Search Results (130)

With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression models have limitations in capturing the coupled effects of traffic, structure, and environmental factors. To overcome this limitation, this study constructed a dataset containing 10,836 samples based on the Long-Term Pavement Performance (LTPP) database, integrating traffic load, pavement structure parameters, and climate variables. The variance inflation factor (VIF) and correlation analysis were used to validate the effectiveness of feature selection. We trained nine machine learning models and optimized the hyperparameters using a Bayesian optimization method with five-fold cross-validation to ensure good generalization ability. Results show that the TabPFN model, based on prior information, achieved the best overall performance with a coefficient of determination $R^2$ = 0.9474 and a low prediction error (RMSE = 0.138) on the test set. Paired t-tests based on cross-validation further confirmed that TabPFN’s predictive performance is statistically superior to the baseline model. SHAP and generalized additive model (GAM) analyses indicate that traffic load is the main driver of IRI growth, while structural layer thickness, within a certain range, can mitigate pavement roughness. Climatic factors have indirect long-term effects through cumulative environmental exposure. Although the main drivers differ slightly among different pavement structures, traffic load consistently plays a dominant role. To enhance the model’s practical applicability, we also developed a user-friendly graphical interface (GUI) for fast and accurate IRI prediction. Full article

Accurate, scalable pavement condition monitoring is essential for effective asset management, yet traditional methods of collecting metrics like the International Roughness Index (IRI), Pavement Condition Index (PCI), and Pavement Surface Evaluation and Rating (PASER) can be inefficient, expensive, and subjective. Recent efforts by Original Equipment Manufacturers have introduced crowdsourced approaches that estimate IRI at scale using connected vehicles (CVs). This study analyzes one month of CV-estimated IRI (IRI_CVe) data and compares it with manually collected PCI data from Marion County, Indiana, in 2024. The study includes four roadway classes: primary arterial, secondary arterial, primary collector, and local street, with 562, 147, 426, and 2402 centerline miles of data, respectively. IRI_CVe coverage was nearly complete for arterial and collector roads (93–100%) but was limited for local streets (37%). Threshold optimization revealed that the “needs maintenance” IRI category (IRI > 170 in/mi) correlates most strongly with PCI values below 50. The study found that 68%, 65%, 70%, and 59% of the roadway segments had PCI and IRI classifications in agreement. Spatial and categorical comparisons suggest some systematic biases between the metrics across roadway types, reflecting how they measure different dimensions of pavement condition. The results demonstrate near-term applications of IRI_CVe data for quality control in PCI-based asset management and support practical guidelines for integrating complementary pavement assessment metrics. Full article

This study presents a comprehensive material characterization program to develop the database inputs required for implementing the Mechanistic–Empirical Pavement Design Guide (MEPDG) in the United Arab Emirates (UAE). Five asphalt concrete (AC) mixtures were evaluated, including two conventional penetration-grade binders (PEN 40/50 and PEN 60/70) and three SBS-modified binders (PG70E–0, PG76E–10, and PG82E–22). The experimental program followed AASHTOWare Pavement ME Design requirements and included asphalt binder testing (penetration, softening point, rotational viscosity, DSR, and BBR) and AC mixture testing (dynamic modulus, flow number, axial fatigue, and indirect tensile strength). The results showed that SBS-modified binders and mixtures, particularly PG70E–10 and PG82E–22, exhibited improved rheological behavior, reduced permanent deformation, and enhanced fatigue resistance, while PG76E–10 demonstrated intermediate performance, highlighting the influence of polymer formulation and mixture structure. Pavement ME simulations indicated that Level 1 material inputs preserved laboratory-observed performance trends, resulting in lower predicted rutting, fatigue cracking, and International Roughness Index (IRI). In contrast, Level 3 inputs masked material-specific behavior and, in some cases, altered mixture performance rankings. These findings emphasize the necessity of mixture-level testing and Level 1 inputs for reliable mechanistic–empirical pavement design under UAE climatic and traffic conditions. Full article

