Pavement Age Research Articles

Real-time pavement distress detection based on deep learning and visual sensors

Pavement ageing significantly affects traffic safety and requires timely maintenance. However, accurately identifying pavement conditions from images captured by in-vehicle cameras remains challenging. This paper proposes a lightweight pavement distress detection method, MASL-YOLO. To address multi-scale defects, the method employs multi-scale channel mix convolution to enhance feature fusion, thereby improving the sensitivity to pavement damage. A multi-path aggregation attention mechanism and deformable convolutions are used to enhance adaptive sampling, strengthening feature extraction in the backbone. Integrating a large-kernel separable structure into the spatial pyramid pooling fusion further enhances the network's generalisation capability in complex environments. Experiments show that MASL-YOLO achieves an mAP of 85.5%, a 5.1% improvement over YOLOv8n and operates at 73.7 frames per second, meeting real-time requirements. The model, integrated with the RealSense D455 visual sensor and the GNSS module on the detection vehicle, achieved fast and effective pavement distress detection in field tests.

Enhancement of Anti-UV Aging Performance of Asphalt Modified by UV-531/Pigment Violet Composite Light Stabilizers

Ultraviolet (UV) radiation accelerates the aging of asphalt pavements and shortens the service life of the pavement. To effectively mitigate the impact of UV aging on asphalt performance, a novel composite anti-UV aging agent, 2-hydroxy-4-n-octyoxybenzophenone/pigment violet (UV-531/PV), was developed. After UV-accelerated aging, the modified asphalt samples were characterized by conventional performance tests, Fourier transform infrared spectroscopy (FTIR), gel permeation chromatography (GPC), and a dynamic shear rheometer (DSR). The results show that UV-531/PV-composite-modified asphalt maintains excellent conventional properties after UV aging. The FTIR testing showed that the changes in carbonyl index (Ic=o) and sulfoxide index (Is=o) of the composite-modified asphalt were significantly smaller than those of the matrix asphalt, indicating the less oxidation degree of the composite-modified asphalt. The GPC test results showed that the change in molecular weight of the composite-modified asphalt after UV aging was less than that of the matrix asphalt. DSR results showed that UV-531/PV-modified asphalt exhibited higher viscoelasticity and higher rutting resistance than unmodified asphalt. This study proposes a new method for preparing anti-UV aging asphalt, which can be used for micro-surfacing, fog sealing or ultra-thin overlay on road surfaces.

Utility Cut Impact Assessment and Fee Development Using Pavement Management System

The purpose of this study is to estimate any damage caused by utility cuts to pavements and develop a fee schedule to recover the costs associated with such damage using the City of Pacifica, CA, U.S.’s pavement management system (PMS). To accomplish this, the study looked at the functional deterioration of pavements resulting from utility cuts. The City of Pacifica’s PMS containing a vast dataset of pavement distress spanning 15 years, comprising thousands of data points encompassing various pavement ages and conditions, was analyzed to assess the impact of utility cuts. The findings from this study include: 1) Pavements with cuts of any size deteriorate more than pavements without cuts across all age groups with one exception for older residentials with small cuts; 2) On average, pavement condition index drops by 30% if the cut area is greater than 10% of the section area; and 3) Utility cuts inflict greater damage on newer pavements (<10 years old) compared with older ones (≥10 years old). Consequently, new pavements experience an average percent reduction of approximately 33% in their remaining service life, while older pavements suffer a reduction of about 17%. The statistical analyses indicate that utility cuts caused significant damage, and supported a tiered utility cut fee structure based on functional classification, age of pavement, and size of the cut. The resulting fee schedule is based on 2021 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Example fee calculations are provided.

Machine learning-based models for thermal cracking prediction of flexible pavements

Prediction of transverse cracking is crucial for pavement practitioners and decision-makers to prognosticate the cracking development and allocate maintenance budgets. This research investigates thermal crack predictions, using four various machine learning techniques: the support vector regression (SVR), the regression artificial neural network (RNN), the Gaussian process regression (GPR) and the least-square boost ensemble method (LSBoost). Predictions were based on 214 pavement sections with 1262 data points from the Long-Term Pavement Performance (LTPP) database. The models are developed using 20 variables representing age, binder, mix, aggregate and climate properties. The LSBoost ML algorithm exhibited the best performance, with a coefficient of determination (R2) of 0.926 for training and 0.727 for testing. Sensitivity analysis revealed that pavement age is the predominant factor in transverse crack prediction followed by the freezing index (FI). Overall, climatic parameters played an important role in transverse crack predictions compared to the mix and binder properties.

