Mechanistic-Empirical Pavement Design Research Articles

Resiliency Against Flooding in Pavement Geotechnics: Practice Review of State Transportation Agencies

Extreme weather events driven by climate change pose significant threats to our transportation infrastructure, particularly road pavements, in the United States. This paper proposes a guiding framework for designing resilient pavements. The framework was established after comprehensive reviews of the state of practice from pavement design manuals and construction-, implementation-, and practice documents publicly available on websites of state departments of transportation (DOTs) across the country. The objective of this study was to evaluate the infrastructure flood readiness and awareness of transportation agencies based on the state DOT website surveys. Qualitative analyses of transportation agency engineering and construction manuals revealed a varying degree of consideration among the different states for resiliency in transportation infrastructure design policies. Web scraping of state DOT websites highlighted the awareness levels of resiliency and sustainability issues concerning pavements. The importance of constructing permeable pavement base layers to alleviate flood-induced damage was investigated for assessing moisture infiltration effects on pavement performance and how to incorporate these improvements into mechanistic-empirical pavement design concepts. The findings underscored the need to incorporate climate change stressors and predictive climate data modeling in pavement design to enhance infrastructure resilience. Through study findings and considering implementation of the guidelines proposed in this paper, transportation engineers and state agencies are anticipated to be able to better prepare for future extreme weather challenges and safeguard the nation’s transportation assets.

Comparative Study of Mechanistic-empirical Structural Design Methods Applied to Sustainable Asphalt Mixtures

Introduction/ Background Nowadays, there is a growing interest in achieving sustainable alternatives to conventional flexible pavements. The incorporation of recycled materials into asphalt mixtures has become a departure point. This study focuses on developing sustainable asphalt mixtures incorporating different residues to find a material that performs well on both technical and environmental grounds. Materials and Methods Mixtures with recycled materials and others are compared on a pavement structure. A mechanistic-empirical pavement design method is used to carry out analyses in different conditions. A Life Cycle Assessment is conducted to consider the balance of positive and negative environmental aspects of each mixture and pavement structure. Results The results indicate that all mixtures with sustainable features have a longer life span and an enhanced performance. They also incur higher impacts per tonne produced. However, the “avoided impact” is considered in their favour. Discussion Mixtures with enhanced mechanical properties are key to achieving long-term environmental benefits. The usage of local materials is also essential. Conclusion Proving that unconventional mixtures have a satisfactory performance requires innovative, properly calibrated models and a thorough material characterization. Accurately reflecting on their in-service behaviour is key to evaluating the environmental benefits.

Mechanistic-empirical pavement design to include mechanical performance of rubberized asphalt pavements in the use stage of life cycle assessment

The use phase of road pavements includes fuel consumption, urban heat island, and leaching. Life cycle assessment (LCA) often omits the use phase for its complexity. The use phase should also consider the durability of the asphalt mixtures. This study used the Mechanistic-Empirical Pavement Design to include the durability of five asphalt mixtures (control, styrene-butadiene-styrene - SBS, polymer coated rubber - PCR dry and wet, and devulcanized rubber - DVR) in the LCA of 1-mile road pavement over 50 years. Considering only the impact of the mixture production, the control mix had a carbon footprint 12.9 % lower than the other mixtures. When including the durability, the control mixture's carbon footprint was 69 % and 84 % higher than SBS and PCR dry mix, respectively. This study showed the importance of including the mechanical performance of the asphalt mixtures to better evaluate the environmental impact of road pavements over the service life.

Impacts of climate change on environmental and economic sustainability of flexible pavements across China

Pavements are susceptible to accelerated deterioration due to changing climate conditions, leading to increased maintenance and excess fuel consumption through pavement-vehicle interaction. China's diverse climates raise concerns about the environmental and economic sustainability of flexible pavements amid climate change and the effectiveness of preventative maintenance strategies. This study examines climate change's potential impacts on long-term pavement performance, greenhouse gas (GHG) emissions, and costs, employing the Mechanistic-Empirical Pavement Design Guide method. The calibrated World Bank's Highway Development and Management Model 4 assesses the impacts of surface characteristics on vehicle fuel consumption. Life cycle assessment and life cycle cost analysis quantify GHG emissions and costs. Results reveal significant pavement deterioration in Southeast and Central China. Preventative maintenance strategies reduce fuel consumption, with GHG emissions and cost savings from smoother driving conditions outweighing those from maintenance. These insights stress the importance of proactive maintenance strategies for mitigating climate-induced deterioration and enhancing sustainability.