The International Roughness Index (IRI) is calculated from elevation profiles acquired by high-speed profilers or laser scanners, but these raw data often contain measurement noise and extraneous wavelength components that can degrade the accuracy of IRI calculations. Existing filtering methods expose a limitation in removing IRI-insensitive wavelength components. Thus, this paper proposes a Gaussian filtering algorithm based on the Nyquist sampling theorem to remove IRI-insensitive components of the longitudinal profile. The proposed approach first adaptively determines Gaussian template lengths according to sampling intervals, and then incorporates a boundary padding strategy to ensure processing stability. The proposed method enables precise wavelength selection within the IRI-sensitive band of 1.3–29.4 m while maintaining computational efficiency. The method was validated using the Paris–Lille dataset and the U.S. Long-Term Pavement Performance (LTPP) program dataset. The filtered profiles were evaluated by Power Spectral Density (PSD), and IRI values were calculated and compared with those obtained by conventional profile filtering methods. The results show that the proposed method is effective in removing the non-sensitive components of IRI and obtaining highly accurate IRI values. Compared with the standard IRI provided by the LTPP dataset, mean absolute error of the IRI values from the proposed method reaches 0.051 m/km, and mean relative error is less than 4%. These findings indicate that the proposed method improves the reliability of IRI calculation. Full article

Conventional PMSs often rely on static age-based assumptions, which can fail to capture nonlinear, state-dependent deterioration and improvement-like responses observed in long-term monitoring data. This study addresses these limitations by proposing a reaction-oriented analytical framework using eight years of Korea Highway PMS data (2015–2022). We construct a Δ–State Vector by combining the previous-year condition grade with noise-filtered annual changes in the International Roughness Index (IRI) and Rut Depth (RD). Measurement noise is separated from structural signals via MAD-based noise bands (ΔIRI: ±0.089 m/km; ΔRD: ±0.993 mm), with a global MAD floor (minimum-threshold constraint) to avoid degenerate zero-band cases under sparse or near-constant transitions. The resulting vectors are embedded into a low-dimensional Reaction Space using UMAP and clustered with HDBSCAN. To validate interpretability, a rule-based Trend × Mode Reaction Signature taxonomy is used to assess the semantic consistency of unsupervised clusters. Five dominant reaction regimes are identified, showing strong agreement with signature-based labels (weighted purity = 0.927; coverage for purity ≥ 0.60 = 0.911). Overall, the results indicate that deterioration dynamics are governed by lane–segment heterogeneity and prior-state dependence rather than chronological age, providing a reproducible foundation for future event-sensitive, dynamic age reset frameworks. Full article

Deflection slopes measured by the traffic speed deflectometer (TSD) are being used to backcalculate the moduli of pavement layers. Pavement surface roughness causes variations in tyre load magnitude due to excitation, which affects TSD measurements. In this study, three rough pavement surface profiles over 150 m longitudinal distances were extracted from the Long-Term Pavement Performance (LTPP) programme database. Utilising finite element method (FEM) simulation of the TSD pass at a travel speed of 80 km/h over a three-layer flexible pavement system containing the rough surface profiles and employing the Greenwood Engineering TSD backcalculation tool, it was found that tyre load excitation can lead to backcalculation errors of up to 48%. By obtaining deflection slopes at equal distance intervals along the 150 m pavement profiles, it was found that averaging the deflection slopes across 9 measurement points reduced backcalculation errors to 10%, while increasing the number of measurement points to 28 further lowered the backcalculation errors to 5%. These findings highlight the potential to mitigate the effects of tyre load excitation on TSD backcalculation outputs without relying on strain gauges, which are mounted on modern TSDs to measure instantaneous tyre load magnitudes but are sensitive to environmental conditions and require calibration. Full article

