Search Results (160)

The mounting impacts of climate change on infrastructure demand proactive adaptation strategies to ensure long-term resilience. This study investigates the effects of predicted future global warming on asphalt binder performance grade (PG) selection in the United States using a time series method. Leveraging Long-Term Pavement Performance (LTPP) data and Superpave protocol model, the research forecasts temperature changes for the period up to 2060 and calculates the corresponding PG values for different states. The results reveal significant temperature increases across the majority of states, necessitating adjustments in PG selection to accommodate changing climate conditions. The findings indicate significant increases in average 7-day maximum temperatures across the United States by 2060, with 38 out of 50 states likely to experience rising trends. Oregon, Utah, and Idaho are anticipated to face the largest temperature increases. Concurrently, the low air temperature has risen in 33 states, with notable increases in Maine, North Carolina, and Virginia. The widening gap predicted between required high and low PG poses challenges, as some necessary binders cannot be produced or substituted with other grades. The study highlights the challenge of meeting future PG requirements with available binders, emphasizing the need to consider energy consumption and CO₂ emissions when using modifiers to achieve the desired PG properties. Full article

This research manuscript presents a comprehensive investigation into the prediction and detection of reflective cracking in pavement infrastructure through a combination of machine learning approaches and advanced image detection techniques. Leveraging machine learning algorithms, reflective cracking prediction models were developed and optimized for accuracy and efficiency. Additionally, the efficacy of image detection methods, particularly utilizing Mask R-CNN, was explored for robust and precise identification of reflective cracking on pavement surfaces. The study not only aims to enhance the predictive capabilities of pavement management systems (PMSs) through machine learning-based models but also seeks to integrate advanced image detection technologies to support real-time monitoring and assessment of pavement conditions. By providing accurate and timely detection of reflective cracking, these methodologies contribute to the optimization of pavement maintenance strategies and the overall improvement of pavement infrastructure management practices. Results indicate that the developed machine learning models achieve an average predictive accuracy of over 85%, with some models achieving accuracies exceeding 90%. Moreover, the utilization of a mask region-based convolutional neural network (Mask R-CNN) for image detection demonstrates exceptional precision, with a detection accuracy of over 95% on average across different pavement types and weather conditions. The results demonstrate the promising performance of the developed machine learning models in predicting reflective cracking, while the utilization of Mask R-CNN showcases exceptional accuracy in the detection of reflective cracking from images. This research underscores the importance of leveraging cutting-edge technologies to address challenges in pavement infrastructure management, ultimately supporting the sustainability and longevity of transportation networks. Full article

Polyurethane (PU) mixture, which is a new pavement mixture, exhibits different dynamic properties compared to a hot-mixed asphalt mixture (HMA). This paper analyzed whether the Kramers–Kronig (K–K) relation and thermorheologically simple properties applied to the PU mixture. Based on the results, the PU mixture exhibited thermorheologically simple properties within the test conditions. The time–temperature superposition principle (TTSP) was applicable for the PU mixture to construct a dynamic modulus master curve using the standard logistic sigmoidal (SLS) model, the generalized logistic sigmoidal (GLS) model, and the Havriliak–Negami (HN) model. The Hilbert integral transformed SLS and GLS models for the phase angle can accurately fit the measured phase angle data with newly fitted shift factors and predict the phase angle within the viscoelastic range. The core–core and black space diagrams both displayed single continuous smooth curves, which can be utilized to characterize the viscoelastic property of the PU mixture. The K–K relation is applicable for the PU mixture to obtain the phase angle master curve model, storage modulus, and loss modulus from the complex modulus test results with the test temperatures and loading frequencies. The phase angle of the PU mixture at extremely high or low test temperatures cannot be derived from the dynamic modulus data. Full article

Pavement distress detection is a crucial task when assessing pavement performance conditions. Here, a novel deep-learning method based on a transformer network, referred to as ISTD-DisNet, is proposed for multi-type pavement distress semantic segmentation. In this methodology, a mix transformer (MiT) based on a hierarchical transformer structure is chosen as the backbone to obtain multi-scale feature information on pavement distress, and a mixed attention module (MAM) is introduced at the decoding stage to capture the pavement distress features across different channels and spatial locations. A learnable transposed convolution upsampling module (TCUM) enhances the model’s ability to restore multi-scale distress details. Subsequently, a novel parameter—the distress pixel density ratio (PDR)—is introduced based on the segmentation results. Analyzing the intrinsic correlation between the PDR and the pavement condition index (PCI), a new pavement damage index prediction model is proposed. Finally, the experimental results reveal that the F1 and mIOU of the proposed method are 95.51% and 91.67%, respectively, and the segmentation performance is better than that of the other seven mainstream segmentation models. Further PCI prediction model validation experimental results also indicate that utilizing the PDR enables the quantitative evaluation of the pavement damage conditions for each assessment unit, holding promising engineering application potential. Full article

