Pavement Maintenance Research Articles (Page 1)

Framework for Bundling Pavement Maintenance Projects Using Compatibility Scores

With the advancement of intelligent transportation and the “vehicle-road collaboration” concept, road roughness has become a key factor affecting ride safety and comfort. This study proposes a road elevation reconstruction method based on the International Roughness Index (IRI) and integrates it with the CarSim platform to simulate vehicle vibration responses under various speeds and IRI conditions. The weighted root mean square acceleration defined by ISO 2631 is adopted to evaluate ride comfort, and multiple regression, machine learning and deep learning models are employed to capture the nonlinear coupling between speed and roughness. Based on comfort thresholds, a driving strategy framework is developed to determine the optimal recommended speed. Field validation confirms that machine learning models effectively predict comfort levels, providing a reliable basis for intelligent driving assistance and pavement maintenance optimisation.

Abstract To investigate temperature variations within asphalt pavements during hot recycling, this study constructed a heterogeneous model of the pavement using industrial computed tomography (CT) and digital image processing. Building on heat-transfer theory, it then proposed a three-dimensional heterogeneous heat transfer model and evaluated its reliability through calculations of thermophysical parameters. The accuracy and precision of this heterogeneous heat transfer model were further verified via controlled heating experiments. In addition, the temperature field distribution characteristics of heterogeneous asphalt pavements were compared with those predicted by an idealized homogeneous heat transfer model. The results indicate that the proposed three-dimensional heterogeneous heat transfer model accurately captures the thermophysical behavior of real asphalt pavements. Relative to the homogeneous model, it shows markedly better agreement with experimental measurements, with a coefficient of determination (R2) exceeding 0.95. Under steady-state heat transfer conditions, the model more precisely resolves temperature variations within aggregate structures, thereby providing more reliable thermal data. This optimized model offers a solid theoretical foundation for asphalt pavement maintenance and hot-recycling technologies.

In recent years, pavement imaging systems, such as three-dimensional (3D) laser technology, have been widely adopted by state highway agencies (SHAs) for network-level pavement condition evaluation. This technology enables the collection of high-resolution pavement surface images at highway speed. Despite some SHAs collecting 3D pavement images for several years, these multi-temporal images have not yet been fully utilized to enhance pavement maintenance strategies at the project level. Therefore, this study aims to develop a predictive and precision pavement maintenance methodology by fully leveraging multi-temporal pavement images to advance cost-effective maintenance, allowing expensive treatments (e.g., deep patching) to be applied to isolated pavement spots at the optimal time. To achieve this, a data preparation pipeline was first developed to measure cracking severity and its deterioration for each pavement section. Then, a two-by-two matrix was created to prioritize pavement sections according to both cracking severity and deterioration rate. Finally, using deep patching as an example, a treatment planning optimization function was developed to strategically arrange treatment locations, considering construction operation constraints (e.g., a minimum deep patching distance of 10 m). A case study conducted on six years of data from a 5.8 mi section of US-80 near Savannah, Georgia, demonstrated the feasibility of the developed predictive and precision maintenance strategy, achieving more cost-effective maintenance.

Optimizing pavement maintenance and rehabilitation (M&R) strategies is essential, especially in developing countries with limited budgets. This study presents an integrated framework combining a deterioration prediction model and a genetic algorithm (GA)-based optimization model to plan cost-effective M&R strategies for flexible pavements, including asphalt concrete (AC) and double bituminous surface treatment (DBST). The GA schedules multi-year interventions by accounting for varied deterioration rates and budget constraints to maximize pavement performance. The optimization process involves generating a population of candidate solutions representing a set of selected road sections for maintenance, followed by fitness evaluation and solution evolution. A mixed Markov hazard (MMH) model is used to model uncertainty in pavement deterioration, simulating condition transitions influenced by pavement bearing capacity, traffic load, and environmental factors. The MMH model employs an exponential hazard function and Bayesian inference via Markov Chain Monte Carlo (MCMC) to estimate deterioration rates and life expectancies. A case study on Cambodia’s road network evaluates six budget scenarios (USD 12–27 million) over a 10-year period, identifying the USD 18 million budget as the most effective. The framework enables road agencies to access maintenance strategies under various financial and performance conditions, supporting data-driven, sustainable infrastructure management and optimal fund allocation.

