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Warm Mixed Asphalt (WMA) is regarded one of the most preferred techniques of producing asphalt pavement materials since it has comparable performance to Hot Mixed Asphalt (HMA) while having lower production and compaction temperatures. The current study investigates the impact of WMA additives (organic-Sasobit and Chemical-Zycotherm) on compassion energy parameters like compactibility factor (CF), number of gyrations (Ndesign), energy consumption, and intermediate cracking potential of asphalt mixtures containing Reclaimed Asphalt Pavement (RAP) aggregates. The semi-circular bend test is used to assess the cracking potential of one conventional and fourteen modified asphalt mixtures with varying RAP and WMA-additive proportions. WMA additives improved the workability of both conventional and RAP-incorporated asphalt pavement mixtures. Furthermore, the inclusion of additives improved the cracking resistance of low-proportion RAP mixtures. Additionally, it decreases strain energy regardless of the amount of RAP present, demonstrating the capacity of additive to lower stiffness and enhance the workability of RAP mixtures. The current study's objective is to evaluate the workability and crack resistance of asphalt mixes that incorporate WMA (organic and chemical) additives along with RAP aggregates. This information will undoubtedly aid future researchers and field engineers in designing WMA mixes that contain a high percentage of RAP aggregates and organic and chemical additives in an appropriate way. [ABSTRACT FROM AUTHOR]

As railway engineering moves toward a more severe environment, the service safety of track structure infrastructure needs more detailed control, and correct cognitive mechanical behavior of infrastructure has always been the difficulty and bottleneck of refined analysis of railway engineering. In this thesis, taking the stress–sensitive nonlinear constitutive of subgrade as a breakthrough, this thesis puts forward that the constitutive relationship that is self-consistent with material behavior is embedded in the finite element calculation model of subgrade, which provides a feasible method for fine analysis of railway engineering. In the study, the bottom layer of the subgrade bed for the Ballastless track is taken as the research object. The stress-related nonlinear constitutive relationship N37A is inserted into the subgrade material through a user-defined subroutine. Then, the material’s rebound modulus evolution parameters returned from the triaxial test are used to analyze the nonlinear base mechanical behavior of the embedded material. The following conclusions are obtained: (1) The N37A nonlinear model can capture the stress sensitivity of the material, and describe the mechanical behavior of the bottom layer of subgrade bed factually. (2) The deadweight has led to the nonlinear significant increase of the resilient modulus, and the train load causes the different development of the stress–strain response at different locations. (3) The resilient modulus of the bottom layer of the subgrade bed is small at the top surface but large at the bottom surface, which is monotonically increasing. With the loading of the train, the resilient modulus curve with different depths rotates approximately clockwise at the center about 0.75m away from the top surface of the bottom layer of the subgrade bed. (4) The value of resilient modulus at the top surface of the bottom layer of the subgrade bed is as the center line of the subgrade > under the rail > the edge of the base, and the bottom surface is just opposite. The relative difference in resilient modulus between them is less than 3MPa. [ABSTRACT FROM AUTHOR]

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. [ABSTRACT FROM AUTHOR]

This article presents the results of laboratory tests conducted to identify the granulometric stabilization and chemical improvement techniques used in an experimental segment of the unpaved BR-030 highway in the Maraú Peninsula, Bahia. The segment was designed to evaluate the performance of primary coating sections stabilized with sand, clayey gravel, reclaimed asphalt pavement (RAP), and simple graded crushed stone (GCS), as well as chemically improved with Portland cement and hydrated lime. The laboratory campaign focused on mechanical resistance, resilient modulus, and permanent deformation tests. In this research, chemical improvement with the addition of 2% Portland cement presented the most promising results for potential application in the section of the BR-030 highway intended to remain unpaved. Additionally, a life cycle assessment (LCA) revealed that mechanical stabilization of the primary coating has the lowest environmental impacts, making it a suitable and sustainable option among stabilization methods. [ABSTRACT FROM AUTHOR]