Pavement performance models (PPMs) are utilized to predict pavement network conditions which is an essential part of any sustainable pavement management system (PMS). The reliability of a PMS and its outputs is proportional to the reliability of the PPM used. This article describes a mechanistic–empirical pavement performance model based on pavement response parameters—strains calculated in the pavement layers measured by tensometers embedded in the pavement surface and verified by calculations in the elastic half-space model and supplemented by empirical data from long-term pavement performance monitoring and accelerated pavement testing. Hence, the herein described PPM combines pavement serviceability evaluation, pavement bearing capacity, and the physico-mechanistic properties of paving materials. The analytical methods which were used to ascertain the physico-mechanistic characteristics, the material fatigue degradation model, and the surface degradation, unevenness in particular, are described. A comparison of the empirical PPM created in the last century used by the national road administrator to this day and the newly created PPM is presented. The comparison shows the difference in the calculated socio-economic benefits and subsequent cost–benefit analysis results. The comparison shows that the use of the old PPM may have produced false economic evaluation results that have led to poor decision making, partially explaining the unsustainable trend of road network management in our country. Full article

Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently. Full article

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential. Full article

Overloading and climate change are often problems in pavement structures. For this reason, hard asphalt binders have high softening points, are elastic, and have good adhesion, which is needed to improve pavement performance. Asphalt binder performance can be enhanced by adding additives such as natural rubber or natural-rubber-modified asphalt. However, natural-rubber-modified asphalt shows poor storage stability problems. This is due to differences in density and viscosity between the constituent components of natural-rubber-modified asphalt. This study examines the phase separation mechanism in technically specified natural rubber (TSNR) modified asphalt. Prediction of the optimum storage length of modified asphalt before phase separation occurs, using a combined incompressible Navier–Stokes and phase field model and carried out with COMSOL Multiphysics software version 5.5. Experimental validation was conducted at TSNR levels of 8, 10, and 12% at 160 °C for 48 h, with and without sulfur. The simulation showed that the asphalt modified with TSNR experienced phase separation after 12 h of storage at 160 °C under conditions without stirring. This aligns with the experimental results, which showed phase separation at 160 °C after 48 h. Adding sulfur additives did not have much effect on improving storage stability. The combined incompressible Navier–Stokes and phase field model accurately describes the phase separation in TSNR-modified asphalt. The results of this research recommend that the industry store natural-rubber-modified asphalt in a constantly stirred condition to prevent phase separation of modified asphalt. In addition, the results of this research help the industry predict or increase the homogeneity of polymer-modified asphalt production and save time and costs. Full article

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets. Full article

This study researches environmental factors, vehicle dynamics, and loading conditions on pavement structures, aiming to comprehend and predict their impact. The susceptibility of asphalt pavement to temperature variations, vehicle speed, and loading cycles is explored, with a particular focus on the lateral distribution of wheel tracks in driving and passing lanes. Utilizing video analysis and finite element modelling (FEM) through ABAQUS 2022 software, multiple input factors, such as speed (60, 80 and 100 km/h), loading cycles (100,000 to 500,000), and temperature range (0 °C to 50 °C), are applied to observe the maximum rutting (17.89 mm to 24.7 mm). It is observed that the rut depth exhibited is directly proportional to the loading cycles and temperature, but the opposite is true in the case of vehicle speed. Moreover, interpretable machine learning models, particularly the Bayesian-optimized light gradient boosting machine (LGBM) model, demonstrate superior predictive performance in rut depth. Insights from SHAP interpretation highlight the significant roles of temperature and loading frequency in pavement deformation. This study concludes with a comprehensive understanding of how these factors impact road structures in Pakistan. Its implications extend to valuable insights for optimizing road design, offering a significant contribution to enhancing the durability and sustainability of road infrastructure in the region. Full article

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R² of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking. Full article