A multi-objective optimization (MOO) problem of scheduling maintenance, repair, and rehabilitation (MRR) activities for pavements in an urban network while considering emissions is developed. The gaps that are addressed include accurately accounting for queue spillbacks that may arise due to MRR activities in urban areas to improve prediction of emissions. A bi-level optimization framework is utilized. At the lower level, the Link Transmission Model is used to analyticallly account for traffic flow dynamics and queue spillbacks due to MRR actions on links in an urban network. At the upper level, the population-based incremental learning algorithm is used. Various scenarios are simulated on an urban grid network of Philadelphia, PA. The results demonstrate that accounting for emissions in the MOO formulation leads to an MRR schedule with the lowest overall costs, despite slightly higher user costs. A Pareto frontier is developed to reveal the tradeoff between emissions and user-agency costs. Results suggest that emissions can be lowered by increasing user-agency costs until a certain point, after which only diminishing returns can obtained on emission reduction. The proposed methodology can provide valuable insights for agencies in improving overall network performance and, when considered together with broader social and economic impacts, enhancing sustainability.

Accurate pavement crack detection under adverse weather conditions is essential for road safety and effective pavement maintenance. However, factors such as reduced visibility, background noise, and irregular crack morphology make this task particularly challenging in real-world environments. To address these challenges, we propose CrackNet-Weather, which is a robust and efficient detection method that systematically incorporates three key modules: a Haar Wavelet Downsampling Block (HWDB) for enhanced frequency information preservation, a Strip Pooling Bottleneck Block (SPBB) for multi-scale and context-aware feature fusion, and a Dynamic Sampling Upsampling Block (DSUB) for content-adaptive spatial feature reconstruction. Extensive experiments conducted on a challenging dataset containing both rainy and snowy weather demonstrate that CrackNet-Weather significantly outperforms mainstream baseline models, achieving notable improvements in mean Average Precision, especially for low-contrast, fine, and irregular cracks. Furthermore, our method maintains a favorable balance between detection accuracy and computational complexity, making it well suited for practical road inspection and large-scale deployment. These results confirm the effectiveness and practicality of CrackNet-Weather in addressing the challenges of real-world pavement crack detection under adverse weather conditions.

This research provides useful insights into sustainable and cost-effective pavement rehabilitation by evaluating the combined effects of both Reclaimed Asphalt Pavement (RAP) and Crumb Rubber (CR) modification on flexible pavement performance using actual motorway sections. Pavement rehabilitation and maintenance can enhance the design and serviceable life of the pavement. Additionally, modification of asphalt with Crumb Rubber (CR) and Reclaimed Asphalt Pavement (RAP) not only proves to be economical but can also increase the resistance of flexible pavement concerning rutting, fatigue, and moisture damage. Four different pavement sections were selected, which were rehabilitated and modified with Reclaimed Asphalt Pavement (RAP), Crumb Rubber (CR), and a combination of both, along the Islamabad-Lahore motorway (M-2), Pakistan. The first pavement section consists of Asphalt Concrete Wearing Course (ACWC) with 60/70 grade bitumen as a binder (RAP 0%, CR 0%), the second pavement section was a mixture of asphalt concrete with crumb rubber modified bitumen as a binder (RAP0%, CR7%), the third pavement section was a blend of 15% RAP with 60/70 grade bitumen as a binder (RAP15%, CR 0%), the fourth section was a mixture of 15% RAP and 7% crumb rubber modified bitumen (RAP 15%, CR 7%). Pavement cores were extracted from the selected four pavement sections, which were experimentally explored in the laboratory to find the impact on the performance of highway pavement employing Reclaimed Asphalt Pavement (RAP) and Crumb Rubber (CR), partially replacing bituminous binder in the asphalt. The results show notable improvements in rutting resistance, tensile strength, and resilience. It was concluded that the performance of the section employing either RAP, CR or combined RAP and CR modified bitumen better enhanced the performance of pavement in terms of rut depth, indirect tensile strength and modulus of resilience. For instance, rutting depth was reduced by 41.35% and indirect tensile strength of pavement was increased by 17.93% by employing 15% RAP and 7% CR in modified bitumen binder for asphaltic mix. Likewise, the modulus of resilience was increased by 38.23% for the section employing 15% RAP and 7% CR in pavement.