A prerequisite for the reliable design of low volume roads (LVRs) is the development of simple and reliable testing techniques to simulate the behavior of unbound granular materials (UGMs) under conditions similar to those that occur when subjected to traffic. Therefore, there is a need for a simple correlation method to characterize the mechanical behavior of UGM under the action of moving traffic loads. This paper discusses the results of the experimental study and statistical analysis on the resilient modulus (MR) characterization of UGMs through laboratory Dynamic Cone Penetration (DCP) test, this correlation is intended to be adopted in the of UGMs used in LVRs. The laboratory study was conducted on twenty-one UGMs samples to obtain index, strength, MR, and laboratory DCP-Dynamic Number (DN) parameters. The correlation has been achieved by correlating MR values compacted at 95% of Maximum Dry Density (MDD) with the laboratory DCP-DN values. The results show that MR values of UGMs significantly correlate with laboratory DCP-DN values and follow the power equation of M R = 700 × DN - 0.7 with coefficient of determination value of 0.99. Furthermore, a cyclic load triaxial test was carried out on UGMs to validate the result of the MR–DN. The MR–DN relationship estimates a reasonably resilient modulus of UGMs which can be used as an input into the mechanistic-empirical (M-E) pavement design analysis of LVRs in the absence of Repeated Load Triaxial (RLT) testing facilities. [ABSTRACT FROM AUTHOR]

This paper investigates the application of Artificial Neural Networks (ANNs) for predicting the resilient modulus (Mr) of subgrade and subbase soils, which is a critical parameter in pavement design. Utilizing a dataset of 1683 Mr observations, the ANN model incorporates eight input variables, including soil gradation, plasticity, and stress conditions. The model was optimized using a quasi-Newton method, achieving high predictive accuracy, with a coefficient of determination (R2) of 0.9613 and low error rates for both selection and testing datasets. To further enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, revealing the significant influence of specific input parameters, such as saturation ratio, plasticity index and soil gradation, on Mr predictions. This study underscores the potential of ANNs as a practical tool for estimating resilient modulus, offering a reliable alternative to conventional laboratory testing methods. The findings suggest that integrating ANNs into pavement design processes can lead to more accurate predictions of pavement performance, ultimately supporting the development of more efficient and durable road structures. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to develop a new in situ nondestructive testing (NDT) method to determine nonlinear parameters of subgrade soils and to establish a method to calculate the subgrade design modulus. The new method collects nonlinear characteristics of the subgrade by in situ test with various combinations of dynamic stress and static stress and back-calculates subgrade nonlinear parameters by machine learning algorithm. The highlight of this study is to solve the problem that the results of different test methods are not unique, and the stress-dependence characteristics of the subgrade are fully considered. The main work is as follows: (1) develop a new resilient modulus prediction model considering dynamic and static effects; (2) propose a novel in situ NDT method based on loading sequences; (3) establish a fuzzy back-propagation neural network (BPNN) algorithm to compute subgrade nonlinear parameters; and (4) propose a calculation method of subgrade design modulus based on the principle of deflection equivalence and establish an empirical equation of subgrade design modulus. The results showed that the contact stress has an enhancing effect on resilient modulus of subgrade soils. The new in situ NDT method has been proved feasible in theory and can accurately obtain the nonlinear characteristics of the subgrade. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The resilient modulus (MR) and the backcalculated modulus from the FWD testing (EFWD) of the unbound layers are critical inputs in the analysis/design of pavements. Several studies have tried to develop a conversion factor between these two parameters, while the nonlinear stress dependency of unbound materials and the pavement strain response are mostly missing from the literature. This study aims to compare the laboratory-measured MR of recycled aggregate base (RAB) materials and a virgin aggregate base using field-based EFWD and tries to establish pavement's responses to loading using vertical strains from both the MR and EFWD values of the respective materials as comparability parameters between the two. For this purpose, a control virgin aggregate (VA, limestone) and three types of RAB materials were selected to construct four test sections. The test sections were modeled in layered elastic- and finite-element-based pavement response models to calculate the vertical strains at the mid-depth of the base and top of the subgrade layers. A comparison of the lab-calculated vertical strains using MR with actual vertical strains in the field from EFWD showed that there was no relationship between the two stiffness parameters in all tested RABs. The vertical strains, based on the lab MR, undermined the stiffness of the recycled aggregates in the field. In contrast, the values of EFWD based on the vertical strains remained close to the MR strains of limestone (VA) throughout the testing period, establishing an EFWD vs. MR relationship (MR = 0.87 EFWD). The results also show that fine RCA was a better-performing material over three years. This research not only explores how the hydration process in RABs limits the development of MR-EFWD correlations but also underscores the need to consider real-world conditions when assessing their performance. [ABSTRACT FROM AUTHOR]