To accurately simulate the progression of pipeline corrosion, this paper proposes a three-dimensional corrosion modeling method for curved random surfaces based on spatial frequency composition. It applies this method to the inner surface of layered pipelines to emulate both the morphological characteristics and the evolution of internal corrosion. Combined with ultrasonic guided wave technology, the approach enables quantitative assessment of internal corrosion in layered pipelines. First, trigonometric series expansion and nonlinear polynomial superposition are used to characterize the roughness and curvature of the corroded surface, respectively, establishing a mathematical model capable of accurately representing complex corrosion morphologies. Next, a COMSOL–ABAQUS co-modeling approach is employed to build a finite element model of a three-layer composite pipeline consisting of a steel pipe, an insulating layer, and an anti-corrosion layer, with curved random-surface corrosion on the inner surface of the steel pipe. Finally, a wavelet packet decomposition algorithm is applied to extract features from the guided wave echo signals, creating a damage index matrix to correlate the corrosion area with the damage index quantitatively. The results show that the damage index increases steadily with the corrosion area, confirming the effectiveness of the proposed method. This study provides an alternative technical approach for high-fidelity modeling and precise assessment of pipeline corrosion detection. Full article

Accurate modeling of pavement performance is vital to maintaining safe, reliable, and sustainable transportation infrastructure. This review synthesizes current approaches to pavement deterioration modeling, with emphasis on key influencing factors, performance indicators, and methodologies employed within Pavement Management Systems (PMS). Primary deterioration drivers, including traffic loading and environmental stressors, are analyzed for their impact on degradation patterns. Performance indicators such as the Pavement Surface Evaluation and Rating (PASER), Pavement Condition Index (PCI), and International Roughness Index (IRI) are evaluated for their effectiveness in capturing pavement condition and guiding maintenance decisions. Modeling techniques are broadly categorized into deterministic, probabilistic, and intelligent (machine learning–based) frameworks to illustrate the evolution of predictive approaches. Across these approaches, the notion of symmetry can be interpreted as the balance and consistency achieved between model assumptions, input variables, and predicted pavement behavior, while asymmetry represents deviations caused by uncertainty, variability, and nonlinearity inherent in real-world conditions. Recognizing these symmetrical and asymmetrical relationships helps unify different modeling paradigms and provides insight into how each framework handles equilibrium between accuracy, complexity, and interpretability. The review also highlights persistent challenges in data availability, quality, and standardization. Notably, the increasing adoption of machine learning reflects its capacity to handle high-dimensional and spatiotemporal datasets. Recommendations are proposed to improve the robustness, scalability, and transparency of future deterioration models, thereby enhancing their role in data-driven, resilient, and cost-effective pavement management strategies. Full article

This research, carried out within the framework of the European Space Agency’s second Scout mission (HydroGNSS), seeks to utilize CYGNSS Level 1B products over land for soil moisture estimation. The approach involves a novel physically based algorithm, which inverts a semiempirical forward model of surface reflectivity proposed in the literature. An Artificial Neural Network (ANN) algorithm has also been developed. Both methods are implemented in the frame of the HydroGNSS mission to make the most of the reliability of an approach rooted in a physical background and the power of a data-driven approach that may suffer from limited training data, especially right after launch. The study aims to compare the results and performance of these two methods. Additionally, it intends to evaluate the impact of auxiliary data. The static auxiliary data include topography, Above Ground Biomass (AGB), land cover, and surface roughness. Dynamic auxiliary data include Vegetation Water Content (VWC) and Vegetation Optical Depth (VOD) from Soil Moisture Active Passive (SMAP), as well as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Moderate Resolution Imaging Spectroradiometer (MODIS), on enhancing the accuracy of retrievals. The algorithms were trained and validated using target soil moisture values derived from SMAP L3 global daily products and in situ measurements from the International Soil Moisture Network (ISMN). In general, the ANN approach outperformed the semiempirical model with RMSE = 0.047 m³ m⁻³ and R = 0.91. We also introduced a global stratification framework by intersecting land cover classes with climate regimes. Results show that the ANN consistently outperforms the semiempirical model in most strata, achieving around RMSE = 0.04 m³ m⁻³ and correlations above 0.8. The semiempirical model, however, remained more stable in data-scarce conditions, highlighting complementary strengths for HydroGNSS. Full article

