Resilient Modulus Research Articles

Experimental study on the resilient behavior of unbound granular materials and the evaluation of prediction model

Although there have been many laboratory studies on the resilient behavior of road UGMs, however, the relative roles of various factors on <i>M</i><sub>r</sub> and its prediction model are not clearly clarified. This study conducted a series of cyclic tests on UGMs. Test results show that the effects of fines content and oversized soil particle on the resilient modulus are limited. The initial stresses are the primary factors influencing the resilient behavior, and the impact of initial deviatoric stress on <i>M</i><sub>r</sub> is much smaller compared to confining pressure. Both the amplitude and frequency of cyclic stress have significant effects on <i>M</i><sub>r</sub> , while the influence of loading waveform is relatively small. The prediction model of <i>M</i><sub>r</sub> recommended by JTG D50-2017 is employed to fit the test results, and it is found that the loading frequency is the predominant factor influencing the regression coefficients, the initial deviatoric stress and loading waveform are moderate factors, while the fines content and oversized soil particle are minor factors. A model involving the factor of loading frequency is proposed, and its accuracy and application upon various factors are evaluated. Some advice is given for the application of UGMs on road subgrade and pavement base/subbase.

Remolding Water Content Effect on the Behavior of Frozen Clay Soils Subjected to Monotonic Triaxial Loading

Understanding the mechanical behavior of frozen clay subgrade soils was essential for ensuring the safe and stable operation of transportation lines. However, the influence of remolding water content w on this behavior remained unclear. To address this gap, this study examined the effect of w through monotonic triaxial testing. Three typical remolding water contents (w = 19%, 27.5% and 35%) and three confining pressures (σ3 = 200 kPa, 700 kPa and 1200 kPa) were considered. Results showed that the mechanical behavior of frozen clay soils displayed a clear dependence on w, which was controlled by microstructural evolution. As w increased, the shear strength qmax, resilient modulus E0 and cohesion c increased, which resulted from the progressive development of ice bonding within the shear plane. A threshold w value was found at wopt = 27.5%, marking a structural transition and separating the variations of qmax, E0 and c into two regimes. When w ≤ 27.5%, the soil fabric was controlled by clay aggregates. As w increased, the growth in ice cementation was confined within these aggregates, leading to limited increase in qmax, E0 and c. However, as w exceeded 27.5%, the soil fabric transitioned into a homogeneous matrix of dispersed clay particles. In this case, increasing w greatly promoted the development of an interconnected ice cementation network, thus significantly facilitating the increase in qmax, E0 and c. The friction angle φ decreased with w increasing, primarily due to the lubrication effect caused by the growing ice. In addition, the enhanced lubrication effect in the clay particle-dominated fabric (w > 27.5%) resulted in a larger reduction rate of φ. Regarding Poisson’s ratio v and dilation angle ψ, the w increase led to growth in both parameters. This phenomenon could be explained by the increased involvement of solid ice into the soil structure.

Durability of Recycled Concrete Aggregate as a Pavement Base Material Including Drainage: A Laboratory and Simulation Study