Abstract The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σ d ), and confining stress (σ 3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σ d parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.

The resilient modulus (MR) of subgrade soil is crucial in pavement design, as it significantly affects its structural performance. However, Traditional methods, aimed at estimating this parameter, are characterized by inefficiency, time consumption, and high costs. This study introduces a novel alternative model using ten advanced machine-learning techniques including Deep Neural Network (DNN), Extreme Learning Machine (ELM), Support Vector Regression (SVR), LASSO regression (LASSO), Random Forest (RF), Ridge Regression (Ridge), Partial Least Square Regression (PLSR), Stepwise Regression (Stepwise), Kernel Ridge (KRidge), and Least Square Regression (LSR), to predict the resilient modulus (MR). The model is trained on a comprehensive dataset comprising 891 repeated load triaxial tests, and it considers nine pertinent factors as input parameters based on literature suggestions. Evaluating the efficacy of the machine-learning methods reveals that the Deep Neural Network (DNN) model outperforms others in accuracy. Subsequently, a user-friendly graphical interface called "ResiMod2024" based on the DNN model is developed to streamline the estimation process of resilient modulus, offering significant time and cost savings for researchers and civil engineers. [ABSTRACT FROM AUTHOR]

This study focuses on assessing the resilient characteristics of a clayey soil modified with a fly ash (FA)-based geopolymer and reclaimed asphalt pavement (RAP) as an unpaved road material. RAP-geopolymer-soil mixtures were designed using the response surface methodology-central composite design with 0–40% RAP and 0–25% FA. The repeated-load California bearing ratio (CBR) testing method was used to determine the recoverable and permanent deformations and then obtain the equivalent (resilient) modulus (Mequ). The Mequ values were used to develop predictive models and determine the optimum soil–RAP–geopolymer mixture. The effects of the load level and soaking period on the stiffness of the optimum mixture were also investigated. The results revealed that the geopolymer binder played a significant role in enhancing the stiffness of the mixtures, with the maximum Mequ obtained at 25% FA and 0% RAP. However, it was determined that RAP has an adverse effect on the stiffness for almost all the studied cases and more significantly for the 40% RAP and 0% FA mixture. The optimal mixture was found to be 25% FA and 30% RAP. The developed model exhibited excellent predictive capability based on ANOVA results. The optimum mixture exhibited stress-softening behavior at an increased load level. No clear trend was observed in the effect of the soaking period on the resilient modulus within the examined soaking period range. Overall, this study agrees with several pavement design guidelines to limit the RAP content used in road applications owing to uncertain adequacy. Additionally, it suggests that the geopolymer binder is an effective stabilizer with excellent environmental and economic potential.

Abstract Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining MR are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that the GEP model consistently outperforms MEP and ANN models, demonstrating the lowest error metrics and highest correlation indices (R2). During training, the GEP model achieved an R2 value of 0.996, surpassing the MEP (R2 = 0.97) and ANN (R2 = 0.95) models. Sensitivity and SHAP (SHapley Additive exPlanations) analysis were also performed to gain insights into input parameter significance. Sensitivity analysis revealed that confining stress (21.6%) and dry density (26.89%) are the most influential parameters in predicting MR. SHAP analysis corroborated these findings, highlighting the critical impact of these parameters on model predictions. This study underscores the reliability of GEP as a robust tool for precise MR prediction in subgrade soil applications, providing valuable insights into model performance and parameter significance across various machine-learning (ML) approaches.