Optimizing roads for sustainability: Inverted pavement design with life cycle cost analysis and carbon footprint estimation

Inverted pavements have proven performance across the world and there is a need to optimize the layer thickness and material properties of the pavement addressing the critical failures in the Mechanistic-Empirical Pavement Design. The present study is made on bituminous concrete pavement with traffic up to 50 MSA and studying the critical factors affecting the pavement performance. The Minitab’s Response Surface Methodology − Box Behnken method was used for the Design of Experiments which includes critical factors and responses obtained from ANSYS finite element modeling of the inverted pavement. The critical factors and responses are normally distributed and indicate a linear relationship with the least error. The composite desirability for minimum stress and strains in the pavement layers was found to be 0.89. The optimized pavement thickness and layer material properties were validated with two pavement field cross sections of different Indian National Highways and it is observed that the optimized cross-section is safe. Further, this research paper carried out Life Cycle Cost Analysis and Life Cycle Assessment of inverted pavement with optimized pavement cross-section obtained including the carbon footprint during the vehicle operation phase. The study demonstrated the benefits of inverted pavement with reduced costs and carbon emissions. Thus, this approach paves the way towards sustainable and long-lasting pavements.

Concrete Pavement Design Analysis Using AASHTOWare Pavement Mechanistic-Empirical Design Software

Local calibration of pavement performance models embedded in the AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software may be needed to implement this design methodology. A recent survey showed that, despite local calibration, several state highway agencies in the U.S. are using the American Association of State Highway Transportation Officials (AASHTO) Guide for Design of Pavement Structures (1993 version) and the PMED software for comparative design of jointed plain concrete pavement (JPCP). This paper describes the recalibration process of the JPCP distress models in the PMED software and comparative designs obtained by the PMED software and 1993 AASHTO design guide. The results show that the calibration/recalibration process depends on the availability of measured performance data. Not all global coefficients must be changed in the calibration/recalibration process. The 1993 AASHTO design guide yielded higher JPCP slab thickness for projects with high truck traffic than the PMED software when no friction is assumed between the slab and a cementitious base. An assumption of full friction will have a discernible effect on the slab thickness obtained from PMED.

Bus Lane Design Based on Actual Traffic Loads and Climate Conditions

Bus lanes play a crucial role in urban areas as their primary objective is to increase public transport efficiency and help traffic and public transit systems flow more smoothly. This study starts with traffic and climate monitoring to verify asphalt bus lanes in Rome, Italy, according to the Italian Pavement Design Catalogue published in 1995. KENLAYER software calculated the stress-strain conditions under real traffic loads (i.e., hourly passages of urban buses, considering their axle load and seat occupancy rate), typical subgrade bearing capacity (i.e., resilient modulus equal to 90 MPa), current climate conditions, and road material properties. Then, the Mechanistic-Empirical Pavement Design Guide (MEPDG) was used to verify the response of the pavement structure. The fatigue verification of bound materials resulted in damage values much lower than 1 at the end of the 20-year service life (i.e., 0.12 with the Asphalt Institute and 0.31 with the Marchionna law, respectively) and highlights that the Italian catalogue’s sheets are overdesigned. On the other hand, the rutting verification according to MEPDG is not satisfied after an 11-year service life (i.e., the total rutting is equal to 1.50 cm), forcing frequent and expensive maintenance of wearing and binder courses. Therefore, the results confirm the validity of the Italian catalogue for fatigue service life and suggest the need for high-performance asphalt to prevent early rutting due to bus traffic increasing by load and frequency in previous decades.

Evaluation of Subgrade Resilient Modulus using CBR Test Data to Facilitate Mechanistic-Empirical Pavement Design (MEPD)

Sri Lankan road pavement design has relied on empirical design guidelines, and it is now transforming to a more modern approach known as the Mechanistic-Empirical Pavement Design (MEPD) method, aligning with global best practices. One crucial input parameter in this new methodology is the Resilient Modulus (RM) of unbound materials. Determination of RM and establishment of typical values is essential to implement the MEPD. This study has devoted significant attention to the RM evaluation process and developing predictive models for determiningRM from CBR values. Furthermore, typical RMvalues for locally available soil types have been established. In addition to that, ageospatial database has been established with information on soil properties and RMvalues throughout Sri Lanka. This database is a valuable resource for M-E road pavement design, providing quick reference data to conceptual designs and Level 3-MEPD.