This study investigates the potential of Floating Car Data (FCD) collected from Volkswagen Group vehicles since 2022 for monitoring pavement conditions along two Italian road stretches. While such data are primarily gathered to analyze vehicle dynamics and mechanical behaviour, here, they are repurposed to support road network assessment through the estimation of the International Roughness Index (IRI). Daily aggregated datasets provided by NIRA Dynamics were analyzed to evaluate their reliability in detecting spatial and temporal variations in surface conditions. The results show that FCD can effectively identify critical sections requiring maintenance, track IRI variations over time, and assess the performance of surface rehabilitation, with high consistency on single-lane roads. On multi-lane roads, limitations emerged due to data aggregation across lanes, leading to reduced accuracy. Nevertheless, FCD proved to be a cost-efficient and continuously available source of information, particularly valuable for identifying temporal changes and supporting the evaluation of maintenance interventions. Further calibration is needed to enhance alignment with high-performance measurement systems, considering data density at the section level. Overall, the findings highlight the suitability of FCD as a scalable solution for real-time monitoring and long-term maintenance planning, contributing to more sustainable management of road infrastructure. Full article

The current global economic challenges and resource scarcity necessitate the development of cost-effective and sustainable pavement solutions. This study investigates the performance of asphalt mixtures modified with Low-Density Polyethylene (LDPE) and Styrene–Butadiene–Styrene (SBS) as binder modifiers, and Hydrated Lime (Ca(OH)₂) and Reclaimed Asphalt Pavement (RAP) as aggregate replacements. The research aims to optimize the combination of these materials for enhancing the durability, sustainability, and mechanical properties of asphalt mixtures under various climatic and traffic conditions. Asphalt mixtures were modified with 5% LDPE and 2–6% SBS (by bitumen weight), with 2% Hydrated Lime and 15% RAP added to the mix. The performance of these mixtures was evaluated using the Simple Performance Tester (SPT), focusing on rutting, cracking, and fatigue resistance at varying temperatures and loading frequencies. The NCHRP 09-29 Master Solver was employed to generate master curves for input into the AASHTOWare Mechanistic-Empirical Pavement Design Guide (MEPDG), allowing for an in-depth analysis of the modified mixes under different traffic and climatic conditions. Results indicated that the mix containing 5% LDPE, 2% SBS, 2% Hydrated Lime, and 15% RAP achieved the best performance, reducing rutting, fatigue cracking, and the International Roughness Index (IRI), and improving overall pavement durability. The combination of these modifiers showed enhanced moisture resistance, high-temperature rutting resistance, and improved dynamic modulus. Notably, the study revealed that in warm climates, thicker pavements with this optimal mix exhibited reduced permanent deformation and better fatigue resistance, while in cold climates, the inclusion of 2% SBS further improved the mix’s low-temperature performance. The findings suggest that the incorporation of LDPE, SBS, Hydrated Lime, and RAP offers a sustainable and cost-effective solution for improving the mechanical properties and lifespan of asphalt pavements. Full article

Accurate prediction of the International Roughness Index (IRI) is critical for road safety and maintenance decisions. In this study, we propose a novel Physics-Aware Informer (PA-Informer) model that integrates the efficiency of the Informer structure with physics constraints derived from partial differential equations (PDEs). The model addresses two key challenges: (1) performance degradation in short-sequence scenarios, and (2) the lack of physics constraints in conventional data-driven models. By embedding residual PDEs to link IRI with influencing factors such as temperature, precipitation, and joint displacement, and introducing a dynamic weighting strategy for balancing data-driven and physics-informed losses, the PA-Informer achieves robust and accurate predictions. Experimental results, based on data from four climatic regions in China, demonstrate its superior performance. The model achieves a Mean Squared Error (MSE) of 0.0165 and R² of 0.962 with an input window length of 30 weeks, and an MSE of 0.0152 and R² with an input window length of 120 weeks. Its accuracy is superior to that of other models, and the stability of the model when the input window length changes is far better than that of other models. Sensitivity analysis highlights joint displacement and internal stress as the most influential features, with stable sensitivity coefficients (S_p ≈ 0.89 and S_p ≈ 0.81). These findings validate the PA-Informer as a reliable and scalable tool for predicting pavement performance under diverse conditions, offering significant improvements over other IRI prediction models. Full article

