Pavement Damage Research Articles

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.

Influence of acidity and alkalinity of water environment on water stability of asphalt mixture: Phase II-microscopic erosion mechanism

Acidity and alkalinity of water environments have been proven to influence the water stability of asphalt mixture. However, the relevant influencing mechanism still remains unclear. To fulfill this research gap, this study conducted a series of microscopic tests, aiming at unveiling the microscopic erosion mechanism of the water environment on the water stability of asphalt mixture for better moisture stability achieved. Specifically, the asphalt-aggregate interfacial transition zone (ITZ) samples were prepared by combining #70 asphalt with limestone and granite, respectively, which were then immersed in water solutions at 3 different pH levels (pH = 3.0, 7.0, and 11.0) for 7 days. The chemical composition and microstructure of the asphalt film on the surface of the ITZ samples pre and post-treatment were scrutinized using the Fourier Transform Infrared Spectroscopy (FTIR) and the Atomic Force Microscope (AFM), while the development of microcracks at the asphalt-aggregate interface were identified and traced utilizing the Scanning Electron Microscope (SEM). The test results indicate that after treatment with acidic and alkaline aqueous solutions, the asphalt film's surface is enriched with polar components such as asphaltenes, resins, and aromatic fractions. The migration of these polar components to the surface of the asphalt film reduces the adhesion between asphalt-aggregate, leading to the formation of microcracks at the asphalt-aggregate interface. The most severe crack development occurs in alkaline water environment, followed by acidic water environment. It is interestingly noted that the adhesion at the asphalt-granite interface is improved under the acidic environment. This improvement may be attributed to the covalent nature of silica, which hinders the adsorption of ions from acidic solution into the asphalt film. This hindrance effectively prevents water intrusion at the asphalt-aggregate interface, thereby reducing the adhesion loss. These findings can help elucidate the mechanism of water damage in asphalt pavement exposed to real-world conditions and enhance its water stability with effective countermeasures proposed.

Influence of Sustainable Grout Material on the Moisture Damage of Semi-flexible Pavement

Semi-flexible pavement (SFP), also known as grouted pavement, is a type of pavement structure consisting of a porous asphalt skeleton with air voids between 25 and 35% injected with cementitious grout materials. The special skeleton of SFP provided enhanced durability and resilience, making it a promising option for the construction of road surfaces in high-traffic areas and severe conditions. The main aim of the current research is to investigate the rutting behavior and moisture resilience of SFP-containing sustainable grout using ceramic waste powder (CWP). This research introduced the use of CWP as a replacement for conventional grout (cement) in SFP for the first time. CWP partially replaced cement at ratios of 15, 20, 30, 40, and 50% of the cement weight. Indirect Tensile Strength (ITS) and Hamburg Wheel Tracking Tests (HWTT) were used to evaluate the performance of SFP to reduce the detrimental effect of moisture. The grout modified with CWP shows excellent results in the ITS and Tensile Strength Ratio (TSR), and all modified SFP mixtures give higher values in these tests compared with Control Mix (CM). In the HWTT, the minimum rut depth of the modified SFP was 2.8 mm and 3.10 mm. Compared to CM, rutting decreased by 73%–76% for CWP mixes with 20% and 30% replacement. This indicates the high fluidity of CWP, which enabled it to penetrate all voids in the porous pavement (PA) and form a dense microstructure due to its excellent pozzolanic interaction, making it a strong structure capable of bearing rutting.

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

Experimental evaluation of one and two-directional wicking geotextile stabilized permeable base

Incorporating wicking geotextiles significantly reduces the moisture levels in road bases under saturated and unsaturated conditions, improving the strength of granular materials and reducing water-related pavement damage. Despite the numerous well-documented applications of wicking geotextiles, most practical uses or research predominantly feature wicking fibers oriented in a single direction. Although theoretically incorporating multidirectional wicking fibers should enhance drainage efficiency, empirical studies on the impact of bidirectional fibers on geotextile drainage performance are scarce. A year-long comparative study was conducted in Shenzhen to examine the influence of fiber orientation. In this study, the moisture content in the aggregate base of a permeable sidewalk across three distinct test sections – single-directional, bidirectional, and no wicking geotextile – determined that two-directional wicking geotextiles are superior to their one-directional counterparts in reducing the volumetric water content in the granular base.

