Abstract The consumption of waste cooking oil (WCO) as a bitumen modifier offers a sustainable route for waste valorization, yet challenges persist concerning stability and mechanical performance at high content. This study explores the performance of asphalt mixtures containing modified waste cooking oil granulates (MWCOG), produced by dry-blending WCO with styrene-butadiene-styrene (SBS) at two ratios (45:55 and 50:50). Asphalt mixtures were prepared using MWCOG-modified bitumens at 5%, 10%, and 15% contents and assessed through Marshall stability, rutting resistance, resilient modulus, and Cantabro durability tests. The results confirmed that MWCOG addition significantly enhanced mixture performance, particularly at 10% dosage. Marshall stability increased from 13.75 kN (control) to 16.42 and 16.59 kN for 45∶55-10% and 50:50-10% mixes, respectively, indicating comparable stability performance for both blends, with the small difference falling within typical experimental variability. Cumulative rutting strain was reduced by up to 27% at 5% MWCOG, with permanent strain declines and creep modulus improvements of 35% over the control observed at 10%. Resilient modulus at 25°C increased by 16% and 12% for the 45∶55-5% and 50∶50-5% mixes, respectively. The cohesion and mass loss further confirmed the enhancement in the asphalt mixture by the Cantabro test. The results revealed a mass loss reduction of 61% and 52% for 45∶55-15% and 50∶50-10% mixes compared with the control. However, performance declined at 15% MWCOG due to excessive softening. The study confirms that MWCOG inclusion at 10% yields optimal stiffness, durability, and strength, supporting its practical perspective as a sustainable, high-performing asphalt mixture for pavement applications.
Abstract The performance of two-lift concrete pavements is significantly influenced by the strength of each lift and the degree of bonding between the two concrete lifts. Research on the influence of the bottom-lift concrete strength on the effective flexural strength of two-lift concrete pavements is scarce. The paper examines, numerically and experimentally, the influence of the strength of the bottom-lift concrete on the effective flexural strength of two-lift concrete pavements for both bottom-up and top-down cracking considerations. The current practice of design of two-lift concrete pavements considers the flexural strength of either the top or the bottom layer for estimating the bottom-up or top-down fatigue damage of the pavement, leading to an under- or over-design of the pavement. Thus, unlike existing design methods, the present approach enables selection of the effective flexural strengths of the two-layer system to produce designs to avoid the under-/over-estimation of fatigue life. Two-lift concrete beams were prepared by maintaining a time gap of 1 h between the placement of the bottom-lift and that of the top-lift to ensure a strong bond between the two layers. The grade of concrete used in the top-lift concrete (with a compressive strength of 36 MPa) was the same for all the combinations of beams investigated. The experimentally measured effective flexural strength (for bottom-up cracking case) of two-lift concrete beams increased by 33% and 63% with an increase in compressive strength of bottom-lift concrete (from 17.1 MPa to 25.4 and 32.7 MPa), respectively. The corresponding increments in the effective flexural strengths for top-down cracking were found to be 2.6% and 14.9%. The present study developed a numerical framework to evaluate the effective flexural strength of two-lift concrete by using the concrete damaged plasticity (CDP) model proposed by Lubliner/Lees/Fenves. The damage variables required in the CDP model were evaluated using the modified Alfarah method. The values of direct tensile strength and secant elastic modulus are taken as 0.9 times the split-tensile strength and static elastic modulus obtained respectively from the experiments instead of the original relations adopted in the Alfarah method. The proposed numerical framework successfully predicted the effective flexural strength of two-lift concrete beams for both bottom-up and top-down cracking cases, using the calibrated CDP parameters of both bottom- and top-lift concretes. In addition, the fatigue lives of two-lift concrete beams for both bottom-up and top-down cracking cases were estimated experimentally and numerically.