The vehicle lateral stability control algorithm is an essential component of the electronic stability program (ESP), and its control effect directly affects the vehicle’s driving safety. However, there are still numerous shortcomings and challenges that need to be addressed, including enhancing the efficiency of processing intricate pavement condition data, improving the accuracy of parameter adjustment, and identifying subtle and elusive patterns amidst noisy and ambiguous data. The introduction of machine learning algorithms can address the aforementioned issues, making it imperative to apply machine learning to the research of lateral stability control algorithms. This paper presented a vehicle lateral electronic stability control algorithm based on the back propagation (BP) neural network and PID control algorithm. Firstly, the dynamics of the whole vehicle have been analytically modeled. Then, a 2 DOF prediction model and a 14 DOF simulation model were built in MATLAB Simulink to simulate the data of the electronic control units (ECU) in ESP and estimate the dynamic performance of the real vehicle. In addition, the self-correction of the PID algorithm was verified by a Simulink/CarSim combined simulation. The improvement of the BP neural network to the traditional PID algorithm was also analyzed in Simulink. These simulation results show the self-correction of the PID algorithm on the lateral stability control of the vehicle under different road conditions and at different vehicle speeds. The BP neural network smoothed the vehicle trajectory controlled by traditional PID and improved the self-correction ability of the control system by iterative training. Furthermore, it shows that the algorithm can automatically tune the control parameters and optimize the control process of the lateral electronic stability control algorithm, thus improving vehicle stability and adapting it to many different vehicle models and road conditions. Therefore, the algorithm has a high practical value and provides a feasible idea for developing a more intelligent and general vehicle lateral electronic stability system. Full article

In this study, full-scale laboratory tests were conducted on a 315 mm diameter HDPE pipe under shallow buried and localised surface loading conditions to investigate the effects of pipe deflection and arching on stress distribution and the lateral earth pressure coefficient. The tests were validated using 2D finite element software, and further analyses were carried out through parametric studies. These studies considered variations in pipe stiffness, burial depth, backfill properties and pavement stiffness to increase the reliability of the test results. For a shallowly buried HDPE pipe, a comprehensive explanation is provided regarding the evolution of the lateral earth pressure coefficient within the central soil prism. Initially set at Ko conditions, this coefficient tends to shift towards Kp with increasing arching and transitions to Ka with weakening arching. The findings suggest that stress predictions in the crown region of shallow buried flexible pipes are achievable through the application of Terzaghi’s arching theory, contingent upon an accurate estimation of the lateral earth pressure coefficient for the central soil prism. Furthermore, the horizontal deflection of the pipe at the springline results in compressive behaviour and passive effects in the surrounding backfill in this specific region. This situation demonstrates that the horizontal stresses at the springline and the lateral earth pressure coefficient can be reliably estimated by considering them as functions of the horizontal deflection of the pipe. Full article

The paving layer on the steel box girder bridge deck is widely used when constructing pavements for steel bridges. Owing to the orthotropic feature of steel decks, a transverse clapboard and rib can lead to a concentration of stress. Consequently, fatigue cracks are often identified in asphalt concrete pavement layers due to re-compaction caused by heavy vehicles. This study aims to derive an evaluation method of fatigue life for asphalt pavement based on the inhomogeneous Poisson stochastic process in view of the highly random and uncertain working conditions of layered composite structures. According to the inhomogeneous Poisson stochastic process, along with Miner’s fatigue damage accumulation theory and the linear elastic fracture mechanics theory, the fatigue life formula could be deduced. Meanwhile, fatigue experiments for asphalt concrete are designed to investigate the correlation between the theoretical formula and the actual fatigue damage life of the material. Compared with the test, the accuracy error is within 10%, which is better than other traditional methods. Therefore, the fatigue life prediction model could better reflect the loading order effect and the interaction between loads, providing a new path for the fatigue reliability design of steel bridge deck asphalt pavement. Full article

This study investigated the potential of lightweight deflectometer (LWD) data in predicting layer moduli and response measurements within the Mechanistic–Empirical Pavement Design Guide. To achieve this goal, field repeated LWD tests and laboratory repeated load triaxial tests were carried out on granular base material compacted at 3% and 6% water content, sandy subgrade soil compacted at 3%, 4% and 9% water content and silty sand subgrade soil compacted at 8% and 10% water content. The results revealed that substituting traditional repeated load triaxial (RLT) data with LWD data for predicting these parameters was notably effective for cohesionless materials, especially for unbound granular materials (UGMs) compacted at optimum water content. The accuracy and reliability of predictions were remarkably high, showcasing the potential of LWD to enhance efficiency and precision in pavement design within this context. Conversely, for cohesive road materials, the study emphasized the importance of considering specific material properties and water content when integrating LWD into the Mechanistic–Empirical Pavement Design Guide. The distinctive characteristics and behaviors of cohesive materials necessitate a nuanced approach. This understanding is critical to ensuring the accuracy and reliability of pavement design and assessment across diverse conditions. In summary, the study presents a promising avenue for utilizing LWD data in cohesionless road materials, offering potential cost and time-saving advantages. Additionally, it underscores the necessity of tailored approaches when considering material properties and moisture content for cohesive materials, thereby advancing the field of pavement engineering by providing insights for improved practices and adaptable frameworks. Full article