a factor in the performance of pavements, little national research has been conducted to study the long-term performance differences caused by frost exposure. To address this concern, a pooled fund study, Effect of Multiple Freeze Cycles and Deep Frost Penetration on Pavement Performance and Cost, was conducted under the direction of the FHWA Long-Term Pavement Performance data analysis program. This study not only explored the effect of frost on pavements but also differentiated between two types of frost exposure: deep frost penetration, remaining present throughout the winter months, and freeze–thaw cycling, occurring multiple times during the frost season. Performance models were developed to evaluate the independent effects of these two freezing conditions on long-term pavement performance. These models were used to make comparisons of predicted fatigue, rutting, and roughness measures in different environmental settings. Predictions revealed that significant differences existed among the various climatic scenarios. An evaluation also was conducted on the application of the models for regional calibration of the NCHRP 1-37A design procedure for locations where measured performance data are not available. An example shows that the models can be used as an effective calibration tool.

Concrete Pavement Performance in Midwestern Argentina Compared with Long-Term Pavement Performance Data

Portland cement concrete pavements have been constructed in many areas in Argentina that feature heavy traffic and freeways and highways in freezing areas. To predict their performance, computational programs such as the World Bank's HDM-4 may be used. This particular software includes distress models that need to be adjusted to local conditions in order to optimize the evaluation of maintenance strategies and resource allocation. The main objective of this study is to evaluate the performance of concrete pavements in the midwestern region of Argentina by using the HDM-4 software to obtain calibration coefficients for each distress model. A specific methodology for model calibration was applied to concrete pavements of the national road network located in the provinces of San Juan, Mendoza, and Córdoba. Such roads include jointed plain and jointed reinforced concrete pavement (JPCP and JRCP), with an ample range of traffic levels and climate conditions. Calibration coefficients were determined individually for each road and for road groups with similar characteristics; the results were subsequently compared with data calibration results from the Long-Term Pavement Performance database obtained from previous studies. Many similarities were found in the ability of the models to predict distress levels. Some models show an irregular pattern in predicting distress without calibration, but other models show better results, such as those to predict joint faulting and international roughness index.

Network-Level Evaluation of Specific Pavement Study–2 Experiment

The research described here was conducted as a part of NCHRP Project 20–50 (10&16), LTPP (Long-Term Pavement Performance) Data Analysis: Influence of Design and Construction Features on the Response and Performance of New Flexible and Rigid Pavements. The relative effects of various design and site factors on the performance of jointed plain concrete (JPC) pavements are presented. The data used in this study were primarily drawn from Release 17 of DataPave. The Specific Pavement Study (SPS) 2 experiment was designed to investigate the effects of portland cement concrete (PCC) slab thickness, base type, drainage, PCC flexural strength, and slab width on the performance of JPC pavements. On the basis of the statistical analysis of 167 test sections, ranging in age from 5 to 12 years, it was concluded that base type was the most critical design factor affecting performance in terms of cracking and roughness as measured by the international roughness index. Pavement sections with a permeable asphalt-treated base and in-pavement drainage performed better than those with a dense-graded aggregate base or a lean concrete base. PCC slab thickness also played an important role in improving the cracking performance of the pavements. PCC flexural strength and slab width have only marginal effects on performance at this time.

Part 1: Rigid Pavements: Network-Level Evaluation of Specific Pavement Study-2 Experiment: Using a Long-Term Pavement Performance Database

The research described here was conducted as a part of NCHRP Project 20-50 (10&16), LTPP (Long-Term Pavement Performance) Data Analysis: Influence of Design and Construction Features on the Response and Performance of New Flexible and Rigid Pavements. The relative effects of various design and site factors on the performance of jointed plain concrete (JPC) pavements are presented. The data used in this study were primarily drawn from Release 17 of DataPave. The Specific Pavement Study (SPS) 2 experiment was designed to investigate the effects of portland cement concrete (PCC) slab thickness, base type, drainage, PCC flexural strength, and slab width on the performance of JPC pavements. On the basis of the statistical analysis of 167 test sections, ranging in age from 5 to 12 years, it was concluded that base type was the most critical design factor affecting performance in terms of cracking and roughness as measured by the international roughness index. Pavement sections with a permeable asphalt-treated b...

Deterioration of Asphalt Pavement at Long-Term Pavement Performance Program Site 49-1001 in Utah

Highway 191 near Bluff, Utah, features a well-monitored section of the long-term pavement performance (LTPP) program. Constructed in 1980, this section of flexible pavement performed well for nearly 13 years. Through this time, cracking of the asphalt layer was minimal. In the fourteenth year, however, the extent of longitudinal cracking in the wheel path increased and necessitated placement of a chip seal on the pavement surface. The purpose of this research was to determine the cause of pavement deterioration using LTPP data. Deflection basins obtained from falling-weight deflectometer testing were analyzed to investigate the extent to which structural degradation influenced deterioration of the pavement. Pavement layer modulus values were plotted against time and clearly show that weakening of the pavement base layer immediately preceded the occurrence of cracking. The geography of the site, as documented in photographs, supports the conclusion that inadequate water drainage at the site permitted saturation of the aggregate base layer during a period of midsummer flooding. This finding emphasizes the importance of specifying non-moisture-susceptible base materials and providing necessary drainage works in pavement design.