Abstract A traffic speed deflectometer (TSD) captures pavement surface deflection velocities while traveling at the speed of the traffic. Normalized deflection velocities by travel speeds, known as deflection slopes, can be used for pavement management purposes. Many of the practical approaches currently available for back-calculating pavement layers’ moduli from deflection slopes report either a linear elastic modulus or a constant complex modulus as the output for the asphalt concrete (AC) layer. This study evaluated the effect of the AC layer’s viscoelasticity on deflection slopes over a wide range of temperatures from 4°C to 37°C and travel speeds from 20 to 80 km / h using finite-element method (FEM) modeling of a three-layer flexible pavement system. Subsequently, a TSD back-calculation tool was used to back-calculate pavement layers’ moduli from deflection slopes by simplifying the AC layer’s full viscoelastic behavior to either a linear elastic modulus or a constant complex modulus. Both approaches were shown to produce errors of less than 16% across all pavement layers when back-calculation was performed at an AC temperature of 4°C and a travel speed of 20 km / h , using a conventional approach for equating the AC design modulus to its viscoelastic master curve based on travel speed. However, it was also found that simplification of AC viscoelasticity to linear elastic behavior or a constant complex modulus could lead to errors as high as 80% and 100%, respectively, in the back-calculation of the AC layer’s modulus at the high temperature of 37°C. These findings highlight the potential to correct deflection slopes to low pavement temperatures and travel speeds, thereby enabling the simplification of the AC layer’s full viscoelastic behavior while still achieving high accuracy in the back-calculation process.
Abstract Environmental conditions have a major influence on pavement design, performance, and long-term service life. In Oklahoma state, diverse and often extreme climatic conditions can greatly accelerate deterioration through cracking, rutting, moisture damage, and surface wear. To build more resilient transportation infrastructure, it is critical to integrate reliable climate projections into the mechanistic–empirical (ME) pavement design process. This study evaluates and ranks 10 of NASA’s NEX-GDDP-CMIP6 for Oklahoma under three shared socioeconomic pathways: SSP1-2.6 (low emissions/strong mitigation), SSP2-4.5 (intermediate stabilization/moderate mitigation), and SSP5-8.5 (high emissions/limited mitigation). Two complementary approaches were employed to rank model suitability. The first used traditional statistical metrics aggregated with the technique for order preference by similarity to ideal solution multi-decision-making method to assess how closely models reproduced observed temperature and precipitation. The second applied a Siamese long short-term memory neural network to evaluate temporal similarity between observed and simulated time series, capturing nonlinear patterns and seasonal dynamics that standard statistics may overlook. Together, these approaches address the limitations of using statistical metrics alone, which can be sensitive to daily variability or extreme values, by balancing accuracy with temporal consistency. Results indicated that the SSP5-8.5–CanESM5 model provided the strongest overall performance, followed by SSP2-4.5–FGOALS and SSP5-8.5–FGOALS. The framework not only identifies the most suitable models for pavement design in Oklahoma but also demonstrates a transferable methodology for climate-adaptive infrastructure planning in other regions.
Abstract Structural evaluation of pavement is crucial for informed maintenance and rehabilitation decisions within pavement management systems. Traffic speed deflection devices (TSDDs) can be used as practical tools to support these activities expeditiously. The effectiveness of TSDD data in pavement management depends on a thorough understanding of the complex interplay between pavement properties, environmental conditions, and the deflection parameters captured. Material attributes such as layer thickness and composition, alongside operational factors such as temperature and vehicle speed, may significantly influence pavement deflection parameters. In this study, more than 100,000 simulated pavement structures were analyzed using viscoelastic numerical modeling and statistical analysis techniques to decipher the intricate relationship between pavement characteristics and TSDD-measured deflections or deflection slopes. The subgrade modulus is the primary factor influencing the deflection/slope parameters of TSDDs. Operational parameters, including vehicle speed and tire pressure, are marginally correlated with the TSDDs’ deflections/slopes as long as they are controlled reasonably well during data collection. The predictive power of more than 40 deflection-based indices was evaluated to identify the most effective index for isolating the layers of concern within a pavement structure. This research contributes to the optimization of TSDDs’ practical applications in pavement evaluation for more effective management and preservation of pavement infrastructure.
Abstract This study focuses on the construction, optimization, and interpretability analysis of different International Roughness Index (IRI) prediction models based on the Long-Term Pavement Performance (LTPP) database. A multidimensional feature system was established, and 15 key variables related to pavement structures, traffic loading, and climatic factors were identified using the Boruta algorithm in combination with Gini and permutation importance measures. Several machine learning models, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed and compared. To enhance predictive performance, Bayesian optimization (BO) was employed to fine-tune hyperparameters, and the BO-XGBoost model achieved the best results on the testing set [ R 2 = 0.9549 , root mean square error (RMSE) = 0.0335, mean absolute percentage error (MAPE) = 4.77%], while maintaining high computational efficiency. Comparative experiments confirmed that BO-XGBoost outperformed models optimized with differential evolution (DE) and grid search (GS), as well as other candidate models, in terms of both accuracy and stability. Furthermore, Shapley additive explanations (SHAP)-based interpretability analysis revealed that surface layer thickness, traffic loading, and average humidity were the most influential factors affecting IRI.
