Pavement Damage Research Articles

Pavement crack detection based on depth-supervision FRRN model

Timely detection of early pavement distress is crucial for effective pavement maintenance. A promising approach involves the segmenting the road surface cracks area from its images. However, the previously employed full-resolution residual network (FRRN), when employing a large number of full-resolution residual units (FRRUs), is susceptible to challenges such as exploding and vanishing gradients. Addressing these issues, this paper has introduced a depth supervision mechanism into FRRN, leading to the development of the Depth Supervision FRRN (DSFRRN) model, designed specifically for accurate segmentation of cracks in road images. This model enhances nonlinear expressiveness while retaining essential features, achieving high precision in pavement crack segmentation. Through experimental validation using a dataset comprising 1565 images of damaged pavements, the result demonstrate that DSFRRN outperforms the original FRRN in terms of both F1 score and Jaccard index, accurately delineating cracks. Consequently, the automated pavement damage detection method presented herein facilitates cost-effective and efficient pavement inspection, contributing to the safeguarding of roadway surfaces and transportation infrastructure.

Real-Time Monitoring of Road Networks for Pavement Damage Detection Based on Preprocessing and Neural Networks

This paper presents a novel multi-initialization model for recognizing road surface damage, e.g. potholes and cracks, on video using convolutional neural networks (CNNs) in real-time for fast damage recognition. The model is trained by the latest Road Damage Detection dataset, which includes four types of road damage. In addition, the CNN model is updated using pseudo-labeled images from semi-learned methods to improve the performance of the pavement damage detection technique. This study describes the use of the YOLO architecture and optimizes it according to the selected parameters, demonstrating high efficiency and accuracy. The results obtained can enhance the safety and efficiency of road pavement and, hence, its traffic quality and contribute to decision-making for the maintenance and restoration of road infrastructure.

Design of Two-Layer Roller Compacted Concrete with Joints by KENSLAB

Roller compacted concrete (RCC) is a type of rigid pavement that is designed in a similar way to conventional concrete pavements reflecting its mode of failure. The thickness design of RCC pavements is based on keeping the flexural stresses and fatigue damage in the pavement caused by wheel loads within allowable limits. As in all jointed concrete pavements, there is a greater effect from loads placed along edges and less at interior locations in the pavement. Joints in RCC pavement are clearly critical areas and form weak points. RCC is typically constructed with saw-cut joints to prevent random cracking, improve the appearance of the pavement surface, and maintain the highest possible load transfer across the joints. This paper presents an approach to designing the thickness of a two-layer RCC with induced cracks (i.e., joints) based on load transfer stiffness and fatigue damage, using the KENSLAB program to obtain pavement life, predict joint deterioration, and pavement damage.

Pore water pressure dynamic response in asphalt mixture: A measurement system development

The pore water pressure dynamic response in asphalt pavements is complex and distinct, which is closely related to pavement damage and seepage characteristics. However, existing measurement methods often don’t simulate the dynamic pore water pressure realistically and combine with measuring position and pavement saturation adequately. This study develops a system to measure pore water pressure in asphalt mixtures under dynamic loading. It simultaneously considers vehicle-pavement interactions, sensors embedding and pavement saturation. The test results shows that the output equivalent loads accuracy of the system reaches 80 % and higher. The noise interference from air is identified, and the FFT digital filters are designed to minimize it. The application result shows pore water pressure response varies under different loading modes. The pressure rapidly rises, peaks, then diminishes to 0 under intermittent loading, while it rebounds after decreasing but never reaches 0 under continuous loading. A turning point at 9.8 % void ratio is noted, where denser mixtures exhibit more hysteresis and longer dissipation times, suggesting increased resistance to water transfer.

Implementation of a low-cost comprehensive pavement inspection system

Assessing the condition of roads is crucial to the maintenance and rehabilitation process as a country's progress is closely linked to its transport infrastructure. Therefore, it is essential to have well-maintained roads and to be able to control and monitor them properly. Technological advancements have transformed the way pavement inspections are carried out. This study presents an innovative approach that combines stereo cameras and a GPS module for efficient and accurate data collection. This integration of low-cost technologies provides a detailed three-dimensional view of pavements, complemented by accurate geospatial information. The experimental results showed that the 3D images of pavement damage had a relative volume measurement error of 0.80 %. Unlike traditional systems such as LIDAR and ground-penetrating radar, which involve more expensive technologies, the proposed method offers a cost-effective solution. This methodology not only simplifies the inspection process but also improves the planning and execution of road maintenance and repair activities. Its low cost makes it a viable option for various projects and applications in road infrastructure.