Abstract The long-term performance of Continuously Reinforced Concrete Pavement (CRCP) and the optimization of maintenance strategies depend on the accurate forecasting of the International Roughness Index (IRI). For the purpose of accurately predicting the IRI in CRCP, this study offers a strong hybrid modeling framework that combines Support Vector Regression (SVR) with Genetic Algorithm (GA) optimization. Utilizing an extensive dataset from the Long-Term Pavement Performance (LTPP) program that included 395 observations and 33 CRCP sections, the suggested GA-SVR model was assessed against a number of benchmark models, such as Artificial Neural Networks (ANN), Decision Trees, Random Forests, Linear Regression, and SVR. The GA-optimized SVR model significantly outperformed all alternatives, achieving a mean RMSE of 0.039 and a coefficient of determination (R²) of 0.991 across five-fold cross-validation. Comprehensive residual analysis confirmed the model’s stability, while sensitivity analysis and feature importance rankings identified key influential variables such as Initial IRI, Layer 4 Type, and Layer 3 Thickness. Partial Dependence Plots and 3D visualizations further demonstrated how these factors affect IRI trends. The findings underscore the model’s high reliability, interpretability, and potential to support proactive pavement maintenance and design decisions. This research contributes a scalable and interpretable tool for enhancing the predictive capabilities of pavement performance models in data-driven infrastructure management.

Cost-effective optimization system for automated asphalt pavement maintenance

Road cracks pose a significant challenge to pavement maintenance by affecting structural integrity, jeopardizing traffic safety, and reducing driving comfort. With the limitations of manual inspection methods, such as being labor intensive, time consuming, and prone to human error, automated detection techniques have emerged as efficient and scalable alternatives. This study presents a detailed comparative analysis of state-of-the-art crack detection models, i.e., YOLOv7, VGG-19, ResNet-50, Naive Bayes, and deep convolutional neural networks, evaluating their performance on diverse and complex pavement image datasets. To ensure fairness and consistency, all models were trained and tested under identical conditions. ResNet-50 demonstrated superior performance, achieving the highest accuracy, that is, 99.8% in detecting and segmenting cracks in a variety of pavement scenarios. Its ability to balance precision and robustness makes it a leading solution for automated crack detection.

ABSTRACT Performance evaluation using Non-Destructive Testing (NDT) devices is a significant task for defining appropriate maintenance and rehabilitation strategies of in-service pavements. This study attempted to develop an alternative Falling Weight Deflectometer (FWD) testing scheme that minimise testing cost and time at project and network level. A series of experimental investigations were performed on flexible pavement sections by using Network Survey Vehicle (NSV) and FWD. The distresses and responses captured on 150 pavement sections, were analysed to estimate pavement condition, Coefficient of Variance (CV) of deflections, Structural Condition Index (SCI) and backcalculated layer moduli (MR_back). Among these sections, 115 and 35 sections were selected for development and comparison of alternative testing scheme. The developed test frequency for good, fair and poor pavement condition is 2 (500 m), 5 (200 m) and 10 (100 m) points, for a typical 1000 m section. The SCI and MR_back values for existing and developed alternative testing schemes are comparable at percentage variation of less than 5% and 2%. Therefore, this study suggests an alternative approach and testing scheme that minimises the cost by 38% - 41% and time for the periodical evaluation of pavement sections, thereby defining project maintenance strategies at project and network levels.

Attain: Inclusive annotated pavement distress types and severity dataset.