Abstract Asphalt pavements are prone to cracking during service, which adversely affects structural integrity and service life. To enhance automation and controllability in crack repair, a jet-based 3D printing system was developed and systematically evaluated. The system adopts a dual-module architecture integrating air compression, multizone asphalt heating, and motion path control, enabling precise regulation of pressure, temperature, and deposition trajectory. Crumb rubber (CR)/styrene-butadiene-styrene (SBS) composite-modified asphalt, incorporating 4% C9 petroleum resin to improve storage stability and overall performance, was selected as the repair material based on its temperature-viscosity characteristics. Through controlled laboratory experiments on planar substrates, key process parameters were optimized. The optimal printing window was identified at a printing temperature of 150°C, a pressure range of 0–5 MPa, a printing speed of 400 mm / s , a nozzle height of 4 mm, a printing width of 5 mm, and a layer height of 0.4 mm. Under these conditions, stable jetting, uniform deposition, and improved surface integrity were achieved. The results indicate that balancing volumetric filling capacity and deposition stability is critical for jet printing of high-viscosity asphalt materials. This study establishes a reproducible process parameter framework for jet-based asphalt crack repair under laboratory-scale conditions. Although field-scale validation remains necessary, the proposed system provides a technical foundation for the development of automated and intelligent pavement maintenance technologies.
Abstract High solar absorptivity causes asphalt pavements to reach surface temperatures above 60°C during peak summer, accelerating rutting and intensifying the urban heat island effect. These elevated thermal conditions accelerate binder aging, reduce service life, and compromise ride quality and safety. Pavement-integrated thermoelectric generator systems (PTEGS) have the potential to harvest this excess thermal energy while influencing pavement thermal behavior. However, the performance of such systems is strongly governed by the effectiveness of cold-side thermal regulation. This research systematically evaluates different cooling setup configurations in the laboratory. Results indicated that the water-based cooling configuration was the most effective cooling strategy, maintaining cold-side temperatures below 30°C and achieving a maximum voltage output of 0.39 V at a temperature gradient ( Δ T ) of approximately 55.5°C. Phase change material (PCM)-assisted modules, particularly OM36P, stabilized cold-side temperatures and generated 0.29 V, outperforming conventional heat sinks. The findings establish a clear hierarchy of cooling effectiveness and identify hybrid liquid PCM strategies as practical approaches for developing multifunctional and sustainable pavement systems aligned with the United Nations Sustainable Development Goals (SDGs).
Abstract Frost heave and thaw settlement occur widely in northern China. Environmental change has increased their occurrence. High-latitude permafrost regions have high temperature sensitivity, experience freeze–thaw settlement, and exhibit unique rheological properties. Multiple roads have been damaged by freeze–thaw settlement, adversely affecting highway construction in high-latitude regions. This study assesses road damage in high-latitude permafrost regions and the underlying mechanisms and evolution. Highway construction in permafrost areas may encounter various problems due to freeze–thaw cycles, such as frost heaving, thaw settlement, and ice damage to roads. This study discusses countermeasures to prevent hazards and reduce risks. Multiple cases are analyzed to evaluate the measures’ effectiveness. The results indicate that the proposed measures maintain the road’s thermal stability, provide thermal insulation, and prevent disasters. This study provides insights into the long-term durability and stability of roads in permafrost regions and information for the structural design of roads in cold regions.
Abstract Effective monitoring of pavement conditions plays an important role in ensuring road safety and optimizing maintenance. Traditional inspection methods are labor-intensive and limited in coverage, motivating the use of remote sensing and deep learning for scalable alternatives. This study proposes a novel framework for classifying pavement type and condition by integrating medium-resolution optical imagery from Planet satellite and synthetic aperture radar (SAR) data from Sentinel-1. A deep learning dataset was constructed by combining eight bands and labeling them using IRI data collected over multiple years. Two semantic segmentation models, U-Net and DeepLabV3, were trained and evaluated. Also, a series of band-drop scenarios was tested to assess the importance of each spectral and radar band. Results showed that DeepLabV3 outperformed U-Net in both overall accuracy and weighted F 1 -score. Particularly, DeepLabV3 was more successful in identifying fair and poor asphalt pavements, which are critical for proactive maintenance decisions. The analysis illustrates that combining SAR with optical data consistently improved results compared to using either source independently. These findings demonstrate the feasibility and effectiveness of using multisource satellite imagery with deep learning for scalable, cost-efficient pavement condition assessment. The proposed framework offers transportation agencies a practical solution for enhancing infrastructure monitoring and data-driven decision-making.