Cosmogenic 10Be‐ and 21Ne‐based model exposure ages of desert pavements in the Thar Desert, India

AbstractThe Thar Desert, India has desert pavements comprising angular‐subangular to well‐rounded gravels at marginally higher elevations than the surrounding terrain. Sedimentological and geomorphic analyses suggest that the pavements are lags of weathered Mesozoic and older bedrock. The presence of Palaeolithic artefacts on the pavement surfaces and occasionally within their matrix was used to infer their antiquity and landscape stability.This study presents the first surface exposure ages based on cosmic‐ray‐produced 10Be and 21Ne for pavements at four sites in the Thar Desert, viz. Bhojka, Hamira, Solanki and Jayal. The computation of model exposure ages assumed that (a) the gravels were derived from cemented conglomerates, uplifted by tectonics and thereafter disintegrated by climate, and (b) cosmogenic nuclide production in the gravels began when the conglomerates approached the surface and, continued during their disintegration, gravity sliding of individual gravels and storage, until the present. Assuming an average burial depth of 50 cm, 21Ne and 10Be data provide ages ranging from 1.30 to 2.92 Ma and 1.11 to 5.4 Ma, respectively, for the two nuclides.Published electron spin resonance ages of Thar calcretes suggest the presence of water and extreme seasonality since 1.54 Ma. Such conditions facilitated the mobilization and precipitation of carbonates. The pavement ages and the minimum age of the conglomerate at 2.51 Ma extend the time of such desertic conditions to > 2.51 Ma and suggest that the initiation of desertic conditions in the Thar was possibly linked to global aridity beginning around 3.6 Ma.Depending on assumptions, cosmic ray surface exposure (10Be) ages at Jayal range between 0.76 and 2.43 Ma. In the context of the Indian Palaeolithic, the presence of tools on the gravel surfaces and within dunes, suggests frequent occupation of this region from at least 0.76 Ma and, parallels Early to Middle Pleistocene Acheulian assemblages from Southern India.

Road traffic noise prediction model "ASJ RTN-Model 2023" proposed by the Acoustical Society of Japan -Part 3: Case studies of prediction accuracy on Japanese highways and roundabouts

The Acoustical Society of Japan (ASJ) has published a new version of road traffic noise prediction model "ASJ RTN-Model 2023" this April. Some case studies of the prediction accuracy, namely the correspondence between the predicted and measured value, using the new model is presented in this paper, as one of the three papers with same main title in this congress. The case study on Japanese highways was examined using the measurement at the straight sections. The LAeq at the measurement point was calculated using obtained traffic data for each lane and the age of pavement. The mean difference between predicted and measured LAeq were less than 3 dB. The case study at the vicinity of roundabouts was also examined using the prediction method newly introduced in the ASJ RTN-Model 2023. The case measurements were conducted at two round abouts in Japan, and the predicted LAeq corresponded to within 3 dB of the measured values.

A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

Nuclear Magnetic Resonance (NMR) Assessment of Bio and Crude Oil-Based Rejuvenation

Asphalt binders in pavements lose their stability through aging and eventually fail in the field. Using nuclear magnetic resonance (NMR) to monitor the primary longitudinal relaxation time of asphalt samples and the ratio of material that carries this primary relaxation time has been shown to indicate the impact of ultraviolet (UV) radiation on the aging of asphalt pavements. Longitudinal NMR relaxation was used to investigate two types of proposed asphalt rejuvenators, a bio-oil-based rejuvenator and a crude-oil-based rejuvenator. Two different binders with the performance grades (PG) 64-22 and 76-22 were considered for their interactions with the rejuvenators. After 72 h of exposure to intense UV radiation, specifically designed NMR relaxometry experiments were applied to compare the rejuvenation capabilities of the two rejuvenator samples. The crude oil-based rejuvenator was found to exhibit relaxation times similar to the binder samples while the bio-based material showed relaxation times that pointed to different nuclear hydrogen environments. Both rejuvenators reduced the primary relaxation time of the PG 76-22 binder, which indicates that their stiffness was reduced. Both types of rejuvenators also seemed to prevent the effects of UV aging. Two mechanisms of rejuvenation were identified by NMR relaxometry. The primary relaxation time can be used to indicate a change in stiffness while the primary ratio of the material is tied to oxidative aging. Oxidative aging creates distinct hydrogen environments due to asphaltene aggregation. The bio-based rejuvenator only reduced the binder’s stiffness, while the crude oil-based rejuvenator also reduced the aggregation of asphaltenes. Consequently, the bio-based rejuvenator could be classified as an asphalt softener, while the oil-based material acted like a true rejuvenator.

