Abstract The conventional design and quality-control practices of lime-stabilized soils rely primarily on the pH of the mix and empirical, arbitrary limits on strength, which provide limited insight into the early-age physicochemical mechanisms governing stabilization. This study aims to evaluate the capability of electrochemical impedance spectroscopy (EIS) to differentiate the effects of varying lime contents on two fundamentally distinct clayey soils containing kaolinite and montmorillonite by mechanistically investigating lime–clay interactions in pure kaolinite, pure montmorillonite, and controlled kaolinite–montmorillonite mixtures, thereby isolating mineral-specific responses that are normally difficult to discern directly. Cylindrical specimens with lime dosages ranging from 0% to 10% were prepared at their optimum moisture contents and tested using EIS immediately after compaction and after 2 and 24 h of curing. Complementary X-ray fluorescence (XRF), X-ray diffraction (XRD), and pH measurements link electrochemical behavior with chemical and mineralogical evolution. Kaolinite-rich systems exhibited rapid electrochemical stiffening, with maximum development at approximately 6% lime, above which effectiveness decreased. In contrast, montmorillonite-rich systems exhibited delayed but sustained impedance growth, reaching peak response only at the highest lime content (∼10%), due to higher cation exchange capacity and buffering capacity and slower reaction kinetics. Mixtures of different proportions of kaolinite and montmorillonite displayed intermediate impedance evolution governed by mineral proportions. Across all mixtures, electric impedance followed a characteristic temporal pattern comprising an initial decrease associated with increased ionic conductivity from lime dissolution, followed by a progressive increase as cementitious reaction products formed and pore connectivity decreased. Strong correlations among impedance evolution, Ca/(Si + Al) ratios, pH, and XRD-derived structural changes are an indication of EIS as a sensitive, nondestructive tool for identifying early-age stabilization mechanisms and optimal lime dosage.
Abstract The utilization of reclaimed asphalt pavement (RAP) is often limited by challenges like particle agglomeration and variability, which necessitate advanced processing methods such as refined decomposition (RD). This technique enables better separation and recovery of aged binder-rich materials, which facilitates the high-value and high-content utilization of RAP. However, achieving balanced performance requires careful optimization of this recycled fraction. To address this, the present study proposes a kinetics-based approach to determine the optimum content of this recycled material for balanced performance. The approach employs RD-processed RAP fractions (0–3 mm) for fine aggregate matrix (FAM) characterization. This characterization calculates the activation energies of key distresses: moisture damage ( E S ), permanent deformation ( E P ), fatigue cracking ( E a c ), and oxidative aging ( E a c r ). The optimal recycled material (ORM) content is identified at the intersection of these activation energy curves, balancing critical performance factors. The framework was validated using different binder types [Pen-70, styrene-butadiene-styrene (SBS)-modified] and gradations (AC-13, SUP-20), confirming its versatility and potential to improve the design. The results show that both aggregate gradation and binder type are crucial in determining the ORM content. The SBS-modified binders and finer aggregate gradations (AC-13) enhance the overall performance and allow a higher ORM content of 38%. Meanwhile, SUP-20 with SBS-modified and AC-13 with Pen-70 binder exhibit ORM content of 30% and 20%, respectively. This approach provides a robust framework for optimizing the use of aged binder-rich recycled materials while maintaining a balanced performance.
Abstract This study investigates the mechanical performance of slag placed over subgrade soil, with a focus on the reinforcing role of woven geotextiles under cyclic loading. To achieve this, two complementary experimental approaches were employed: large-scale box tests simulating repeated traffic loads with a peak load of 20 kN applied over 100,000 cycles and resilient modulus ( M R ) tests conducted in accordance with AASHTO T 307. Results showed that the inclusion of a woven geotextile layer substantially enhanced both the permanent deformation and resilient behavior of the combined slag–subgrade system. In the large-scale tests, geotextile reinforcement reduced permanent deformation beneath the loading plate by 70.5%, compared to the unreinforced case. Similar improvements were observed around the loading plate, indicating enhanced lateral confinement. Stress–strain responses confirmed stabilized behavior in the reinforced sections, free from peak stress surges and instability. Furthermore, resilient modulus results demonstrated a consistent increase in M R values across all stress stages, confirming improved recoverability and stiffness under repeated loading. Overall, the combined findings reveal that the utilized geotextile reinforcement effectively suppresses plastic deformation while improving the resilient performance of granular layers, underscoring its potential as a structurally reliable solution for pavement base systems incorporating slag.