Recycled concrete aggregates (RCAs) have the potential to be used as a sustainable, cost-effective, and environmentally friendly material in pavement base construction. However, there is a lack of information on the durability, strength, and hydraulic properties of RCA. The primary purpose of this study was to evaluate the properties and performances of commonly available RCAs in Oklahoma as pavement bases through laboratory testing and AASHTOWare Pavement ME simulations. For this purpose, three RCAs (RCA-1, RCA-2, and RCA-3) and a virgin limestone aggregate (VLA-1) were collected from local sources. RCA-1 and RCA-3 were produced in the field by crushing the existing concrete pavement of Interstate 40 and US 69 sections, respectively. RCA-2 was produced by crushing concrete and rubble collected in a local recycling plant. Laboratory testing for this study included particle size distribution, wash loss, optimum moisture content and maximum dry density (OMC-MDD), Los Angeles (LA) abrasion, durability indices (Dc and Df), permeability (k), and resilient modulus (Mr). The properties of aggregates were compared and the service life (performance) of aggregate bases was studied through mechanistic analysis using the AASHTOWare Pavement ME design software (version 2.6, AASHTO, USA). The results indicated that the properties of RCAs can differ greatly based on the origin of the source materials and the methods used in their processing. Recycled aggregates from concrete pavements of interstate and state highways exhibited similar or improved performance as virgin aggregates. RCA produced in a recycling plant was found to show durability and strength issues due to the presence of inferior quality materials and contaminants. Also, the results indicated that the fine aggregate durability test is a useful tool for screening recycled aggregates to ensure quality during production and construction. Bottom-up fatigue cracking was identified as the most affected performance criterion for flexible pavements when using RCA as the base layer. The findings will help increase the use of RCA as pavement base to promote environmental sustainability.

Effect of Wetting and Drying Cycles on Resilient Modulus and Microstructure Characteristics of Heterogeneous Quicklime-Stabilized Subgrade

The resilient modulus changes due to post-compaction seasonal variations in weather such as wetting and drying, thus affecting pavement performance. Existing research highlights significant differences in back-calculated resilient modulus test outcomes for compacted quicklime-stabilized subgrades after construction. Nonetheless, limited studies exist on the effect of the wetting and drying cycles considering the variability in the compacted quicklime stabilized composites. Using a Taguchi L9 orthogonal array, four groups of nine samples varying in dry density, water, and quicklime contents were created and subjected to multiple wetting and drying cycles (0th, 4th, 8th, and 12th). Resilient modulus testing revealed an initial increase up to the 8th cycle, followed by a decrease, with a more pronounced negative effect on samples with lower quicklime and water contents. SEM-EDX, XRD, and FTIR analyses indicated distinct microstructural responses after each cycle, with SEM micrographs showing complex pore structures of low circularity and the formation of average form factors ranging from 0.301 to 0.3564. Canonical correlation analysis between the pore geometrical properties and resilient modulus is approximately 0.689, the associated Wilks' Lambda value is 0.526, and the eigenvalue of 0.901, thus offering direct relationships between macroscopic and microscopic properties of the materials. The highest calcium content was found in the 0th cycle samples. This study contributes to a better understanding of the resilient modulus characteristics and microstructural evolution of stabilized quick-lime subgrades, offering valuable insights for engineers and researchers in pavement design and maintenance.

Evaluating subgrade dynamic and static resilience modulus through enhanced testing techniques

Evaluating subgrade dynamic and static resilience modulus through enhanced testing techniques

Olive pomace lignin as a sustainable bio-based modifier for asphalt: performance, economic, and environmental evaluation

The pressing challenges of environmental pollution and the depletion of fossil resources have intensified the search for renewable and sustainable materials in the pavement industry. In response, this study investigates the potential of olive pomace lignin (OPL), a bio-product derived from agricultural waste, as a sustainable and eco-friendly modifier for asphalt binders and concrete mixtures. Unlike prior studies utilizing raw olive pomace or its ash, this research isolates lignin—a high-performance biopolymer—enhancing compatibility, stability, and mechanical performance. The OPL was extracted using acid hydrolysis and blended with asphalt binder at dosages ranging from 4 to 12%. Comprehensive laboratory evaluations, including penetration, ductility, softening point, rotational viscosity, Marshall stability, and resilient modulus tests, were conducted to assess the mechanical and rheological characteristics of asphalt binder and mixture performance. Results highlight that 10% OPL represents the optimal dosage, delivering improved stiffness, resistance to deformation, and thermal stability without compromising workability. Economic and environmental assessments demonstrate 22.3% cost savings, 5–7% energy reductions, and 6.1–9.8% lower CO2 emissions compared to conventional asphalt. These findings emphasize OPL’s role as a sustainable, high-performance alternative for modern infrastructure, promoting circular economy principles by transforming agricultural waste into functional materials for road construction.