Proppant crushing seriously affects the efficiency and effectiveness of oil and gas production. In conventional studies, multi-particle crushing research often adopts the particle replacement method; however, this method results in a relatively rough and discontinuous crushing simulation process, making energy conservation difficult to maintain before and after crushing, neglects complex mechanical behaviors such as internal stress distribution and crack propagation of particles, and thus lacks mechanical authenticity. Thus, this study employs the bonded crushing method and establishes a calibration method for mesoscopic parameters. By constructing a particle flow numerical model, the force and crushing processes of proppants under different mesoscopic parameter conditions for both single-particle clusters and multi-particle clusters are simulated, enabling comprehensive monitoring of internal crack propagation within particle clusters. The study systematically analyzes and investigates the influence of key mesoscopic parameters including the tensile strength of parallel bonds (pb-ten), cohesion of parallel bonds (pb-coh), effective modulus (emod), and stiffness ratio (kratio) on the maximum force required for particle crushing. Additionally, orthogonal experiment analysis is used to study the influence of different mesoscopic parameters on the proppant crushing rate. The results show that the larger the pb-ten and pb-coh, the less likely the proppant particle clusters are to crush; conversely, the higher the emod, the more likely the particle clusters are to crush. Within a certain range, pb-ten has the most significant impact on the proppant crushing rate, followed by pb-coh and emod, while kratio has a smaller impact. Based on the research results regarding the influence of laws of different mesoscopic parameters on proppant crushing performance, the mesoscopic parameters of the proppant were calibrated using the post-experiment proppant crushing rate as the fitting index. The simulation results were then compared with the experimental results, verifying the accuracy of the model. The findings of this study clarify the influence of laws of mesoscopic parameters on proppant crushing performance, providing a basis for the subsequent calibration of mesoscopic parameters for numerical proppants and helping to accurately characterize the macroscopic crushing performance of numerical proppants. Full article

Many agencies use pavement condition assessments such as the Pavement Surface Evaluation and Rating (PASER) and Pavement Condition Index (PCI) to develop localized pavement management programs. However, both techniques involve some subjectivity and inconsistent measurement practices, making it difficult to scale uniformly across all 86 thousand miles of local agency roadway in Indiana’s 92 counties. International Roughness Index (IRI) data is one emerging data source that could address this need. This paper evaluates the feasibility of using Connected Vehicle-estimated IRI (IRI_CVe) data for long-term statewide pavement monitoring on local roads. The analysis is based on approximately 4.1 billion daily IRI_CVe records collected over a multi-year study period from connected vehicles operating throughout the state. A modular data processing workflow was developed to clean and process these records and is presented in detail in the paper. The study includes network-level condition comparisons, insights on spatiotemporal trends, and localized segment-level condition monitoring. In 2024, approximately 53% of paved local roads in Indiana had at least one IRI_CVe observation per year. Coverage varied widely by county: for example, 79% of roads in urban Hamilton County had coverage, but only 14% had coverage in rural Martin County. The findings in this study demonstrate the potential of IRI_CVe to support local agency pavement asset management by providing cost-effective data-driven insights in near real-time. Full article

This study evaluated the benefits of pavement preservation treatments across two climatic zones using data from the National Center for Asphalt Technology (NCAT) Pavement Preservation Group Study. Longitudinal data analysis was conducted to quantify pavement performance over time. Results indicate that in the freeze zone, treatments significantly improved pavement smoothness, as evidenced by reductions in the progression of the International Roughness Index (IRI), whereas similar trends were not observed in the no-freeze region, highlighting the need for further research to quantify the benefits in these zones. Life cycle cost analysis (LCCA) showed that selected preservation treatments reduced user costs by 54–57% due to lower excess fuel consumption, particularly in high-traffic corridors. These treatments also contributed to reductions in greenhouse gas (GHG) emissions by decreasing fuel use. Despite these findings, comprehensive, high-quality data are needed to fully evaluate the economic and environmental benefits of preservation treatments at the project level and to improve decision-making in pavement management strategies. Full article