Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

Implementation Initiatives of the Mechanistic–Empirical Pavement Design Guides in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Tommy Nantung ◽  
Ghassan Chehab ◽  
Scott Newbolds ◽  
Khaled Galal ◽  
Shuo Li ◽  
...  
Keyword(s):  
Pavement Design ◽  
Sensitivity Analyses ◽  
Pavement Performance ◽  
Design Parameters ◽  
State Highway ◽  
Design Guide ◽  
Input Parameters ◽  
Pavement Structures ◽  
State Highway Agencies ◽  
Empirical Design

The release of the Mechanistic–Empirical Design Guide for New and Rehabilitated Pavement Structures (M-E design guide) generated a new paradigm for designing and analyzing pavement structures. It is expected to replace the commonly used empirical design methodologies. The M-E design guide uses a comprehensive suite of input parameters deemed necessary to design pavements with high reliability and to predict pavement performance and distresses realistically. However, the considerable amount of input needed and the selection of the corresponding reliability level for each might present state highway agencies with complexities and challenges in its implementation. An overview is presented of ongoing investigative studies, sensitivity analyses, and preimplementation initiatives conducted by the Indiana Department of Transportation (INDOT) in an effort to accelerate the adoption of the new pavement design guide by efficiently using existing design parameters and determining those parameters that influence the predicted performance the most. Once the sensitive inputs are identified, the large amount of other required design input parameters can be significantly reduced to a manageable level for implementation purposes. A matrix of trial runs conducted with the M-E design guide software suggests that a higher design level input does not necessarily guarantee a higher accuracy in predicting pavement performance. The software runs also confirmed the need to use input values obtained from local rather than national calibration. Such findings are important for state highway agencies such as INDOT in drafting initiatives for implementing the M-E design guide.

Mechanistic-Empirical Flexible Pavement Design: An Overview

1996 ◽  
Vol 1539 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Marshall R. Thompson
Keyword(s):  
Task Force ◽  
New Technology ◽  
Flexible Pavement ◽  
Pavement Design ◽  
Analysis And Design ◽  
Design Procedures ◽  
State Highway ◽  
Pavement Structures ◽  
Available Technology ◽  
State Highway Agencies

Activities associated with the development of the revised AASHTO Guide for the Design of Pavement Structures (1986 edition) prompted the AASHTO Joint Task Force on Pavements (JTFOP) recommendation to immediately initiate research with the objective of developing mechanistic pavement analysis and design procedures suitable for use in future versions of the AASHTO guide. The mechanistic-empirical (M-E) principles and concepts stated in the AASHTO guide were included in the NCHRP 1-26 (Calibrated Mechanistic Structural Analysis Procedures for Pavements) project statement. It was not the purpose of NCHRP Project 1-26 to devote significant effort to develop new technology but to assess, evaluate, and apply available M-E technology. Thus, the proposed processes and procedures were based on the best demonstrated available technology. NCHRP Project 1-26 has been completed and the comprehensive reports are available. M-E flexible pavement design is a reality. Some state highway agencies (Kentucky and Illinois) have already established M-E design procedures for new pavements. M-E flexible pavement design procedures have also been developed by industry groups (Shell, Asphalt Institute, and Mobil). The AASHTO JTFOP continues to support and promote the development of M-E procedures for pavement thickness design and is facilitating movement toward an M-E procedure. The successful and wide-scale implementation of M-E pavement design procedures will require cooperating and interacting with various agencies and groups (state highway agencies, AASHTO—particularly the AASHTO JTFOP, FHWA—particularly the Pavement Division and Office of Engineering, and many material and paving association industry groups). It is not an easy process, but it is an achievable goal.