Numerical analysis of field aging in asphalt pavements via coupling of diffusion process and kinetics aging model

The asphalt aging model is a crucial module in the Mechanistic-Empirical (ME) pavement design programme for asphalt pavement performance prediction. In this study, an analytical aging model that couples diffusion and kinetics aging model (CDKAM) is improved to analyze and predict the field aging of asphalt pavements. The complex modulus (E*) of field cores was used as the aging property to illustrate the derivation of explicit expressions of time-averaged thermal source term ( TST ¯ ( t ) ) and time-averaged diffusion coefficient ( D ¯ ( t ) ). The value of the material-related parameter (M) in TST ¯ ( t ) model differs among different mixtures, which could assess the mixture’s susceptibility to thermal oxidation. The CDKAM with TST ¯ ( t ) and D ¯ ( t ) was used to predict E* of field aging for asphalt pavements in Texas after 22-month service life. The calibrated CDKAM could reasonably quantify the aging extent over time and the aging gradient over depth for studied asphalt pavements.

Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness

The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.

Using Repeated Light-Weight Deflectometer Test Data to Predict Flexible Pavement Responses Based on the Mechanistic–Empirical Design Method

This study investigated the potential of lightweight deflectometer (LWD) data in predicting layer moduli and response measurements within the Mechanistic–Empirical Pavement Design Guide. To achieve this goal, field repeated LWD tests and laboratory repeated load triaxial tests were carried out on granular base material compacted at 3% and 6% water content, sandy subgrade soil compacted at 3%, 4% and 9% water content and silty sand subgrade soil compacted at 8% and 10% water content. The results revealed that substituting traditional repeated load triaxial (RLT) data with LWD data for predicting these parameters was notably effective for cohesionless materials, especially for unbound granular materials (UGMs) compacted at optimum water content. The accuracy and reliability of predictions were remarkably high, showcasing the potential of LWD to enhance efficiency and precision in pavement design within this context. Conversely, for cohesive road materials, the study emphasized the importance of considering specific material properties and water content when integrating LWD into the Mechanistic–Empirical Pavement Design Guide. The distinctive characteristics and behaviors of cohesive materials necessitate a nuanced approach. This understanding is critical to ensuring the accuracy and reliability of pavement design and assessment across diverse conditions. In summary, the study presents a promising avenue for utilizing LWD data in cohesionless road materials, offering potential cost and time-saving advantages. Additionally, it underscores the necessity of tailored approaches when considering material properties and moisture content for cohesive materials, thereby advancing the field of pavement engineering by providing insights for improved practices and adaptable frameworks.

Empirical transfer functions for foam glass aggregates insulation used in flexible pavement layered systems

Pavement design in cold regions is challenging due to the difficult conditions of soils, humidity, and temperatures. Insulation layers have been identified as a suitable solution for these conditions. Due to their unique engineering properties, foam glass aggregates (FGAs) are a promising material for use as an insulating granular layer in pavement design. However, understanding their mechanical performance is critical for predicting long-term layer and pavement behavior. In this laboratory study, an empirical transfer function was developed using an environmental and heavy vehicle simulator and an experimental pavement built in an indoor test pit. The study aimed to determine the allowable number of load repetitions for an FGAs insulation layer and to develop an empirical transfer function that can be used as part of a mechanistic-empirical pavement design procedure. This article proposes a linear relationship between permanent deformation, the number of load cycles, and the equivalency factor between the effect of resilient strain, or vertical stress, and allowable damage. The proposed empirical transfer functions allow defining an allowable number of load repetitions for a characteristic resilient strain or vertical stress and an allowable damage. The allowable damage can be modulated with respect to road classification, and a damage value of 0 % to FGAs layer can be considered as a safety factor. The findings of this study provide valuable insights into the use of FGAs as an insulating granular layer in pavement design in cold regions.

Traffic input module for mechanistic-empirical pavement design with weigh-in-motion data

• Weigh-in-motion data provides large quantity of traffic information for pavement design. • Reliability of pavement design highly depends on the quality of truck traffic data. • The weigh-in-motion recorded traffic data may not be sufficiently accurate for satisfactory pavement design. • The accuracy of weigh-in-motion traffic data can be improved through machine learning algorithms. The success of the mechanistic-empirical pavement design guide implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Truck axle load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to create an accurate traffic design input module, and thus to improve the quality of pavement designs. The traffic input module was created with the most recent data to better reflect the axle load spectra for pavement design. The unclassified vehicles by weigh-in-motion devices were analyzed and a neural-network-model-based classification method was utilized to determine the appropriate allocations of unclassified vehicles to truck classes. The updated truck traffic information includes average annual daily truck traffic, truck volume monthly adjustment factors, truck volume lane distribution factors, truck volume directional distribution factors, truck volume class distributions, traffic volume hourly distribution factors, distributions of for single-axle, tandem-axle, tridem-axle, and quad-axle loads, average axle weight, average axle spacing, and average number of axle types.