Development of a Comprehensive Computational Framework for Evaluating Geogrid Reinforcement Effectiveness in Flexible Pavement Design

Flexible pavements face increasing deterioration under growing traffic loads and adverse environmental conditions. This study develops a comprehensive computational framework for evaluating geogrid reinforcement effectiveness in flexible pavement performance through synthetic data modeling and statistical analysis. The research methodology involves creating a Python-based analytical tool that generates realistic synthetic datasets representing various pavement configurations. The framework encompasses multiple geogrid types (unreinforced, uniaxial, biaxial, and triaxial) across diverse soil conditions and traffic loading scenarios. Key performance parameters include rutting depth, fatigue life, bearing capacity, resilient modulus, settlement characteristics, and integrated performance indices. The analytical framework employs advanced statistical techniques including ANOVA, correlation analysis, and machine learning algorithms for predictive modeling. Comprehensive data visualization capabilities enable three-dimensional surface plotting and cost-benefit analysis for design optimization. The synthetic data generation is based on established pavement engineering principles and documented geogrid performance relationships. This computational methodology provides pavement engineers with a robust tool for preliminary design evaluation and parameter sensitivity analysis. The framework enables systematic exploration of design alternatives that may be difficult to test under field conditions. The research contributes an accessible analytical platform for evidence-based geogrid reinforcement design decisions and establishes foundations for future validation studies against field performance data.

Investigating the Density of Granular Materials for Road Base Performance

Inadequate compaction of road base layer can induce to permanent deformation and fatigue cracking in pavement structure which resulted to pavement deterioration and reducing its lifespan. This study focuses on the investigation the effect of density and level of stress on road base granular material under repeated load application. The gradation specification for road base compaction at optimum moisture content was 90% and 95% of maximum dry density. The granular aggregate was poured into a mould of 100 mm diameter and 200 mm height and compacted by applying 4.5 kg hammer at the desire density. The extruded specimens were tested for repeated triaxial load tests using the universal testing machine. The result shows that the increase of 90% to 95% of maximum dry density granular material has increased the resilient modulus value from 2126.0 kg/m3 to 2246.0 kg/m3, respectively. The MICH-PAVE analysis shows increasing of granular densification has increase the fatigue life in asphalt pavement and reduced rut depth of the road base layer. This is due to less void between aggregates particles thus increased their density. It can be concluded that the increasing of compaction effort of road base granular material has increased maximum dry density and subsequently improving the resilient modulus value and fatigue life for better resistance to rut depth in road pavement.

Exploring Relationships between Summary Resilient Modulus, California Bearing Ratio, and Light Weight Deflectometer Modulus for Unbound Coarse Materials in Idaho

Mechanistic–empirical pavement design (using AASHTOWare Pavement ME Design software) incorporates inputs at three different levels (i.e., Level 1, Level 2, and Level 3). Level 1 input is preferred over Level 2 and Level 3 in AASHTOWare Pavement ME Design software. Level 1 inputs are project- or job-specific, and are obtained through direct measurements or laboratory testing. Level 1 input measurements, such as the resilient modulus ( M R ), might require additional resources to those required for Level 2 inputs. Many transportation agencies develop Level 2 inputs, which are more region-specific. For unbound materials, M R is considered one of the fundamental material properties. Measuring M R through laboratory testing requires advanced equipment and trained technicians, and the test is time-consuming. This study is an evaluation of the correlations between the resilient modulus of commonly used unbound materials in Idaho and the California bearing ratio (CBR) to develop Level 2 inputs. In addition, the recently introduced light weight deflectometer (LWD) modulus was correlated with the resilient modulus. The results of this study demonstrate a good and promising correlation between resilient modulus and LWD modulus, and a fair correlation between resilient modulus and CBR.

Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring the potential of incorporating more easily obtainable strength indicators, such as UCS. To evaluate the potential effectiveness of UCS for pavement engineering applications, a dataset of 152 laboratory-tested soil samples was compiled to develop predictive models. For each sample, geotechnical properties including the Atterberg limits, liquid limit (LL), plastic limit (PL), water content (WC), and bulk density (determined using the Harvard miniature compaction apparatus), alongside the UCS, were measured. This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). The aim was to establish a relationship between the dependent variable (UCS) and the independent basic geotechnical properties and to test the effectiveness of each ML algorithm in predicting UCS. The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R2 of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. The performance ranking of the other models, from best to worst, was RF, GB, SV, KNN, MLR, and MNLR.

Evaluation of Resilient Modulus Prediction Models for Saturated Pavement Base Aggregates

Given the increased frequency of heavy rainfall, there is a high probability that pavement base aggregates in the field, particularly in coastal regions, will become saturated and remain so for extended periods. The increase in moisture content can reduce the resilient modulus ( M R ) of base materials. There are numerous constitutive models that help predict the resilient modulus of base aggregates based on moisture content and stress states, to assist pavement design. The performance of most of these models has been evaluated previously, but not at full saturation. This study is an evaluation of the two best performing prediction models, as determined in previous studies and current practices. The assessment was carried out using M R test data for seven laboratory-saturated base aggregates with different properties and moisture sensitivities (high and low). The models were first calibrated based on M R results at the optimum moisture content and then used to predict M R of each base material at saturation. The accuracy of the prediction and the applicability to base aggregates with different moisture sensitivities formed the basis of the evaluation. The models were found to be more accurate and even conservative when predicting M R of saturated base aggregates with low moisture susceptibility. However, the models were erroneous in their prediction and overestimated M R at saturation of highly moisture susceptible pavement base aggregates. An adaptation solution was proposed to improve the prediction accuracy of the existing models. This study joins efforts to design our climate-vulnerable roadways more accurately and improve pavement resilience.

Influence of Moisture on the Shakedown Behavior of Fine Soils for Sustainable Railway Subballast Layers

This study investigates the influence of moisture on the mechanical behavior of fine soil mixtures from the São Luís region, applied as subballast layers in railway track structures. Two samples were analyzed: a non-lateritic sandy soil (NA’, AM03) and a lateritic clayey soil (LG’, AM09). The research included physical and chemical characterization tests, as well as repeated load triaxial tests to determine the resilient modulus and shakedown limits, complemented by numerical simulations using the SysTrain 2.0 software. The samples showed average resilient modulus values of 577 MPa and 638 MPa, respectively. Tests were conducted under optimum moisture content and under moisture 1% above the optimum, induced by capillary rise in compacted samples. The results indicated that under 1% above optimum moisture, the shakedown limits were reduced by up to 50% for AM03 and 25% for AM09, demonstrating greater stability for the lateritic soil. In addition, it was observed that as stress ratios increased, the shakedown limits for both moisture conditions tended to converge. Numerical simulations confirmed the adverse influence of increased moisture on the occurrence of shakedown in both samples. For AM03, the simulations revealed progressive failure under elevated moisture, indicating a more severe stress redistribution within the subballast layer. In contrast, AM09 remained within the shakedown regime under both conditions, although it exhibited higher values of S1/S1max under moisture above optimum, suggesting a greater tendency toward plastic creep. These findings highlight the critical importance of moisture control for the sustainable performance of railway substructures. This study contributes to understanding environmental vulnerability in transportation infrastructure and supports the development of more resilient and sustainable railway systems.

