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<p dir="ltr">Resilient Modulus (M_R) is a fundamental parameter in the Mechanistic-Empirical Pavement Design Guide (MEPDG) that characterizes the stiffness of subgrade soils under repeated traffic loads. Traditionally, M_R determination involves direct laboratory testing, which can be labor-intensive, costly, and impractical for large-scale pavement projects or rehabilitation efforts. To address these challenges, the current research has explored non-destructive testing methods, such as Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR), as well as the use of predictive models to estimate M_R based on soil properties. This study aimed to enhance the understanding of M_R testing and improve predictive models, contributing to more reliable and efficient M_R estimation techniques.</p><p dir="ltr">The research involved an extensive experimental program, which began with the collection of subgrade soil samples from various road construction projects across Indiana. The collected soils were characterized through standard geotechnical tests, including gradation analysis, Atterberg limits, and compaction tests. Resilient modulus testing followed the AASHTO T 307 protocol, performed on both untreated and treated soil samples to simulate field conditions. Post-construction, the test sites were revisited to conduct FWD and GPR tests, ensuring a comprehensive dataset for correlating M_R with field test results. The use of GPR for pavement thickness estimation proved effective in identifying discrepancies between as-built and design thicknesses in both flexible and rigid pavements. For flexible pavements, a strong correlation was observed between laboratory M_R values and FWD backcalculated moduli, indicating that FWD testing can reliably estimate M_R for untreated subgrade soils.</p><p dir="ltr">The study also explored the use of machine learning algorithms, such as random forest and gradient boosting, to predict M_R based on soil properties, offering an alternative to traditional regression analysis. The research found that stress-independent models failed to yield statistically significant correlations between M_R and basic soil properties such as moisture content, dry density, and Atterberg limits. In contrast, stress-dependent models, particularly the Uzan and octahedral models, revealed weak dependencies on confinement and deviatoric stresses, leading to significant variability in M_R values across tested samples. The results highlight the limitations of current soil- and stress-based models, suggesting that while they may work well for specific cases, they cannot be generalized across a wide range of conditions.</p><p dir="ltr">An effort to compare soil performance during the different stages of resilient modulus testing and a numerical method that included a stress-dependent soil model confirmed the empirical finding of a weak dependency between M_Rand confinement and deviatoric stress. This was the case not only for the standard AASHTO T 307 protocol, but also for other protocols where the loading sequence was reversed compared to the standard test.</p><p dir="ltr">The research demonstrated the potential of machine learning for M_R prediction and the complexity of the soil behavior during resilient modulus testing. Thus, models to accurately predict M_R results should be able to follow the stress path that the soil is subjected to during the test.</p>

abstract: Chloride solutions have historically been used to stabilize roads and to prevent dust; however, very little work has been done on investigating the soil stabilizing benefits from interactions between salt solutions and different soil types. The primary goal of this research was to analyze the feasibility of utilizing a salt waste product as an economically and environmentally responsible means of dust control and/or soil stabilization. Specifically, this study documents an investigation leading to the understanding of how the addition of saline based waste products, when using a soil stabilizer, modifies the strength behavior of soils. The scope of work included the evaluation of current literature, examination of the main challenges meeting relevant governmental regulations, and exploring the possibility of using saline waste to improve roadways. Three soils were selected, treated with varying amounts of salt (calcium chloride, CaCl2), and tests included soil composition and classification, correlation of soil characteristics and salt, and obtaining strength parameters that are typically used in pavement design and analysis. The work effort also included the determination of the optimum dosage of salt concentration for each soil. Because Lime treatment is also commonly used in soil stabilization, one of the soils in this study included a treatment with Lime for comparison purposes. Results revealed that when salt concentration was increased, a decrease in the plasticity index was observed in all soils. A modest to considerable strength gain of the treated material was also observed for two of the soils; however, a strength loss was observed for the third soil, which was attributed to its low clay content. When comparing the soil corrosive potential, the additional salt treatment showed promise for increasing strength, to an extent; however, it changes the chemical properties of the soil. The soils prior to treatment were corrosive, which could be managed with appropriate techniques, but the salt increases the values to levels that could be potentially cost prohibitive if salt was used by itself to treat the soil. The pavement design and performance investigation revealed that the Vineyard soil treated at 16% CaCl2 had an improvement that is comparable to the Lime treatment. On the other hand, the Eager soil showed very little pavement performance improvement at 8% CaCl2; this goes back to the effect of acid on the clay mineralogy. It was also postulated that using salt by-products to stabilize highway shoulders could be beneficial and save a lot of maintenance money when it comes to cleaning unwanted vegetation. A salt saturated soil structure could help in dust control as well. Future environmental challenges for salt leaching that could affect agriculture in developing countries will still need to be carefully considered. The chlorine levels in the soil would increase, and if not treated, can potentially have corrosive effects on buried structures. Future research is recommended in this area and to also evaluate soil stabilizing properties of varying proportions of Lime and salt using the approach provided in this study. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2017