Pavement damage characteristics in the permafrost regions based on UAV images and airborne LiDAR data

The rapid degradation of “Xing'an-Baikal permafrost” in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan section (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

Centrifuge tests study on settlement and damage modes of bridge approaches using deep-seated slab

The issue of bridge end bumps is a critical concern in the failure of bridge and bridge approaches. A series of novel centrifuge tests utilizing a ring model box were conducted to investigate settlement and its induced damages at the bridge approach. A new mitigation method, the deep-seated slab, for bridge end bumps was modeled in the test. This study analyzed the decisive role of pavement stiffness, soil modulus, and load cycles on deformation from the perspective of structure-soil interaction under standard traffic load conditions. The test results show that when deep-seated slabs are used, the deformation of the bridge approach follows an exponential decay pattern, eventually stabilizing after approximately one slab length. Furthermore, the upper and lower bridges exhibit distinct damage modes, i.e., the bridge damage by wheel collision at the upper bridge and the pavement damage by wheel impact at the lower bridge. The damage zone on the pavement is approximately 1.7 times the wheel width and the damage zone on the bridge 2.6 times. Finally, a predictive model for the deformation of bridge approaches was proposed, considering the effect of pavement stiffness, subgrade soil modulus, and load cycles. The relationship between the deformation and the three normalized variables conforms to the quadratic polynomial function in matrix form.

Development of Pavement Deterioration Models Using Markov Chain Process

A common phenomenon in developing countries is that the function of the pavement in the road network will experience structural damage before the completion of life is reached, and the uncertainty of pavement damage is difficult to predict. Planning for maintenance treatment depends on the accuracy of predicting future pavement performance and observing current conditions. This study aims to apply the Markovian probability operational research process to develop a decision support system predicting future pavement conditions. Furthermore, it determines policies and effectiveness in managing and maintaining roads. A standard approach that can be used by observing the history of pavement damage from year to year is to estimate the transition probability as a Markovian-based performance prediction model. The results show that the application of the model is quite optimal, changes in pavement conditions after repair can be easily compared with an increase in good condition, reaching 92.8%. Routinely and consistently handling road deterioration will give favorable results regarding pavement condition value. This will ease in the management of the road network and the accomplishment of the optimal maintenance and repair policies. Doi: 10.28991/CEJ-2024-010-09-012 Full Text: PDF

YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism

YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism

Investigating the impact of temporal instability in smart roadway retrofitting on terrain-related crash injury severity

The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.

Understanding Toughening Mechanisms and Damage Behavior in Hybrid-Fiber-Modified Mixtures Using Digital Imaging

Pavement cracking is a primary cause of early damage in asphalt pavements, and fiber-reinforcement technology is an effective method for enhancing the anti-cracking performance of pavement mixtures. However, due to the multi-scale dispersed structure of pavement mixtures, it is challenging to address cracking and damage with a single fiber type or fibers of the same scale. To investigate the toughening mechanisms and damage behavior of hybrid-fiber-modified mixtures, we analyzed the fracture process and damage behavior of these mixtures using a combination of basalt fiber and calcium sulfate whisker hybrid fiber modification, along with semicircular bending tests. Additionally, digital imaging was employed to examine the fracture interface characteristics, revealing the toughening mechanisms at play. The results demonstrated that basalt fibers effectively broaden the toughness range of the modified mixture at the same temperature, reduce mixture stiffness, increase residual load at the same displacement, and improve crack resistance in the mixture matrix. While calcium sulfate whiskers enhanced the peak load of the mixture, their high stiffness modulus was found to be detrimental to the mixture’s crack toughness. The fracture interface analysis indicated that the three-dimensionally distributed fibers form a spatial network within the mixture, restricting the relative movement of cement and aggregate, delaying crack propagation, and significantly improving the overall crack resistance of the mixture.