Evaluation of Clustered Traffic Inputs for Mechanistic-Empirical Pavement Design: Case Study in New Jersey

2019 ◽  
Vol 2673 (11) ◽  
pp. 332-348
Author(s):  
Abbas F. Jasim ◽  
Hao Wang ◽  
Thomas Bennert
Keyword(s):  
Cluster Analysis ◽  
New Jersey ◽  
Hierarchical Cluster ◽  
Pavement Design ◽  
Traffic Data ◽  
Truck Traffic ◽  
Vehicle Class ◽  
Site Specific ◽  
Load Spectra ◽  
Pavement Structures

Truck traffic is one of the significant inputs in design and analysis of pavement structures. This paper focuses on comprehensive cluster analysis of truck traffic in New Jersey for implementation of mechanistic-empirical pavement design. Multiple year traffic data were collected from a large number of weigh-in-motion stations across New Jersey. Statistical analysis was first conducted to analyze directional and temporal (yearly) variations of traffic data. Hierarchical cluster analysis was conducted and three optimum clusters were found for axle load spectra (single, tandem, tridem), vehicle class distribution, and axle/truck ratio, respectively. Road functional classifications were employed to identify different clusters as no common geographic trend could be perceived. The results illustrate that the predicted performance using the site-specific traffic data is comparable with that using the traffic cluster for the selected 10 sites. Among four different traffic inputs, the cluster traffic inputs generated the closest predictions of pavement life as compared with those using site-specific traffic input and the default traffic inputs yielded the highest error. It is recommended to use traffic clusters in mechanistic-empirical pavement design when site-specific data is unavailable.

Seed Modulus Generation Algorithm for Backcalculation of Flexible Pavement Moduli

2005 ◽  
Vol 1905 (1) ◽  
pp. 117-127 ◽  
Author(s):  
T. F. Fwa ◽  
Thakur Swapna Rani
Keyword(s):  
Closed Form ◽  
Flexible Pavements ◽  
Flexible Pavement ◽  
Pavement Performance ◽  
Program Performance ◽  
Generation Algorithm ◽  
User Input ◽  
Pavement Structures ◽  
Performance Project

The seed moduli chosen for backcalculation analysis of multilayer flexible pavements can have significant impacts on the performance of backcalculation software and, sometimes, the final solutions of the backcalculated moduli. Practically all backcalculation programs provide internally generated seed moduli for backcalculation analysis. However, as the internally generated seed moduli do not always produce satisfactory results, the use of user-input seed moduli is generally encouraged. With the aim of providing useful guidance in the choice of seed moduli, a seed modulus generation algorithm, 2L-BACK, for multilayer flexible pavements based on a closed-form modulus backcalculation solution for two-layer flexible pavement structures was developed. The proposed algorithm does not require any subjective judgment by the user. An evaluation analysis of the effectiveness of the proposed procedure is presented by the use of two types of backcalculation software, MICHBACK and EVERCALC, and is based on measured and computed data for flexible pavement segments from the Long-Term Pavement Performance project. A comparison was made of the backcalculation program performance and the computed moduli of solutions obtained from internally generated seed moduli and those obtained from seed moduli generated by the proposed algorithm. It was found that the proposed seed modulus generation algorithm led to enhanced program performance of MICHBACK with respect to convergence characteristics and the accuracies of the backcalculated solutions. In comparison, the corresponding improvements for the case of EVERCALC were less. The proposed seed modulus generation algorithm does not suffer from the location and pavement type transferability constraints of most regression-based seed modulus generation methods. The results of the study suggest that the algorithm can be effectively incorporated into backcalculation software for multilayer flexible pavements.

Use of Long Term Pavement Performance-Seasonal Monitoring Program Data to Develop and Validate a Generalized Regression Model to Predict the In-Situ Resilient Modulus of Subgrade Soils for Pavement Design and Evaluation

2020 ◽  
Vol 2674 (5) ◽  
pp. 673-684
Author(s):  
Mohamed Elshaer ◽  
Christopher DeCarlo ◽  
Wade Lein ◽  
Harshdutta Pandya ◽  
Ayman Ali ◽  
...  
Keyword(s):  
Prediction Model ◽  
Resilient Modulus ◽  
Monitoring Program ◽  
Pavement Design ◽  
Pavement Performance ◽  
Regression Approach ◽  
Seasonal Monitoring ◽  
Generalized Regression