Abstract. Monitoring pavement condition is a crucial aspect for pavement maintenance management systems (PMMS). There are several pavement characteristics that affect the pavement condition, Crack distress is a highly representative type of pavement distress and often serves as an early indicator of more extensive pavement issues. Cracks impact both the operational efficiency and safety of road pavements and significantly influence maintenance decisions. We propose a workflow to detect cracks using YOLOv9 deep learning algorithm combined with statistical analysis through principal component (PCA) and Gaussian distribution. The proposed workflow includes camera calibration to address the metric issues in vision-based crack detection methods, utilizing Zhang's calibration method to compute the camera's internal and external parameters. To validate the proposed framework, three different datasets were acquired. Laser Crack Measurement System (LCMS) was used as a ground truth data for further verification the proposed method. Experimental results demonstrate that the proposed method achieves millimeter-level accuracy (std= ±1.0mm) compared to LCMS. This indicates the method's potential applicability for asphalt road crack segmentation and crack width estimation.

Aiming at the problems of complex process flow, high energy consumption, and difficult emulsification in the preparation of traditional SBS-modified emulsified asphalt, a preparation method of fast-melting SBS (referred to as SBS-T) modified emulsified asphalt based on the integration of modification and emulsification is proposed. Based on surface free energy theory, the contact angles between three rapid-melting SBS-modified emulsified asphalts with different dosages and three probe liquids (deionized water, glycerol, and formamide) were measured using the sessile drop method. The adhesion performance of the asphalt–aggregate system was studied by means of micromechanical methods. The evaluation indicators such as the cohesion work of the emulsified asphalt, the adhesion work of asphalt–aggregate, the spalling work, and the energy ratio were analyzed. The results show that the SBS-T modifier can significantly improve the thermodynamic properties of emulsified asphalt. With increasing modifier content, the SBS-T-modified emulsified asphalt demonstrated enhanced cohesive work, improved asphalt–aggregate adhesive work, and increased energy ratio, while showing reduced stripping work. At equivalent dosage levels, the SBS-T-modified emulsified asphalt demonstrates a slight improvement in adhesion performance to aggregates compared to conventional SBS-modified emulsified asphalt. The SBS-T emulsified modified asphalt provides an effective technical solution for the preventive maintenance of asphalt pavements.

ABSTRACT This study presents a mechanistic model simulation of rutting behaviour in the granular layers of airfield pavements using the time-dependent modified Drucker-Prager Cap (MDPC) model. The time-dependent MDPC is hypothesized as a constitutive model for predicting plastic deformation of granular materials with hardening compression. An efficient inverse optimisation approach is developed, integrating finite element analysis results with full-scale test data from a simulated airfield pavement exhibiting significant rutting in unbound layers. The algorithm calibrates the MDPC model to characterise permanent deformation in the granular base layer. Rutting data were obtained from the US Army Engineer Research and Development Centre (ERDC) full-scale tests. Results show that (1) the time-dependent MDPC model effectively predicts plastic deformation in pavement granular layers; (2) low cohesion values cause premature shear failure, low cap aspect ratios lead to early rutting saturation, and hardening parameters strongly influence rutting magnitude; and (3) the model's creep mechanism allows for characterising densification of material due to aggregate rearrangements under cyclic loading. This modelling and optimisation approach effectively identifies key material parameters governing permanent deformation in airfield pavements under repeated loading, providing valuable insights for advancement of pavement design and maintenance strategies for heavy-duty highways and airfields.