Carbon emissions quantification and different models comparison throughout the life cycle of asphalt pavements

Establishing a comprehensive and systematic method for quantifying carbon emissions (CEs) of asphalt pavement is challenging due to the multifaceted and diverse nature of the involved processes. Utilizing a life cycle assessment framework, this study introduces a practical model system for capturing CEs across the entire lifespan of asphalt pavement. A pivotal aspect of our approach involves calculating activity levels. Material quantities are ascertained based on mixture proportions and specific pavement information. We employ Ridge Regression and the quota method to approximate the energy consumption of construction machinery. A comparative analysis is performed between two CE computation models that use energy consumption and operational time as activity levels, respectively. Additional vehicle fuel consumption is translated based on shifts in the International Roughness Index, as predicted by the Mechanistic-Empirical Pavement Design Guide. CEs from traffic disruptions are calculated collaboratively through VISSIM simulations and MOVES emission modeling. Our findings reveal that material production, mixture preparation, and maintenance duration are the principal factors contributing to CEs throughout the asphalt pavement lifecycle. Notably, emission estimates for machinery in the construction phase can vary by as much as 8.3 times across different models. The study suggests that incorporating the use of recyclable materials and designing more durable pavement structures can offer effective avenues for CE mitigation.

Temperature effect on fatigue behavior of high-modulus asphalt concrete (HMAC)

This study investigated the effect of temperature on the fatigue behavior of high-modulus asphalt concrete (HMAC). The impact of temperature on the Wöhler’s curve parameters, especially with respect to the slope and intercept, was studied to evaluate the accuracy of current methodologies used to estimate them in mechanistic–empirical pavement design tools (M-E PD). Four series of tension–compression (TC) fatigue tests were conducted on HMAC samples at four temperatures (-8.3 °C, 1.4 °C, 11.3 °C, and 21.0 °C). The findings indicated a significant impact of temperature on the intercept; the slope did not exhibit any statistically significant effect.

Resilient response and strength of highly expansive clay subgrade stabilized with recycled concrete aggregate and granulated blast furnace slag

In urban areas, due to urban development and lack of space, roads may be built on the expansive clay subgrade. Expansive clay soils increase in volume when in contact with water and shrink by losing water. Recycled concrete aggregates (RCA) are one of the components of construction and demolition (C&D) wastes that can be used instead of natural resources in road construction projects. In this research, a combined method of mechanical and chemical stabilization was used to evaluate the use of RCA as a mechanical stabilizer and granulated blast furnace slag (GBS) as a chemical stabilizer to improve the strength and resilience characteristics of expansive clay subgrade. Laboratory results showed that adding RCA wastes to expansive clay subgrade reduced atterberg limits (LL, PL, and PI) and optimum water content (OWC) and increased the maximum dry density (MDD), California bearing ratio (CBR), and uniaxial compression strength (UCS). Expansive clay subgrade with 20 % RCA was treated with GBS chemical stabilizer in order to achieve a minimum 7 days target strength of 1.7 MPa for subbase application. A series of repeated load triaxial (RLT) tests were performed according to the AASHTO T307 procedure and resilient modulus (Mr) values of the stabilized clay subgrade specimen were evaluated at different curing times under cyclic loading. Also, the proposed Mechanistic-Empirical pavement design Guide (MEPDG) model was used to predict Mr values in different confining and deviator stresses which showed a good correlation with the measured Mr values. Finally, the improvement of the clay subgrade stabilized with RCA and GBS was also supported by SEM analysis. The results obtained from this research showed that the use of RCA, in addition to reducing disposed waste and environmental pollution, can improve the physical and mechanical characteristics of expansive clay Subgrade layers.

Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils

ABSTRACT The Resilient Modulus (Mr) is perhaps the most relevant and widely used parameter to characterise the soil behaviour under repetitive loading for pavement applications. Accordingly, it is a crucial parameter controlling the mechanistic-empirical pavement design. Nonetheless, determining the Mr by laboratory tests is not always possible due to the high consumption of time and financial resources. Thus, developing new indirect approaches for estimating the MR is necessary. Precisely, this article investigates the application of Deep Neural Networks (DNNs) and statistical methods to predict the Mr of soils. For that purpose, the Long-Term Pavement Performance (LTPP) database was implemented. It includes 64 701 datasets resulting from coarse-grained and fine-grained soil samples considering a wide range of grain size distribution and subjected to different stress levels. The input parameters were the bulk stress, octahedral shear stress, and the percentage of soil particles passing through the different sieves (3”, 2”, 3/2”, 1”, 3/4”, 1/2”, 3/8”, No. 4, No. 10, No. 40, No. 80, and No. 200) and the output was the Mr. The results suggest that while conventional mathematical models are unable to predict the influence of the grain size distribution and stress level on the Mr, the proposed DNNs were able to reproduce very accurate predictions. Notably, the proposed computational models have been uploaded to a GitHub repository and have become a valuable tool for forecasting the Mr when experimental measurements are not feasible.