Resilient modulus (Mr) is a critical input for pavement design as it is the main property used to evaluate the contribution of subgrade to the overall pavement structure. Considering this, practitioners need simple and accurate ways to determine the Mr of in-situ subgrade without the need for expensive and time-consuming testing. The objective of this study is to develop a generalized regression prediction model for in-situ Mr of subgrades, compare it with established prediction models, and assess the model’s predictions on pavement performance using the Mechanistic-Empirical Pavement Design Guide (Pavement ME). The prediction model was built using field data from 30 pavement sections studied in the Long Term Pavement Performance (LTPP) Seasonal Monitoring Program where backcalculated modulus from falling weight deflectometer testing, in-situ moisture contents, and subgrade material properties were considered in the model. Based on the results, it was found that liquid limit, plasticity index, WPI (the product of percent passing #200 and plasticity index), percent coarse sand, percent fine sand, percent silt, percent clay, moisture content, and their respective interactions were significant predictors of in-situ Mr values. The findings showed that the generalized regression approach was able to predict Mr more accurately than predictions from the Witczak model. To assess the application of the predictive model on pavement performance, three LTPP sections located in New York, South Dakota, and Texas were analyzed to predict the rutting performance based on Mr values obtained from the developed generalized prediction model and those obtained from the current Pavement ME model and then compared with rut depths measured in the field. The findings showed that, for coarse-grained subgrades that have a low degree of plasticity, the generalized regression model predicted rutting performance similar to the embedded Pavement ME model. For fine-grained subgrades, the developed model tends to predict lower rut depths which were closer to the field measured rut depths. Overall, the generalized regression approach was successfully applied to create a simple, practical, cost-effective and accurate Mr prediction model that can be used to estimate the stiffness of subgrades when designing and evaluating pavements.

Length-Based Vehicle Classification Schemes and Length Bin Boundaries

2013 ◽  
Vol 2339 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Herbert Weinblatt ◽  
Erik Minge ◽  
Scott Petersen
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
The United States ◽  
Traffic Monitoring ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Department Of Transportation ◽  
Classification Schemes ◽  
Weigh In Motion

Vehicle classification data are an important component of traffic-monitoring programs. Although most vehicle classification conducted in the United States is axle based, some applications could be supplemented or replaced by length-based data. The typically higher deployment cost and reliability issues associated with collecting axle-based data as compared with length-based data present a challenge. This paper reports on analyses of alternative length-based vehicle classification schemes and appropriate length bin boundaries. The primary analyses use data from a set of 13 Long-Term Pavement Performance weigh-in-motion sites, all in rural areas; additional analyses are conducted with data from 11 Michigan Department of Transportation weigh-in-motion sites located in rural and small urban areas and one site located in an urbanized area. For most states, the recommended length-based vehicle classification scheme is a four-bin scheme (motorcycles, short, medium, and long) with an optional very long bin recommended for use by states in which significant numbers of longer combination vehicles operate.

The Clay Subgrade Compactness Infuences on Life-Span of Asphalt Pavement in Northeast China

2011 ◽  
Vol 90-93 ◽  
pp. 1772-1775
Author(s):  
Zhong Gen Liu ◽  
Xiao Hong Liu ◽  
Zhong Sen Yang ◽  
Xiao Feng Liang
Keyword(s):  
Quantitative Analysis ◽  
Life Span ◽  
Modulus Of Elasticity ◽  
Northeast China ◽  
Asphalt Pavement ◽  
Pavement Design ◽  
Traffic Data ◽  
Design Specification ◽  
Pavement Structures ◽  
Subgrade Strength

The life-span of asphalt pavement is influenced by the strength of subgrade , this paper has discussed the modulus of elasticity of subgrade under different compactness . a example of clay subgrade is gived, the pavement structures and traffic data highway are established according to currently asphalt pavement design specification, the pavement bearing abilitie of different subgrade strength is calculated under different compacting conditions, and also a conclusion has been made that a relation between life-span and strength , the calculating results show that the compactness of subgrade cutting down 1% will lead to pavement reducing 0.65 years, through quantitative analysis, it can be believed that improving compaction of subgrade can obviously prolong asphalt pavement life-span.