The influence of water acidity and alkalinity on the adhesion properties of bitumen-aggregate interface

To examine the influence of water acidity and alkalinity on bitumen-aggregate interfacial adhesion, we measured interaction force between aggregate and bitumen using atomic force microscopy with a 1 mmol/L KCl background solution at pH levels ranging from 2 to 10. Experimental materials included four types of aggregates (basalt, granite, limestone A, and limestone B) and two types of bitumen (Zhenhai 90# matrix and Styrene-Butadiene-Styrene modified). Results showed adhesion rankings: granite < basalt < limestone B < limestone A. Long-range forces increased with higher pH, in line with Derjaguin-Landau-Verwey-Overbeek theory. Acidic conditions reduced repulsive forces between bitumen and aggregates while increasing adhesive forces. Compared with the Zhenhai 90# matrix bitumen, Styrene-Butadiene-Styrene modified bitumen demonstrated superior adhesion. Zeta potential values for bitumen and aggregate consistently decreased with rising pH, reflecting the aggregate-bitumen interaction force, suggesting electrostatic forces' dominance. Boiling water method was used to assess bitumen spalling on different aggregates under varied acid-base conditions, finding a significant correlation between spalling rate and microscopic interaction force. This study offers theoretical support for minimizing water damage in bitumen pavements by shedding light on the adhesion process of bitumen and aggregates in wet settings.

An annotated street view image dataset for automated road damage detection

Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide coverage and regular updates. In this study, a road pavement damage dataset, the Street View Image Dataset for Automated Road Damage Detection (SVRDD), was developed using 8000 street view images acquired from Baidu Maps. Based on these images, over 20,000 damage instances were visually recognized and annotated. These instances were distributed in five administrative districts of Beijing City. Ten well-established object detection algorithms were trained and assessed using the SVRDD dataset. The results have demonstrated the performances of these algorithms in the detection of pavement damages. To the best of our knowledge, SVRDD is the first public dataset based on street view images for pavement damages detection. It can provide reliable data support for future development of deep learning algorithms based on street view images.

Mechanistic-empirical assessment of axle load legal limits on Brazilian roadways

This paper presents a comprehensive mechanistic-empirical assessment of axle load legal limits policy on Brazilian roadways, considering the intricate balance between enhanced freight transportation and potential pavement deterioration. Focusing on the prevailing flexible pavements, the study explores factors such as asphalt concrete stiffness, thickness, and axle-load increments, accounting for variations in tire-load and pressure. The analysis reveals complex relationships between these factors and their impact on pavement distress mechanisms, particularly fatigue cracking and rutting. The investigation highlights the substantial influence of axle load legal limits adjustments, even seemingly minor ones, on pavement damage, ride quality, safety, and operational efficiency. A calibrated model for assessing mechanistic-empirical load equivalency factors (LEFs) was developed, offering insights into the disparities between the model and existing empirical equations used in the Brazilian Pavement Design Guide (BPDG). This comparison underscores the potential underestimation of axle load legal limits adjustments in the current approach, emphasizing the importance of informed decision-making in transportation policy. In conclusion, this study provides critical insights for policymakers and engineers while considering axle load policy adjustments. The findings emphasize the significance of accurate engineering considerations to sustainably balance economic growth, road safety, and efficient freight transportation within Brazil’s evolving road network.

A novel moisture damage detection method for asphalt pavement from GPR signal with CWT and CNN