This study focuses on assessing the resilient characteristics of a clayey soil modified with a fly ash (FA)-based geopolymer and reclaimed asphalt pavement (RAP) as an unpaved road material. RAP-geopolymer-soil mixtures were designed using the response surface methodology-central composite design with 0–40% RAP and 0–25% FA. The repeated-load California bearing ratio (CBR) testing method was used to determine the recoverable and permanent deformations and then obtain the equivalent (resilient) modulus (M equ). The M equ values were used to develop predictive models and determine the optimum soil–RAP–geopolymer mixture. The effects of the load level and soaking period on the stiffness of the optimum mixture were also investigated. The results revealed that the geopolymer binder played a significant role in enhancing the stiffness of the mixtures, with the maximum M equ obtained at 25% FA and 0% RAP. However, it was determined that RAP has an adverse effect on the stiffness for almost all the studied cases and more significantly for the 40% RAP and 0% FA mixture. The optimal mixture was found to be 25% FA and 30% RAP. The developed model exhibited excellent predictive capability based on ANOVA results. The optimum mixture exhibited stress-softening behavior at an increased load level. No clear trend was observed in the effect of the soaking period on the resilient modulus within the examined soaking period range. Overall, this study agrees with several pavement design guidelines to limit the RAP content used in road applications owing to uncertain adequacy. Additionally, it suggests that the geopolymer binder is an effective stabilizer with excellent environmental and economic potential. [ABSTRACT FROM AUTHOR]

Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining MR are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that the GEP model consistently outperforms MEP and ANN models, demonstrating the lowest error metrics and highest correlation indices (R2). During training, the GEP model achieved an R2 value of 0.996, surpassing the MEP (R2 = 0.97) and ANN (R2 = 0.95) models. Sensitivity and SHAP (SHapley Additive exPlanations) analysis were also performed to gain insights into input parameter significance. Sensitivity analysis revealed that confining stress (21.6%) and dry density (26.89%) are the most influential parameters in predicting MR. SHAP analysis corroborated these findings, highlighting the critical impact of these parameters on model predictions. This study underscores the reliability of GEP as a robust tool for precise MR prediction in subgrade soil applications, providing valuable insights into model performance and parameter significance across various machine-learning (ML) approaches. [ABSTRACT FROM AUTHOR]

To evaluate the regularity of resilient modulus for hot-mix asphalt (HMA) under large temperature fluctuations, back propagation (BP) neural network technology was used to analyze the continuous change of HMA resilient modulus. Firstly, based on the abundant data, the training model of HMA resilient modulus was established by using BP neural network technology. Subsequently, BP neural network prediction and regression analysis were performed, and the prediction model of HMA resilient modulus at different temperatures (50 ℃ to 60 ℃) was obtained, which fully considered multi-factor and nonlinearity. Finally, the fitted theoretical model can be used to evaluate the HMA performance under the condition of large temperature fluctuations, and the rationality of theoretical model was verified by taking Harbin region as an example. It was found that the relationship between HMA resilient modulus and temperatures can be described by inverse tangent function. And the key parameters of theoretical model can be used to evaluate the continuous change characteristics of HMA resilient modulus with large temperature fluctuations. The results can further improve the HMA performance evaluation system and have certain theoretical value. [ABSTRACT FROM AUTHOR]