Flexible and rigid pavements are commonly built for airport pavements to support the moving loads of aircraft during the pavement design life. Airport pavements represent a cornerstone of the aviation world. Their condition profoundly impacts safety, operational efficiency, airport capacity, and financial well-being. These meticulously engineered surfaces must withstand the immense stresses generated by aircraft during takeoff, landing, and taxiing. At the planning stage, the pavement structure, materials, aircraft loads, environmental conditions, and pavement damage models should be evaluated. Comparing with road pavement design, airport pavement structural design is unique in terms of the traffic loads supported by pavements with high load magnitude, significant tire pressure, and dynamic traffic conditions. Over time, deterioration stemming from environmental exposure, aircraft loading, and other factors becomes inevitable. This study aims to explore the various factors influencing airport pavement performance, review the existing methodologies for pavement design and maintenance, and propose enhancements to current practices to ensure long-term durability and safety of airport pavements. This study aims to explore the various factors influencing airport pavement performance, review the existing methodologies for pavement design and maintenance, and propose enhancements to current practices to ensure long-term durability and safety of airport pavements.

Effective road maintenance is critical for ensuring safety, efficiency, and convenience in transportation networks. However, conventional assessment methods, often relying on isolated metrics or visual inspections, fail to capture the complex interplay of pavement condition factors, leading to potential misallocation of maintenance efforts. This study proposes a two-stage framework to enhance road condition assessment and maintenance prioritization by integrating individual performance indices with a unified pavement assessment metric (UPAM) developed through the fuzzy analytic hierarchy process. In the first stage, road segments are pre-screened based on individual indices—the structural condition index, international roughness index, international friction index, and pavement condition index—to identify localized deficiencies. Segments are classified as high or low priority based on threshold performance. In the second stage, segments are systematically ranked within each group using UPAM scores. While individual indices offer detailed diagnostic insights, they lack consistency for ranking; conversely, the UPAM provides standardized prioritization but may obscure critical localized issues. The combined approach improves transparency, reliability, and decision-making effectiveness, offering a comprehensive tool for optimizing pavement maintenance strategies.

Against the backdrop of China’s “dual-carbon” initiative, this study innovatively applies a process-based life cycle assessment (PLCA) methodology, meticulously tracking energy and carbon flows across material production, transportation, and maintenance processes. By comparing six asphalt pavement maintenance technologies in Xinjiang, the research reveals that milling and resurfacing (MR) exhibits the highest energy consumption 250,809 MJ/103 m2) and carbon emissions (15,095.67 kg CO2/103 m2), while preventive techniques like hot asphalt grouting reduce emissions by up to 87%. The PLCA approach uncovers a critical insight: 40–60% of total emissions originate from the raw material production phase, with cement and asphalt identified as primary contributors. This granular analysis, unique in regional road maintenance research, challenges traditional assumptions and emphasizes the necessity of upstream intervention. By contrasting reactive and preventive strategies, the study validates that early-stage maintenance aligns seamlessly with circular economy principles. Tailored to a local arid climate and vast transportation network, the study concludes that prioritizing preventive maintenance, adopting low-carbon materials, and optimizing logistics can significantly decarbonize road infrastructure. These region-specific strategies, underpinned by the novel application of PLCA, not only provide actionable guidance for local policymakers but also offer a replicable framework for sustainable road development worldwide, bridging the gap between scientific research and practical decarbonization efforts.

Asphalt pavement cracking represents a prevalent form of deterioration that significantly compromises road performance and safety under the combined effects of environmental factors and traffic loading. Crack sealing has emerged as a widely adopted and cost-effective preventive maintenance strategy that restores the pavement’s structural integrity and extends service life. This paper presents a systematic review of the development of crack sealing technology, conducts a comparative analysis of conventional sealing materials (including emulsified asphalt, hot-applied asphalt, polymer-modified asphalt, and rubber-modified asphalt), and examines the existing performance evaluation methodologies. Critical failure mechanisms are thoroughly investigated, including interfacial bond failure resulting from construction defects, material aging and degradation, hydrodynamic scouring effects, and thermal cycling impacts. Additionally, this review examines advanced sensing methodologies for detecting premature sealant failure, encompassing both non-destructive testing techniques and active sensing technologies utilizing intelligent crack sealing materials with embedded monitoring capabilities. Based on current research gaps, this paper identifies future research directions to guide the development of intelligent and sustainable asphalt pavement crack repair technologies. The proposed research framework provides valuable insights for researchers and practitioners seeking to improve the long-term effectiveness of pavement maintenance strategies.