Abstract Accurate prediction of the International Roughness Index (IRI), a longitudinal roughness indicator derived from pavement profile measurements, is central to pavement management systems (PMS), yet current approaches face two gaps: (1) most machine-learning (ML) models trained on long-term pavement performance (LTPP) data act as static predictors and cannot update recursively as new measurements arrive, and (2) mechanistic–empirical (ME) models remain sensitive to calibration and struggle to accommodate sparse, uncertain and irregular monitoring. This study addresses these gaps by developing a hybrid state space–artificial intelligence (AI) digital twin (DT) for asphalt pavement longitudinal roughness using LTPP data. IRI is modeled as a latent state in a stochastic state-space system and updated sequentially using a Kalman filter (KF), and deterioration drift is learned as the annual longitudinal roughness increment from climate, traffic, structural data, distress, age, and lagged longitudinal roughness (i.e., prior-year IRI) using a tuned random forest (RF). Under the Strategic Highway Research Program (SHRP) section-ID-based holdout split (i.e., a rigorous data splitting, analysis, and validation technique used to analyze roadway databases), the proposed DT achieved the strongest performance among benchmark ML models [ R 2 = 0.81 , root-mean square error ( RMSE ) = 0.23 m / km , mean absolute error ( MAE ) = 0.14 m / km , and symmetric (sMAPE) = 10.83%] with negligible bias [mean bias error (MBE) = 0.04], outperforming static RF, multilayer perceptron (MLP), Ridge, and long short-term memory (LSTM) baselines. Residual diagnostics showed sequential updating stabilizes predictions despite uneven inspection intervals and missing years, with errors centered around zero and no systematic pattern. Shapley additive explanation (SHAP) indicated mechanistically reasonable drivers of annual IRI growth, dominated by transverse and surface cracking, followed by initial IRI, and traffic loading, with higher asphalt and base/subbase thickness reducing longitudinal roughness increment (ΔIRI). Section-level tracking yielded bounds that widen during observation gaps and tighten with denser monitoring, enabling threshold assessment for risk-informed prioritization. The framework supports PMS through continuous state updating, uncertainty-aware forecasting, and network-scale scenario evaluation.
Abstract Styrene–butadiene rubber (SBR)-modified emulsified asphalt is widely used in microsurfacing and other preventive maintenance treatments due to its good low-temperature performance and cost-effectiveness; however, the limited high-temperature performance of the SBR-modified system, together with the inherently thin microsurfacing overlay structure, which is more sensitive to aging, results in more pronounced performance deterioration under aging conditions. This study introduces nano-silica sol as a modifier. Nano-silica sol was incorporated at dosages of 0%, 2%, 4%, 6%, and 8% by mass of binder. The high-temperature rheological behavior of the binders before and after modification was characterized by temperature sweep tests, multiple stress creep and recovery, and zero-shear viscosity measurements. On this basis, all binders were subjected to ultraviolet (UV) accelerated aging, and the effect of nano-silica sol on the antiaging resistance of SBR-emulsified asphalt was evaluated using linear amplitude sweep, Fourier transform infrared (FT-IR) spectroscopy, and fluorescence microscopy (FM). The results indicate that the incorporation of nano-silica sol increases the complex shear modulus of SBR-emulsified asphalt and reduces its temperature susceptibility and significantly improves the elastic recovery performance of the asphalt binder. In addition, nano-silica sol markedly enhances the complex viscosity of the binder, leading to improved resistance to flow deformation at high temperatures. FT-IR and FM results indicate that, compared with the UV-aged unmodified binder, the binder modified with 4% nano-silica sol shows a 17.4% decrease in the carbonyl index and a 36.7% increase in the fluorescence area fraction of the SBR phase, suggesting that nano-silica sol effectively retards the aging-induced degradation of the SBR polymer.