Reliability analysis of flexible pavements based on the quantile-value method

ABSTRACT This paper presents a simple yet efficient approach for the reliability-based design of flexible pavements, based on the concept of quantile-values. The reliability-based approaches currently adopted in the pavement design guides are simplified techniques, based on an overall reliability factor. On the other hand, simulation-based probabilistic techniques, widely discussed in literature, can be computationally expensive and tedious to implement. The ‘Simplified Effective Random Dimension Quantile Value Method’ (Simplified ERD-QVM), adopted in this study, presents an elegant technique to bridge the gap between the precision of simulation-based probabilistic techniques and the efficiency of design guide approaches. The accuracy of this technique is verified through Monte Carlo Simulations for different pavement design scenarios. The main advantages of the method are (i) the significant savings in computational times for reliability-based design of flexible pavements without the use of surrogate models (ii) the use of simple techniques already familiar to practitioners who adopt the Mechanistic-Empirical Pavement Design Guide and (iii) it is a comprehensive reliability analysis approach that addresses the drawbacks of the currently used approaches, while capturing the effect of both design parameter and model uncertainty. The methodology for integrating the Simplified ERD-QVM with the existing flexible pavement design codes is also presented.

Comprehensive Balanced Mixture Design Protocol: Design Phase through Production Quality Assurance

The objective of this study was to develop a comprehensive balanced mix design (BMD) protocol for a state highway agency (SHA) using one of the four approaches presented by both the National Asphalt Pavement Association (NAPA) and the American Association of State Highway and Transportation Officials (AASHTO). This comprehensive BMD was to ensure that a mixture design continues to be balanced from the mixture design phase to the production quality assurance (QA) phase. While existing publications by NAPA, AASHTO, and others provide a good initial framework for developing a BMD protocol, many important questions arise that are left for each SHA to answer when developing a comprehensive BMD protocol. This paper outlines these questions and how each of them was addressed. Also, a new approach for developing a pass/fail criterion for bottom-up fatigue cracking using AASHTOWare® pavement mechanistic-empirical design (PMED) predictive models is presented. Twenty-one SHA-approved plant-produced mixtures were obtained and tested to aid in the development of this pass/fail criterion. A similar approach as presented in this paper can be used by other SHAs. A comprehensive BMD protocol is presented at the end of this paper along with recommendations for future studies needed for further refinement.

Evaluation of Three Federal Highway Administration Targeted Overlay Pavement Solutions for Asphalt Pavements Based on Their Performances and Potential to Increase Pavement Service Life

The Federal Highway Administration Targeted Overlay Pavement Solutions (TOPS) program presents innovative overlay solutions for both asphalt and concrete pavements. The objective of this study was to conduct a comparative evaluation of three TOPS types for asphalt pavements, namely, Asphalt Rubber Gap-Graded, Stone Matrix Asphalt and High-Performance Thin Overlay as regards performance, how they extend pavement service life, and whether they can be used interchangeably. Asphalt binders from two sources were used in each of these three TOPS types, thus, providing a total of six TOPS mixtures. All TOPS passed the mixture performance tests where the test had a State Transportation Agency (STA) specified or a preliminary criterion, and the test results indicated that all three TOPS types can be used interchangeably. More specifically, all six TOPS mixtures met the Massachusetts Department of Transportation’s TOPS pilot specification criteria for rutting, moisture damage, and intermediate-temperature cracking. They also passed the mixture performance tests for reflective cracking and raveling based on criteria established by other STAs. AASHTOWare® Pavement Mechanistic-Empirical Design predictive models for bottom-up fatigue cracking indicated that 2 in. of a TOPS mixture placed on an existing in-service pavement can extend pavement service life between 11.2 and 14 years depending on the type of TOPS and source of binder. The maximum difference in service life between the six TOPS was slightly less than 3 years, although most of them were within 2 years of each other. Furthermore, none of the three TOPS types could be chosen to be the best TOPS.