Abstract Asphalt concrete (AC) exhibits complex damage behavior under the combined effects of environmental factors and loads. Phenomenological models may encounter difficulty in deeply explaining its damage evolution mechanisms. To solve this issue, this study developed a multiscale method that establishes a two-way coupling between the macroscopic and mesoscopic finite element (FE) models of AC. Thermal and mechanical properties at both scales as well as the damage behavior at the mesoscale were simultaneously considered. A representative volume element (RVE) of AC was developed based on computed tomography slices and digital image processing methods. An automatic insertion method for 3D cohesive elements suitable for complex structural FE models was proposed, and an RVE model with cohesive elements (RVE-coh) was established. Transient thermal stress fields of asphalt pavement were solved based on real climatic data and sequential coupling computation. A localization method was proposed for determining mesomechanical responses based on 3D shape function interpolation. The multiscale FE method was applied to analyzing the process of reflective crack initiation. The results showed that this method effectively considers the heterogeneous characteristics of AC and the combined effects of temperature field and wheel load. It provides in-depth insights into the mesomechanical responses and the evolution process of damage within the pavement structure.
Abstract The precise generation of project-level maintenance and rehabilitation (M&R) plans is pivotal for ensuring the operational feasibility and effectiveness of pavement interventions. Current M&R plans rarely account for practical on-site construction scenarios. This oversight leads to multiple M&R actions being mixed together on adjacent pavement sections, resulting in wastage of resources, manpower, and time. Furthermore, existing models for generating M&R plans rarely consider traffic safety factors and lack a systematic postmaintenance evaluation mechanism, resulting in the implementation of plans deviating from theoretical expectations. In response to these issues, this study proposes an improved M&R plan generation model based on an artificial neural network (ANN) called ANN_M. First, a clustering method is optimized to merge pavement sections to ensure spatial consistency. Next, this model integrates key traffic safety indicators. Finally, it introduces a postmaintenance evaluation feedback mechanism. Through a pavement performance prediction model, it evaluates the effectiveness of the M&R plans output by ANN_M and replaces plans with poor maintenance results. The M&R plan generation model is used to generate M&R plans with constructability for certain expressways in Shanxi Province. The results show that the addition of postmaintenance evaluation improved the model’s prediction accuracy by 5.57%. The introduction of pavement section spatial consistency is designed to eliminate execution fragmentation, thereby enhancing construction feasibility and potential operational efficiency. Compared with the traditional ANN M&R plan generation model that does not consider spatial consistency and postmaintenance evaluation, the prediction accuracy was improved by 8.66%, proving the obvious advantages of the ANN_M improved model in terms of accuracy. The ANN_M model offers a practical solution for consolidating disjointed M&R actions into spatially consistent and constructible M&R plans.
Abstract Crack repair has long been a critical task in highway maintenance. With the continuous expansion of the scale of roads under maintenance, traditional manual patching can no longer meet the growing demand. Current mainstream pavement crack repair technologies generally have the shortcoming that real-time performance and accuracy are difficult to balance. To address the dual problems of insufficient crack detection accuracy and low efficiency in automated repair path planning, this study is committed to developing an efficient intelligent repair method. It proposes a two-stage algorithm integrating intelligent crack detection and automated repair. In the intelligent detection stage, a lightweight U-shaped hybrid network (LU-Net), which combines U-Net and MobileNetV2, is designed to facilitate intelligent crack identification and trajectory extraction. In the path planning stage, an exact algorithm based on dynamic programming (DP) is introduced to determine the optimal repair sequence automatically. Comparative experiments verified the effectiveness and accuracy of the method: the detection accuracy of LU-Net reached 88.35% with a mean intersection over union (MIoU) of 80.39%; for scenarios with 5–14 crack paths, the proposed dynamic programming algorithm not only found the global optimal solution but also reduced the solution time by 90% compared with existing exact algorithms. The results show that the LU-Net segmentation model and DP algorithm can significantly save operation time while ensuring accuracy.