The stable and safe operation of highway/railway lines is largely dependent on the dynamic behavior of subgrade fillings. Clay soils are widely used in subgrade construction and are compacted at different remolding water contents and compaction degrees, depending on the field conditions. As a result, their dynamic behaviors may vary, which have not been fully investigated until now. To clarify this aspect, a series of cyclic triaxial tests were carried out in this study with three typical remolding water contents (w = 19%, 24%, and 29%), corresponding to the optimum water content as well as its dry and wet sides, and two compaction degrees (Dc = 0.8 and 0.9), which were selected according to the field-testing data. Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) tests were also conducted on typical samples to investigate the corresponding soil fabric variations. The findings indicate the following: (a) The soil fabric at the optimum remolding water content and its dry side was characterized by a clay aggregate assembly with a bimodal pore size distribution. In contrast, the soil fabric on the wet side of the optimum water content consisted of dispersed clay particles with a unimodal pore size distribution. (b) As the compaction degree increased, to ensure the optimum water content and its dry side, large pores were compressed to make them smaller, while small pores remained unchanged. Comparatively, all the pores on the wet side were compressed to make them smaller. (c) For each compaction degree, as the remolding water content increased, a non-monotonic changing pattern was identified for both the permanent strain and resilient modulus; the permanent strain first decreased and then increased, while, for the resilient modulus, an initial increasing trend and then a decreasing trend were identified. In addition, a larger changing rate of the permanent strain (resilient modulus) was observed on the dry side, indicating a larger effect of the remolding water content. (d) For each remolding water content, as the compaction degree increased, the permanent strain exhibited a decreasing trend, but an increasing trend was identified for the resilient modulus. Moreover, the rate of change in the permanent strain (resilient modulus) on the dry side of the optimum water content was larger than that on the wet side. In contrast, the minimum rate of change was identified at the optimum water content. The obtained results allowed for the effects of the remolding water content and compaction degree on the dynamic behavior to be analyzed, and they helped guide the construction and maintenance of the subgrade. [ABSTRACT FROM AUTHOR]

Red mud and bottom ash are two industrial wastes generated in huge quantities by the aluminum industry and coal-based power plants, worldwide. The high alkaline nature and high specific gravity of red mud are the major hindrances in utilizing as constructional geomaterial. The manuscript aims to utilize these two wastes as subgrade material in pavement by mixing different proportions of red mud with bottom ash. The physical behavior i.e., specific gravity, grain size pattern, and chemical characterization i.e., pH of all the mixes were assessed. Geotechnical properties such as compaction, compression, strength, and permeability tests were conducted to investigate the efficiency of different mixes as suitable geomaterial. The California bearing ratio, cyclic triaxial test under different confining pressure, resilient modulus, and nonactivity of clays by methylene blue test results provide the proficiency of different mixes in utilizing as subgrade material. The highest strength, high CBR, and resilient modulus values suggest that the mixture of 70% HRM with 30% HBA is suitable and strength gain upon drying suggests that it can be used as a structural subgrade material. Further, a leachate analysis of all the mixes concluded that the chemicals are within permissible limits and hence not hazardous to the ecosystem. [ABSTRACT FROM AUTHOR]

Jordan's road network continues to deteriorate as a consequence of the continuous increase in traffic and the absence of adequate maintenance work. The primary objective of this study was to enhance HMA performance by using polymer-modified asphalt mixtures. The polymer modifier known commercially as Eastman (EE-2) was mixed with binder penetration grade (60–70) at a ratio of 12%. In order to investigate the performance of polymer, the dynamic creep, the resilient modulus and the stability-flow tests were performed. Marshall Mix design was utilized to prepare a total of 50 samples, of which 20 were used to determine the optimal binder content, and the remaining samples were used to determine the effect of EE-2 modifier on asphalt mixtures. The results showed that the optimal asphalt content was 4.57 percent and revealed that the addition of EE-2 polymer to asphalt cement contributed to the production of a variety of desirable properties. The most important indicator of these developments is increased rutting resistance. For instance, the total permanent deformation has decreased by 87% (8500 to 1000 μm). Conversely, addition of EE-2 has resulted in a threefold increase in the resilience modulus (from 3632 to 10590 MPa). Finally, effect of the EE-2 polymer on the stability was demonstrated by increasing the stability value by about 52%