Predicting pavement surface conditions through artificial neural networks

In this study, six distinct types of pavement distress were evaluated using the pavement condition index (PCI) rating: ravelling, rutting, cracking (including alligator, transverse and longitudinal cracks), shoving, patching and potholes. The severity levels of these distress types are utilised for classification, and these levels are subsequently converted into PCI values. The proposed methodology incorporates a thorough analysis, considering various variables such as average daily traffic, land-use classification, number of lanes, road width and length, pavement age, road alignment, vehicle type and weather conditions in Berzhite, Tirana, Albania. To predict PCI values, an artificial neural network model was employed due to its versatility in handling diverse inputs. Key performance metrics, including R2, mean squared error and mean absolute error, are used for model assessment. Notably, model 2, featuring two hidden layers, exhibits superior performance over model 1. However, due to size constraints in testing and validation datasets in this study, the accuracy of the model is somewhat limited. Future directions for research involve expanding the dataset to enhance model accuracy and incorporating advanced features to capture a more complex comprehension of pavement conditions.

Research Progress of Optical Satellite Remote Sensing Monitoring Asphalt Pavement Aging

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods.

Use of antioxidants to retard aging of bitumen: A review.

Oxidative aging of bitumen is an inevitable and irreversible phenomenon. Exposure to detrimental factors such as sunlight, oxygen, and UV radiations accelerates the aging of bitumen and bituminous pavement. The aging process induces hardening and embrittlement in bitumen, leading to premature pavement failure. Therefore, for constructing sustainable long-lasting pavements anti-aging additives are used. Among the available additives, the use of antioxidants has emerged as a promising solution to mitigate the aging of bitumen. The current review aims to summarise the existing literature for a comprehensive understanding of the effectiveness of these additives as aging inhibitors. It provides an overview of the chemical pathway involved during bitumen oxidation and various quantification techniques to measure the effect of aging. This review also highlights the potential use of antioxidants in bitumen and elaborates on the working mechanism of different types of antioxidants to prevent bitumen aging. Further, the effect of modification in bitumen at micro, macro, and mixture levels are discussed. Additionally, cost analysis and future prospects on the use of antioxidants for bitumen are presented.

Effect of Salt Solution Environment on the Aging of Styrene-Butadiene-Styrene (SBS)-Modified Asphalt.

The aim of this paper is to investigate the aging mechanism of asphalt in the sea salt erosion environment from a rheological point of view. In order to simulate the real pavement aging process in the sea salt erosion environment, base asphalt and Styrene-Butadiene-Styrene (SBS)-modified asphalt were selected for salt environment aging tests. The asphalt samples were aged via a thin film oven test (TFOT) and a pressure aging vessel (PAV) test. Then, thermo-oxidizing conditions were created after the samples were immersed in salt solution, mixed with four different concentrations of sodium chloride (NaCl) and sodium sulphate (Na2SO4), to investigate the aging state of asphalt. Temperature scan (TS), frequency scan (FS), and multiple stress creep and recovery (MSCR) tests performed using a Dynamic Shear Rheometer (DSR) were used to investigate the effects on the rheological properties of aged asphalt in a salt environment. The results showed that both base asphalt and SBS-modified asphalt were aged to different degrees under mixed salt solutions. The two asphalt samples aged in a salt environment showed increased hardness. SBS-modified asphalt exhibited higher aging resistance compared with base asphalt in the sea salt environment. However, due to the degradation of the SBS modifier and the aging of base asphalt, the properties of the SBS-modified asphalt showed more obvious complexity with changes in salt solution concentrations.