Comparative Study of Airport Layer Thickness Planning Between US Army Corp Graphical Method and Federal Aviation Administration (FAA) PCN-ACN and PCR-ACR Method

Airport pavement is designed following the US Corporation of Engineers method or better known as the California Bearing Ratio (CBR) method and the FAA (Federal Aviation Administration) method which issues regulations for calculating airport pavement structures, namely AC (Advisory Circular) 150_5320_6D which same as the method CBR. In 2021, the FAA issued a standard for calculating airport pavement structures, namely AC (Advisory Circular) 150_5320_6G which uses the FAARFIELD assistance program. The difference in pavement thickness is at most 7.6 cm, on the flexible pavement type base course. In the FAARFIELD Auxiliary Program, all aircraft loads are taken into account as a contributor to pavement damage indicated by the CDF value that can accommodate aircraft loads, in contrast to the graphical method where aircraft are converted to design aircraft. The thickness of the base course using the graphical method is greater than that of the FAARFIELD Assistance Program, this is because when performing calculations, the initial base course value is the minimum value based on the minimum base course table for the use of top foundation layer material (AC No.150_5320_6G). The thickness of the surface course pavement is the same according to FAA provisions for the critical thickness of the surface course which is 4 in or 102 mm.

Mechanism and structure of micro-nanofluid directional heat conduction in asphalt pavement in plateau permafrost regions

Solar radiation in plateau permafrost regions is strong. The asphalt pavement strongly absorbs and slowly dissipates heat, leading to significant heat accumulation on the pavement. This accumulation disturbs the underlying permafrost and eventually causes serious pavement damage. To improve the heat resistance and dissipation capabilities of asphalt pavement, a nanofluid directional heat conduction structure (N-DHCS) was suggested and analyzed in this paper. The designed structure can resist heat in the daytime due to the low thermal conductivity of liquid and dissipates heat at night through natural convection. The finite element method and laboratory irradiation experiment were employed to performed thermal analyses of N-DHCS. The results demonstrated that establishing the N-DHCS in asphalt pavements can enhance active heat dissipation capacity, which is beneficial for protecting the frozen soil in plateau permafrost regions.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Study on self-healing performance of microcracks in SBS/waste rubber powder composite-modified asphalt based on molecular dynamics simulation

The internal microcracks in asphalt materials can heal spontaneously under suitable conditions, and understanding the characteristics of the self-healing behavior of asphalt materials is crucial for evaluating the fatigue life of asphalt pavements. In this paper, a molecular model of modified asphalt microcracks was created by introducing modifier molecules into the virgin asphalt model, and the self-healing process of the asphalt microcrack model under different healing conditions was investigated, which revealed the effects of modifier materials and healing temperatures on the healing performance of asphalt. The results show that the effect of healing temperature on asphalt healing performance is more significant than the modifier material, the relative concentration of molecules at the microcrack healing and the healing temperature is positively correlated; asphalt material healing free volume fraction with the increase in healing temperature gradually increases, the incorporation of modifiers can reduce the free volume fraction growth caused by high-temperature healing, improve the healing stability of modified asphalt. The asphalt ductility specimen crack healing test shows that when the healing temperature exceeds 45 ℃, there is a decreased increase in self-healing rate; taking into account the high-temperature healing is easy to make the asphalt material produce permanent deformation, this paper determines the optimal healing temperature in the vicinity of 48 ℃. The research results have certain reference and application values for asphalt pavement damage repair.

Comparison between ACN–PCN and ACR–PCR for rigid airport pavement with case study

This technical note analyses the new Aircraft Classification Rating–Pavement Classification Rating (ACR–PCR) system with a case study. The literatures and case studies showed that anomalies exist between results of pavement structural evaluation of rigid airport pavements using the pavement thickness design (PTD) method and the Aircraft Classification Number–Pavement Classification Number (ACN–PCN) method. In some situations, the ACN–PCN system provides a conservative assessment of pavement damage which will likely overestimate the load impact of aircraft with complex gear configuration. The International Civil Aviation Organisation (ICAO) introduced a new system, the ACR-PCR, which applies layered elastic analysis to estimate pavement bearing capacity. Using a case study from an airport in Southeast Asia, the anomalies between the designed pavement bearing strength of the PTD method and the ACN–PCN method were discussed. The ACR–PCR system can provide pavement structural evaluation results that are consistent with the PTD results.