scholarly journals Mechanistic-empirical approach to evaluate new and rehabilitated flexible pavements

2021 ◽  
Author(s):  
Wais Mehdawi
Keyword(s):  
Flexible Pavements ◽  
Empirical Method ◽  
Pavement Design ◽  
Empirical Approach ◽  
Integrated Analysis ◽  
Highway Traffic ◽  
Design Guide ◽  
The Difference ◽  
Pavement Structures ◽  
Empirical Design

The Mechanistic-Empirical Design provides more insight into pavement behaviour and performance than the 1993 AASHTO empirical method. The new Mechanistic-Empirical Pavement Design Guide (MEPDG) developed under the National Corporation Highway Research Program (NCHRP) 1-37A. A hierarchical approach is employed upon traffic, climate and materials input to produce pavement performance predictions of smoothness and numver of distress types. One of the most significant changes offered in the Mechanistic Empirical Design Guide (ME PDG) is the difference in the method used to account for highway traffic loading. Traffic volume and traffic loads, the two most important aspects required to characterize traffic for pavement design are treated separately and independently and its use-oriented computational software implements an integrated analysis approach for predicting pavement condiditon over time that accounts for the interaction of traffic, climate and pavement structures. The recently developed guide for mechanistic-empirical (M-E) design of new and rehabilitated pavement structures will change the way in which pavements are designed by replacing the traditional emprirical design approach in the AASHTO 1983 Guide. The M-E Pavement Design Guide will allow pavement designers to make better-informaed decisisions and take cost-effect advantage of new materials and features. However, the proposed design guide is substantially more complex than the 1983 AASHTO design guide. It requires more imput values from the designer. There is limited availability of the data for many MEPDG inputs. This project report presents the Mechanistic-Empirical approach of Pavement Design for New and Rehabilitated Flexible Pavements using the new ME PDG. The main objectives of the report are: (1)to demonstrated how the Mechanistic-Empirical design of pavement is more precise than the existing empirical method, (2)to explain the software input and output parameters, (3)to present a complete overview of the M-E design process and to gain a thorough understanding of the materials, traffic, climate and pavement design inputs required for M-E design.

Truck Weights on Municipal and National Roads without Weight Enforcement

2019 ◽  
Vol 2673 (2) ◽  
pp. 275-284
Author(s):  
Ana Vargas-Sobrado ◽  
Luis Rodríguez-Solano ◽  
José Aguiar-Moya ◽  
Henry Hernández-Vega ◽  
Luis Loría-Salazar
Keyword(s):  
Construction Materials ◽  
Costa Rican ◽  
Pavement Design ◽  
Single Units ◽  
Motor Vehicles ◽  
Heavy Vehicles ◽  
Load Spectra ◽  
Pavement Structures

One of the main causes of premature deterioration in pavement structures is overweight heavy vehicles. To characterize these vehicles, real loads of motor vehicles of more than four tons should be monitored, especially vehicles classified as C2 (2-axles, single units), C3 (3-axles, single units), T3-S2 (5-axles, single trailer) and T3-S3 (6-axles, single trailer) type, as they represent 99.5% of the Costa Rican truck fleet. This study includes six temporal weighing surveys on municipal roads and seven weighing surveys on national roads, comprising the weight of 525 and 554 trucks, respectively. On municipal roads, C2 vehicles with bulk and wagon body types are predominant (67% of surveyed vehicles), whereas on national routes T3-S2 vehicles predominate (42%). Likewise, it was determined that most of exceeding data correspond to vehicles transporting pit material, construction materials, and merchandise on both types of roads. Compared with municipal roads (8%), the percentage of overweight vehicles is more than twice that on national roads (18%) where weight regulations are not enforced. To estimate updated and realistic load data that can be included in pavement design manuals and guides, the same results are provided in parameters such as truck factors and load spectra.

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