Comparative analysis of several short-term aging simulation methods and their impact on long-term aging performance of asphalt binders

The aging of the asphalt pavement is an essential factor to consider when constructing a road. Several methods, such as the Thin Film Oven Test (TFOT) and Rolling Thin Film Oven Test (RTFOT), have been used to simulate the short-term aging of asphalt binders. Based on the wide use of polymer-modified asphalt binder and the idea of research facilitation, 5 hours of Pressure Aging Vessel (5 h PAV) was proposed as an alternative to previous short-term aging methods of asphalt binders. However, the efficacy of this technique still needs to be wholly explored in all aspects and requires advanced research. The frequency sweep, temperature sweep, and Multiple Stress Creep Recovery tests were performed to evaluate the high-temperature performance of asphalt binders under aging conditions. In addition, the relaxation test was carried out to determine the low-temperature stress relaxation change and the fatigue cracking resistance of asphalt binders. The Chemical test explored the chemical change of asphalt binders under different aging conditions. The findings revealed that, in the case of neat asphalt binders N50, N70, and N90, the maximum disparity between the aging conditions of TFOT and 5 h PAV was found to be below 6 %. However, the difference between the aging conditions of 5 h PAV and RTFOT was observed to be approximately 20 %. Then, 5 h PAV can be highly recommended as an alternative to replacing TFOT. For SBS and high viscosity-modified asphalt binders HVMA-I and HVMA-II, the maximum gap between 5 h PAV and TFOT was estimated to be approximately 19.71 %. However, the gap between 5 h PAV and RTFOT was estimated to be less than 7 %. Then, 5 h PAV can be an alternative to replace RTFOT for modified asphalt binders if necessary. This study offers a thorough explanation and dispels doubts regarding the suitability of the 5-hour PAV as an alternative for simulating the short-term aging of asphalt binders, thereby enhancing research facilitation. Furthermore, the research delineates the impact of various short-term aging simulation methods on the long-term aging properties of asphalt binders, including the variation rates between them.

Laboratory-accelerated simulation and calibration method for ultraviolet aging of asphalt binders based on radiation equivalent conversion

Ultraviolet (UV) radiation is a significant environmental factor that accelerates the aging of asphalt pavements. To address the gap in the equivalence conversion relationship between laboratory simulation parameters and field environmental factors for asphalt pavement aging in the Tibet area of China, this study investigates the relationship between laboratory simulations and field service for asphalt aging. The results indicate that using macroscopic rheological parameters (rutting factor (G*/sin δ), average recovery rate (R), and average non-recoverable creep compliance (Jnr)) and microscopic parameters (sulfoxide index (SI) and glass transition temperature (Tg)) as quantitative indicators can evaluate the equivalent degrees of laboratory and field aging. Furthermore, this study introduces a correction coefficient for laboratory-accelerated simulated aging to improve the accuracy of the equivalent radiation conversion formula. The correction coefficient range for 90# matrix asphalt is 1.36–2.05, while the range for SBS modified asphalt is 0.82–1.64. This study provides a valuable database for establishing the relationship between laboratory simulations and field service for asphalt aging and offers a parameter basis and technical support for designing durable asphalt pavements in areas with intense UV radiation.

Analysis and Design of Flexible Pavement and Cost Comparison with Cement-Treated Base and Subbase Material

Road transport is important in economic, social; and a country’s overall development. Various commercial vehicle classes have utilized roads with different axle configurations and loads. Due to the cumulative effect of wheel loads and the aging of bitumen binder flexible pavement starts deteriorating. The bitumen pavement failure is mainly due to fatigue cracking as well as rutting deformation. Thus, proper load analysis for the intended period of pavement planned life needs to be analyzed and the pavement designed accordingly. The development of software such as IITPAVE enables the calculation of stress and strain values at required points across various pavement layers. The project's goal is to gather traffic data as well as soil CBR values from the field to design a flexible pavement following IRC: 37-2018 guidelines. The pavement would be analyzed using IITPAVE Software to obtain the actual strains at required critical points. Pavement design is influenced by factors such as properties of subgrade soil, wheel load, climatic conditions, and pavement materials. Pavements are built according to IRC guidelines and MORTH specifications. Excessive strain along with the deformation at the critical locations in pavement are the primary causes of bitumen pavement failure. This research aims to design a flexible pavement and compare the cost of traditional pavement layer composition with cement-treated base as well as sub-base materials. Keywords: Flexible Pavement, Cement treated base (CTB) and Subbase (CTSB), Cost Comparison.