Laboratory characterization of subgrade soils and unbound granular materials is an essential component of the Mechanistic-Empirical Pavement Design Guide (Pavement ME). The design thickness and performance of a pavement structure are highly dependent on the deformation behaviour of subgrade and granular material. Specifications for granular materials vary among transportation agencies based on the availability of materials, climatic conditions, and function. Specifications aim to provide durable materials that meet design requirements and achieve the target design life with cost effective materials. The objectives of the research are to: • evaluate resilient modulus of typical fine-grained soils under traffic loading. • evaluate resilient modulus, permanent deformation, and permeability of typical unbound granular materials. • evaluate the effect of moisture and fines fraction on the performance of unbound granular materials and subgrade soil. • develop prediction models for resilient modulus to improve reliability of Level 2 inputs in the Pavement ME. • provide test data in support of updating Manitoba Infrastructure and Transportation specifications for unbound granular materials to improve the performance of pavement structures. Resilient modulus tests were conducted on three types of subgrade soil (high plastic clay, sandy clay, and silty sand/sandy silt) at four levels of moisture content. Resilient modulus, permanent deformation and permeability tests were conducted on six gradations representing two types of granular material (100% crushed limestone and gravel) at two levels of moisture content. Prediction models were developed for resilient modulus and compared to the models developed under the Long Term Pavement Performance program. The proposed models provided more reliable predictions with lower root mean square error. The deformation behaviour of the granular materials was classified according to the shakedown and dissipated energy approaches. Among the tested fines contents, limestone and gravel materials with optimum fines contents of 4.5% and 9%, respectively, had better resistance to plastic deformation and higher resilient modulus. The dissipated energy approach can be used to determine the stress ratio for the boundary between post compaction and stable zones from multistage triaxial testing. Result of permeability tests showed that the hydraulic conductivity of unbound granular material increased as the fines content decreased. / February 2016

Railway tracks experience elevated rail deflections when the supporting soil is soft and/or the train speed is greater than approximately 50% of the wave propagation velocity in the track-soil system (i.e. the critical velocity). Such vibrations are undesirable, so soil replacement or soil improvement of the natural soil (or alternatively mini-piles or lime-cement treatment) is often used to increase track-ground stiffness prior to line construction. Although areas of existing soft subgrade might be easily identified on a potential new rail route, it is challenging to determine the type and depth of ground remediation required. Therefore, major cost savings can be made by optimising ground replacement/improvement strategies. This paper presents a numerical railway model, designed for the dynamic analysis of track-ground vibrations induced by high speed rail lines. The model simulates the ground using a thin-layer finite element formulation capable of calculating 3D stresses and strains within the soil during train vehicle passage. The railroad track is modelled using a multi-layered formulation which permits wave propagation in the longitudinal direction, and is coupled with the soil model in the frequency-wavenumber domain. The model is validated using a combination of experimental railway field data, published numerical data and a commercial finite element package. It is shown to predict track and ground behaviour accurately for a range of train speeds. The railway simulation model is computationally efficient and able to quickly assess dynamic, multi-layered soil response in the presence of ballast and slab track structures. Therefore it is well-suited to analysing the effect of different soil replacement strategies on dynamic track behaviour, which is particularly important when close to critical speed. To show this, three soil-embankment examples are used to compare the effect of different combinations of stiffness improvement (stiffness magnitude and remediation depths up to 5 m) on track behaviour. It is found that improvement strategies must be carefully chosen depending upon the track type and existing subgrade layering configuration. Under certain circumstances, soil improvement can have a negligible effect, or possibly even result in elevated track vibration, which may increase long-term settlement. However, large benefits are possible, and if detailed analysis is performed, it is possible to minimise soil improvement depth with respect to construction cost.