Effect of the input of structural parameters’ uncertainties and analysts’ arbitrary decisions on the results of backcalculated pavement materials’ resilient moduli

This study examines how uncertainties in the input of structural parameters and analysts' decisions affect the estimated resilient moduli of pavement layers obtained through backcalculation. Three structures with varying asphalt layer thicknesses were created to predict deflectometric basins, using the Falling Weight Deflectometer (FWD). Backcalculations were then performed with variations in layer thicknesses (within the 5% range), Poisson's ratio of each layer, and seed moduli of each layer. This was repeated 60 times for each structure with randomized parameter variations. Resilient moduli from backcalculations were compared to reference structures, revealing relative errors. Statistical analysis showed uncertainties in layer thicknesses and that Poisson's ratios impact backcalculation results by 3% to 33%, depending on asphalt layer thickness. This variability could alter conclusions in pavement assessment. This is a case study with real deflectometric basins validated theoretical findings using field data.

Influence of Coal Bottom Ash as Fine Aggregate Replacement on the Mechanical Properties of Stone Mastic Asphalt

Coal bottom ash (CBA) is a waste produced by burning coal that presents possible hazards to human well-being and the environment. Rapid economic expansion has increased the utilisation of CBA, resulting in a crisis concerning the disposal of this waste. By employing waste as a replacement for natural materials, it is possible to achieve sustainable and environmentally friendly construction. This study assesses the effects of utilising CBA waste as a replacement for fine aggregate in stone mastic asphalt (SMA) pavement. Seven asphalt mixture proportions were designed, each of which employed a different percentage of CBA (0%, 10%, 20%, 30%, 50%, 70%, and 100%) as a fine aggregate replacement. The performance tests conducted in this research were the Cantabro durability test, resilient modulus test, dynamic creep test, and moisture susceptibility test. The findings showed an improvement in the durability and resistance to permanent deformation of the SMA mixtures with 30% and 50% CBA replacement, respectively. However, further increases in the CBA content caused a decrease in the durability and resistance to permanent deformation. Meanwhile, the stiffness and tensile strength ratio (TSR) value decrease with the use of CBA replacement at any percentage. However, the TSR value of the SMA mixtures with 50% or less CBA replacement was more than 80%, which meets the minimum requirement set by JKR. In conclusion, incorporating CBA into SMA mixture has a positive effect on certain mechanical properties, particularly its durability and resistance to permanent deformation at optimal replacement levels, highlighting its potential to be used as a sustainable material in asphalt pavement construction.

Predictive Performance Evaluation of an Eco-Friendly Pavement Using Baosteel’s Slag Short Flow (BSSF) Steel Slag

Predicting pavement performance is essential for highway planning and construction, considering traffic, climate, material quality, and maintenance. This study’s main objective is to evaluate Baosteel’s Slag Short Flow (BSSF) steel slag as a sustainable aggregate in pavement engineering by means of durability. The research integrates pavement performance prediction using BSSF and assesses its impact on fatigue resistance and percentage of cracked area (%CA). Using the Brazilian mechanistic-empirical design method (MeDiNa), eight scenarios were analyzed with soil–slag mixtures (0%, 25%, 50%, and 75% slag) in base and subbase layers under two traffic levels over 10 years. An asphalt mixture with 15% steel slag aggregate (SSA) was used in the surface layer and compared to a reference mixture. Higher SSA percentages were applied to the base layer, while lower percentages were used in subbase layers, facilitating field implementation. The resilient modulus (MR) and permanent deformation (PD) were design inputs. The results show that 15% SSA does not affect rutting damage, with %CA values below Brazilian limits for traffic of 1 × 106. The simulations confirm BSSF as an effective and sustainable alternative for highway pavement construction, demonstrating its potential to improve durability and environmental impact while maintaining performance standards.