Performance prediction for network-level flexible pavements using an optimised ANN approach

ABSTRACT Considering that the road network shares traffic flow as a system, this study aimed to predict the network-level flexible pavement performance, using an optimised artificial neural network (ANN) approach to predict the pavement performance of each individual road, and the Ford-Fulkerson algorithm to determine the weight coefficients of roads. ANNs were developed to predict distress conditions, functional conditions and structural performance using the inputs of the pavement age and structural, traffic and climatic conditions. ANNs were trained with Long-Term Pavement Performance (LTPP) program data, and almost all the coefficient of determination values between ANN outputs and measured results are larger than 0.9. This ANN approach was optimised by establishing grey models to provide predictive short-term performance data for training ANNs, and employing Kalman Filter to modify the long-term performance prediction. Applications in typical LTPP sections validated the effectiveness of the optimisation method. This study used the reduction in road network capacity due to the assumed unavailability of a certain road to quantify its role (weight coefficient), which was calculated using the Ford-Fulkerson algorithm. This network-level performance prediction approach was applied in a 25-expressways network in Shanghai, which validated the good generalisation capabilities of ANNs and feasibility of the Ford-Fulkerson algorithm.

Review of Pavement Crack Types and Their Significance

This essay examines the significant importance of pavement distress detection, specifically in the capacity to identify and categorize various forms of cracks, in order to uphold road safety. This study explores the impact of technical breakthroughs on the detection and classification of pavement cracks, leading to notable improvements in road maintenance and accident prevention. The essay additionally explores a range of studies that indicate a connection between pavement conditions and traffic safety, including the association between pavement age and the likelihood of accidents, as well as the influence on the severity of crashes. This analysis serves to illustrate the paramount significance of maintaining pavement integrity in order to ensure traffic safety. The essay foresees future progress in crack detection techniques, highlighting their crucial role in the construction of safer road networks and the improvement of public safety.

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

This research utilizes the Long-Term Pavement Performance database, focusing on devel-oping a predictive model for flexible pavement performance in the Southern United States. Analyzing 367 pavement sections, this study investigates crucial factors influencing asphaltic concrete (AC) pavement deterioration, such as structural and material components, air voids, compaction density, temperature at laydown, traffic load, precipitation, and freeze–thaw cycles. The objective of this study is to develop a predictive machine learning model for AC pavement wheel path cracking (WpCrAr) and the age at which cracking initiates (WpCrAr) as performance indicators. This study thoroughly investigated three ensemble machine learning models, including random forest, extremely randomized trees (ETR), and extreme gradient boosting (XGBoost). It was observed that XGBoost, optimized using Bayesian methods, emerged as the most effective among the evaluated models, demonstrating good predictive accuracy, with an R2 of 0.79 for WpCrAr and 0.92 for AgeCrack and mean absolute errors of 1.07 and 0.74, respectively. The most important features influencing crack initiation and progression were identified, including equivalent single axle load (ESAL), pavement age, number of layers, precipitation, and freeze–thaw cycles. This paper also showed the impact of pavement material combinations for base and subgrade layers on the delay of crack initiation.

Assessment of asphalt pavement aging condition based on GF-2 high-resolution remote sensing image

Assessment of asphalt pavement aging condition based on GF-2 high-resolution remote sensing image

Numerical analysis of field aging in asphalt pavements via coupling of diffusion process and kinetics aging model

The asphalt aging model is a crucial module in the Mechanistic-Empirical (ME) pavement design programme for asphalt pavement performance prediction. In this study, an analytical aging model that couples diffusion and kinetics aging model (CDKAM) is improved to analyze and predict the field aging of asphalt pavements. The complex modulus (E*) of field cores was used as the aging property to illustrate the derivation of explicit expressions of time-averaged thermal source term ( TST ¯ ( t ) ) and time-averaged diffusion coefficient ( D ¯ ( t ) ). The value of the material-related parameter (M) in TST ¯ ( t ) model differs among different mixtures, which could assess the mixture’s susceptibility to thermal oxidation. The CDKAM with TST ¯ ( t ) and D ¯ ( t ) was used to predict E* of field aging for asphalt pavements in Texas after 22-month service life. The calibrated CDKAM could reasonably quantify the aging extent over time and the aging gradient over depth for studied asphalt pavements.