Cold-mix asphalt is a greener alternative to pavement construction, processed at 10–40 °C, which is typically lower than other techniques like warm-mix asphalt and hot-mix asphalt. Huge amounts of construction and demolition waste, such as broken bricks, recycled concrete aggregates, reclaimed asphalt pavement, ceramic waste, etc., are generated every year due to the acceleration in infrastructure development. The production of such massive amounts causes landfilling issues, and their disposal is a serious issue nowadays. This study examines the effect of binary blended fillers on the performance of cold asphalt mixes using emulsified binders and 50% reclaimed asphalt pavement materials. Moreover, three types of binary blended fillers (BBFs), cement, fly ash, and Stabil Road, were used at different dosages. Overall, 500 samples were prepared for the mix design, and the optimum emulsion content was determined as 11% and 9% for the CM and 50R mixes, respectively, based on the Marshall stability peak value and volumetric properties such as voids in the mineral aggregates, total voids, and dry density. The moisture susceptibility of the recycled cold-mix asphalt (RCMA) mixture was evaluated using the tensile strength ratio. Cantabro abrasion loss was used to assess the cohesion resistance of the mixtures. The dynamic response of the mixes to the applied load was evaluated using the resilient modulus. The results of the present study reveal that using BBFs in the RCMA improved the inter-particle bonding and strength. Furthermore, BBF incorporation enhanced the performance of the recycled cold-mix asphalt. [ABSTRACT FROM AUTHOR]

Resilient modulus (MR) serves as a fundamental material property utilized for characterizing unbound pavement materials in pavement design. While the repeated load triaxial test (RLT) is the standard method to determine MR , it can be impractical due to the required test facilities and expertise. As a result, various constitutive models have been proposed to predict MR of soil based on the stress state and soil properties. This study evaluates the performance of 10 such constitutive models based on their accuracy in representing the relationship between MR and stress conditions for RLT test data taken from the long-term pavement performance (LTPP) database. The 10 constitutive models consisted of models recommended by various researchers and institutions, including the one proposed by the National Cooperative Highway Research Program (NCHRP) and adopted by the Mechanistic-Empirical Pavement Design Guide (MEPDG). A multivariable nonlinear curve fitting technique was used to fit the data to the models and obtain model coefficients. The performance of these models was then measured by using the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE) values as metrics. The results show that the models proposed by NCHRP, Puppala as well as Ni performed best, with Puppala's model showing slight superiority. On the other hand, the models proposed by Rahim and George, Kim, and Moossazadeh and Witczak produced poor results. The results also indicate that models having the general form of NCHRP and expressed in terms of confining pressure and deviator stress perform better. The findings of this study will provide insights into the reliability of existing models and considerations that should be made for developing better alternative models in the future. [ABSTRACT FROM AUTHOR]

Many waste materials can be repurposed effectively within asphalt concrete to enhance the performance and sustainability of pavement. One of these waste materials is sawdust ash (SDA). This study explores the beneficial use of SDA as a substitute for limestone dust (LD) mineral filler in asphalt concrete. The replacement rate was 0%, 15%, 30%, 45%, and 60% by weight of total mineral filler. Scanning electron microscopy (SEM) was employed to assess the surface morphology of Sawdust (SD), SDA, and LD. In addition, a series of tests, including Marshall stability and flow, indirect tensile strength, moisture susceptibility, and repeated uniaxial loading tests, were conducted to examine the performance characteristics of asphalt mixtures of different SDA content. As per Marshall mix design, a slightly higher binder content was required for the preparation of SDA mixes. The results reveal that the asphalt mixtures prepared using SDA attain a tensile strength ratio (TSR) greater than the critical threshold, i.e., 80%, indicating the feasibility of SDA against moisture-induced damage. The highest TSR value of 87% was obtained using 45% SDA as a replacement for LD. Compared to 0% SDA, there is a reduction of 12.08% in permanent deformation for asphalt mixtures produced with 60% SDA. Also, as the SDA content increases, there is a slight improvement in the resilient modulus values, with a peak improvement of 3% at 60% SDA. In addition, the cost of producing SDA mixes was relatively lower than the control mixes, indicating the cost-effectiveness of using SDA. Overall, the study found that SDA is a promising material that can improve the performance and durability of asphalt concrete at lower production costs. [ABSTRACT FROM AUTHOR]