Investigating the effect of overloaded vehicles on asphalt pavement: a case study in India

Overloading road freight vehicles will accelerate the damaging effect on pavement, result in unfair transport demand, and increase road safety risks for road users. Pavement traffic characteristics are characterised by various vehicle and axle types; these factors are measured in pavement design employing the truck factor. The effective use of truck factors in pavement design is to convert truckloads into standard axles or load equivalence factors (LEF). A falling weight deflectometer test was conducted in the field to examine the subgrade resilient modulus of pavement layers. Thus, this study investigates the significant effect of overloaded vehicles on the pavement by considering the truck factor for various existing vehicle classes and a set of five different asphalt pavement thicknesses and subgrade resilient moduli values, respectively. For vehicle classes B1 and B2, with an increase in asphalt layer thickness from 20 to 100 mm, the truck factor decreased by almost 49 and 50%, respectively. The truck factor is negligible when the subgrade resilient modulus/stiffness increases. Road transportation authorities may require special attention to overloaded vehicles during the inspection. This study will help highway authorities to make policy decisions on overloaded vehicles.

Exploring the feasibility of high RAP content in dense bituminous macadam mixes: a comprehensive study

This study evaluates the feasibility of incorporating high Reclaimed Asphalt Pavement (RAP) content in Dense Bituminous Macadam (DBM-II) mixes using bio-oil rejuvenator. Five DBM-II mixes containing 0%, 30%, 40%, 50%, and 60% RAP were prepared and evaluated. Optimum rejuvenator dose was determined using dynamic shear rheometer test and blending chart. Performance tests included indirect tensile strength, tensile strength ratio, resilient modulus, dynamic creep, and texas overlay test. Statistical analysis was done using SPSS. Following encouraging lab results, two trial sections (500 m long and 9 m wide) were constructed using control and 60% RAP. Field cores were tested after one winter, summer, and monsoon cycle. Results showed 60% RAP mixes had higher stiffness, improved rutting resistance, adequate moisture resistance, and comparable cracking performance compared to other mixes. Field results validated the feasibility of using high RAP content, demonstrating that the 60% RAP mix outperformed the control mix.

Study on the Temperature-Dependence of the Modulus of LSAM-50 Pavement Materials.

To investigate the temperature dependence of the modulus of LSAM-50 flexible base asphalt pavement (LSAM-50 pavement) materials, specifically SMA-13, AC-20, and LSAM-50. The effects of temperature on the modulus of LSAM-50 pavement materials were investigated, and a temperature-dependent model of resilient modulus was established. A dynamic modulus master curve was constructed based on a generalized logarithmic Sigmoidal model. The correlation between the resilient modulus and dynamic modulus was studied, and a multiple linear regression model was developed to describe the relationship between the dynamic modulus and resilient modulus, temperature, and loading frequency. The results show that the resilient modulus and dynamic modulus gradually decrease with the increase in temperature and then tend to stabilize. The resilient modulus of LSAM-50 is higher than that of SMA-13 and AC-20 in the entire temperature range, and the dynamic modulus of LSAM-50 is higher than that of SMA-13 and AC-20 in the high-temperature range. The correlation coefficients (R2) of the established resilient modulus and dynamic modulus estimation models are greater than 0.97 and 0.94, respectively.

Influence of suction stress on the resilient modulus model for unsaturated aggregate base

Influence of suction stress on the resilient modulus model for unsaturated aggregate base

Resiliency, morphology, and entropic transformations in high-entropy oxide nanoribbons

We present the successful synthesis and characterization of a one-dimensional high-entropy oxide (1D-HEO) exhibiting nanoribbon morphology. These 1D-HEO nanoribbons exhibit high structural stability at elevated temperatures (to 1000°C), elevated pressures (to 12 gigapascals), and long exposure to harsh acid or base chemical environments. Moreover, they exhibit notable mechanical properties, with an excellent modulus of resilience reaching 40 megajoules per cubic meter. High-pressure experiments reveal an intriguing transformation of the 1D-HEO nanoribbons from orthorhombic to cubic structures at 15 gigapascals followed by the formation of fully amorphous HEOs above 30 gigapascals, which are recoverable to ambient conditions. These transformations introduce additional entropy (structural disorder) besides configurational entropy. This finding offers a way to create low-dimensional, resilient, and high-entropy materials.