Perencanaan Tebal Overlay dengan Alat Benkelman Beam dan Falling Weight Deflectometer

In a certain period, the road pavement will be damaged so this pavement needs to be overlay. There is pavement damage that needs overlay activity in the highway, so the pavement is needed to maintain. This study is performed to find the overlay thick of pavement (overlay) by using Falling Weight Deflectometer (FWD) and Benkelman Beam (BB) tools. Overlay thick design is based on Design of Overlay Thick of Flexible Pavement by Deflection Method (Pd T-05-2005-B). The segment of this study is in Jalan Banda Aceh - Medan highway, the data that taken is from the same point between Falling Weight Deflectometer (FWD) and Benkelman Beam (BB). To design the thickness, this investigation needs the result deflection of Falling Weight Deflectometer (FWD) and Benkelman Beam (BB), then the value result is corrected. After the obtain corrected deflection value, the next process is calculating uniformty factor (FK), representative deflection (Dsbl ov), design deflection (Dstl ov), overlay thick (Ho), factor of overlay thickness (Fo), and corrected overlay thick (HT). The result of this study is the overlay thick from Falling Weight Deflectometer (FWD) and Benkelman Beam (BB), it is 5,235 cm for Falling Weight Deflectometer (FWD) and 3,722 cm for Benkelman Beam (BB). The result difference between Falling Weight Deflectometer (FWD) and Benkelman Beam (BB) is 1.513 cm or 28.90% in the percentage.

Interfacial adhesion features of high-viscosity asphalt and aggregate under water conditioning: Micro perspective and molecular simulation

Interface properties between asphalt and aggregate intimately affect the service performance and long-term durability of asphalt pavement. This study seeks to explore the interfacial adhesion features of high-viscosity asphalt (HVA)-aggregate under water conditioning to provide the understanding for the moisture damage of HVA-aggregate interface and asphalt pavement. To this end, the HVA was initially suffered from different durations of water conditioning, and atomic force microscopy (AFM) test was employed to gather the microscopic phase structure and adhesion force of water-conditioned HVA and aggregate, to analyze the adhesion variations of HVA-aggregate interface. Further, molecular dynamics method was used to simulate the changing process and influencing mechanism of adhesion effect at the HVA-aggregate interface under water immersion. The findings indicated that with the increasing water immersion, Catana phase of HVAs vanished, and concave and convex structures appeared on the surface, along with pit groove dispersion phase structures. Longer immersion time resulted in augmented surface roughness of HVA and a more dispersed height distribution in the area domain, most notably during the initial stages of water immersion. Aging increased sensitivity to immersion duration in terms of surface roughness, mechanical properties and adhesion ability of HVAs. Water immersion had a diminishing effect on the mechanical properties of HVA at different aging degrees, although it increased the micro-mechanical modulus of HVAs and impaired the interface adhesion with aggregates. Under water immersion, the phase structure and mechanical properties of HVAs and the interface adhesion indexes all distinguished a prominent negative Pearson correlation (correlation coefficient |R| > 0.7000), and these factors were critical for HVA-aggregate interface adhesion, as alterations in surface phase structure and micro-mechanical traits of HVAs triggered variations in the HVA-aggregate interface adhesion. In addition, water content in the interface layer, simulation temperature and mineral anisotropy significantly affected the interfacial adhesion of HVA and aggregate in water environment. The introduction of water and the rise of temperature considerably weakened the interfacial adhesion effect of asphalt-aggregate system, and the interface adhesion failure occurred once the water layer thickness surpassed 5 Å. Comparatively, the HVA-calcite system demonstrated better interfacial adhesion competence than the HVA-α-quartz system, and the HVA-calcite {1 0 4} system showed the optimal interfacial adhesion.

Impacts of heavy-duty electric trucks on flexible pavements

ABSTRACT The transportation sector is responsible for significant greenhouse gas (GHG) emissions, with medium- and heavy-duty trucks (MHDTs) being major contributors. In response, medium- and heavy-duty electric trucks (MHDETs) are being explored as zero-emission alternatives. However, the weight of high-capacity batteries needed for long-haul trips and heavy loads could increase the axle loads as well as the gross vehicle weight (GVW) of heavy-duty electric trucks (HDETs), leading to extra damage on flexible highway pavements. This study simulated the GVW increment of HDETs and assessed its effects on pavement damage, environmental impacts, and life-cycle costs of four typical highway pavement structures in Illinois (known as thick–weak, thick–strong, thin–weak, and thin–strong). The utilisation of electric trucks resulted in significant reductions in diesel combustion related global warming potential (GWP) emissions and costs (in $2022) by 73% and 11.5%, respectively. However, the results demonstrated that with a 100% penetration of HDETs carrying an additional weight of 8 kips per truck, the pavement deterioration accelerated compared to traffic with conventional trucks. As a consequence, GWP and costs were reduced by 69% and 10.6%, respectively.

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.