Since the resilient modulus is changed by suction and moisture conditions in subgrade soils, these effects should be considered in the model to rationally predict the modulus. The suction stress is one of the possible stress parameters for considering the influence of the moisture condition and can be taken into account in the resilient modulus. In this study, the average skeleton stress was proposed and the suction stress was applied to the model on subgrades. The predicted resilient modulus was compared with experimental data to evaluate the suitability of the established models. It was found that the suction stress has an independent effect on the resilient modulus and reasonably estimates the resilient modulus on subgrade soils in an unsaturated state. [ABSTRACT FROM AUTHOR]

This study investigated the use of recycled glass aggregate (RG) as unbound base/subbase material. The experimental tests included compaction, Los Angeles, Micro-Deval, and California bearing ratio (CBR) tests. In this regard, a fine proportion of course limestone aggregate (MG20) was replaced by RG with size ranging from 0 to 5 mm based on the volumetric method. Adding RG to the coarse aggregate improved durability in wet conditions, which is more representative of the field condition of Canada/Quebec, but decreased in dry conditions. CBR values decreased with increasing RG inclusion, but all blends with 0%–100% RG in the fine fraction of MG20 met minimum requirements for unbound granular layers in Quebec. A simple model predicting the resilient modulus values of these materials based on CBR values at different stress levels was suggested. This equation estimates Mr values of various aggregate-RG blends under a wide range of mean stresses based on their CBR values. [ABSTRACT FROM AUTHOR]

Stiffness quantification of unbound granular materials (UGMs) is essential for designing pavement structures. This stiffness can be determined directly through the resilient modulus test, or indirectly, by correlation, with the California bearing ratio (CBR) test. Alternatively, the dynamic CBR test (dCBR) has been proposed as a simple and low-cost method for this assessment. This study investigated a new dCBR test protocol based on a novel strain-stage approach, applying 20 load cycles/stage. This testing protocol is expected to facilitate characterization and on-site control of stiffness for UGMs, including marginal granular materials (MGMs). The study characterized four MGMs using the new dCBR test protocol and the resilient modulus test; four replicate specimens manufactured by gyratory compaction were assessed per material and test. The protocol was implemented successfully for the MGMs studied to compute an equivalent resilient modulus. A methodology is proposed for calculating an adjusted equivalent resilient modulus with values similar to those of the resilient modulus. Further research is suggested to explore the relationship between the dCBR and CBR tests to advance the characterization of granular materials for pavement structures and expand the experimental data using a broader set of soils to validate the protocol. [ABSTRACT FROM AUTHOR]

The mechanistic-empirical pavement design guide (M-EPDG) recommends the use of resilient modulus (MR) for characterization of subgrade soils. Subgrade soils may not always have enough strength and stiffness to support the pavement structure. Therefore, a certain type of soil improvement method using cement, lime, or other stabilization techniques is often needed to enhance the strength and stiffness properties of weak subgrade soils. The cement-stabilized soils show brittle behavior under compression loading, which can induce cracking in overlying pavement layers. In general, polymer-treated soils show a semiductile or ductile behavior. It is important to look for combined cement and polymer treatments to address brittle behavior issues as well as moisture susceptibility while maintaining strength and moduli properties. A research study was conducted to understand the strength, resilient, and ductile behaviors of sandy soils treated with cement and a combination of cement and vinyl acetate ethylene (VAE) copolymer. Engineering tests such as unconfined compressive strength (UCS) and resilient modulus tests were conducted on both control and treated soil specimens cured for 7 days. Tests were conducted on specimens before and after immersing in water bath for 4 h to investigate the moisture susceptibility. In these tests, an increase in UCS was observed after cement and cement-VAE treatments as compared to control soil specimens. Results showed that cement-VAE-treated soils exhibited an increase in the axial strain at failure, indicating the semiductile behavior compared to cement-treated specimens. An improvement in the resilient moduli was observed after treatments. Subsequently, two of three-parameter models were used to analyze resilient modulus formulations with stress conditions and determined the regression constants. In conclusion, the study revealed that the use of VAE copolymer improved the stress-strain responses of cement-treated soils and imparted closer to the semiductile behavior, which will reduce cracking in overlying pavement structures. [ABSTRACT FROM AUTHOR]