To locate the moisture damage area in asphalt pavement, an automatic detection method by leveraging the state-of-the-art deep learning approach and continuous wavelet transform (CWT) method from high frequency GPR signal was presented. A numeric simulation and a laboratory experiment were employed to analyze the response of GPR signal in moisture damage area. GPR signals were transferred to time-frequency domain by using a CWT method, before comparing CWT results among normal pavement, moisture and bridge joints. Moisture damage datasets, including 8215 traces of moisture damages, 8215 traces of normal pavement and 7643 traces of bridge joints, were extracted from GPR field survey of bridge pavement with 2.3 GHz antenna. The maximum and mean values of CWT results were obtained from the dataset and used to normalize the CWT dataset. Typical classification models, including Resnet50, ResNet18, ShuffleNet, EfficientNet, NasNetmobile and DenseNet, were trained and compared on the CWT dataset. The result indicates that the CWT features among normal pavement, moisture damage and bridge joints have significant differences. Moreover, the energy distribution in the CWT image can represent the moisture damage depth and width information, proving that the CWT method is effective for extracting moisture damage features from GPR signal. The overall performance of ResNet50 model is best with high accuracy and high efficiency in moisture detection compared to other models. Experimental results proved the promising performance of our approach in detecting moisture damage in asphalt pavement, and this method could also be used for subsurface target detection in other GPR application.

Impact Assessment and Damage Fee Development for Pavement Utility Cuts Using Functional and Structural Field Data

The objectives of this study were: 1) to provide transparency of a recent pavement utility cut impact study which informed the adoption of a pavement utility cut fee in Davis, California; and 2) to present an additional methodology for assessing pavement damage using field data referenced to pavement condition. A total of 24 test sites were selected to represent all functional classifications across the Pavement Condition Index (PCI) spectrum. Functional and structural field data were collected and analyzed. Of the 24 test sites, 67% exhibited both functional and structural damage with 96% exhibiting at least one form of damage. The average PCI reduction resulting from utility cuts was 5.1 points and the corresponding percent reduction in pavement service life was 6%. Of the test sites, 63% exhibited damage 2 ft from the edge of the patch, thus indicating that the zone of influence extends past the current restoration area. The statistical analyses indicated that the pavement utility cuts caused significant damage and supported a tiered utility cut fee structure based on functional classification and PCI. The resulting fee schedule is based on 2022 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Since the negative impact of utility cuts is amplified when there are multiple or large cuts on the same street, and treatments are typically carried out across entire management sections, a full-recovery fee at a threshold of 10% of the management section area is appropriate. Example fee calculations were provided.

In situ mechanical response characteristics of Autonomous Rail Rapid Transit (ART) applied to semi-flexible asphalt pavements

Autonomous Rail Rapid Transit (ART), featuring multi-axles, high tire pressure, and complete channelization, leads to severe rutting on conventional asphalt pavements. Semi-flexible pavement materials (SFP) were applied to ART corridors to mitigate rutting. Mechanical response characteristics were obtained by embedding sensors to understand the interaction between ART and SFP. Results found that the damage of corridor pavement shifted from rutting to top-down transverse cracking after adopting SFP. Nonresidual longitudinal tensile strains primarily caused top-down transverse cracking. The compressive stress at the bottom of corridor pavement caused by ART is 41%, 36%, and 58% higher than that of the BUS, respectively, under the condition of constant speed, acceleration, and deceleration. The maximum strain response caused by ART under a high-temperature environment was ten times higher than that of buses. These findings can provide data support for numerical simulations, material failures, and structural damage of ART corridors. Highlights Mechanical responses of semi-flexible pavement materials influenced by Autonomous Rail Rapid Transit (ART) were first captured using sensors. ART with high tire pressure exhibits a multi-axial cumulative effect in high-temperature conditions, resulting in pavement strains that can be up to ten times those of buses. After adopting semi-flexible pavement, the damage in ART corridors shifted from rutting to top-down transverse cracking. Nonresidual longitudinal tensile strains are the underlying cause of top-down transverse cracking.

Application of the cooling measures in the highway roadbed in permafrost regions of the Qinghai-Tibet Plateau

Permafrost in the Qinghai-Tibet Plateau (QTP) accounts for a total area of 1.05 × 106 km2. Many highway engineerings have been through the permafrost regions of the QTP, including the Qingkang Highway (QKH), Qinghai-Tibet Highway (QTH), and Gonghe-Yushu expressway (GYE), and the Qinghai-Tibet expressway (QTE) will be constructed in the future. Under the action of roadbed construction and global warming, permafrost under roadbed is degraded and causes serious pavement damage. Although many useful cooling measures have been applied to prevent permafrost degradation, their application presents significant challenges on account of the stronger endothermic action of asphalt pavement. This paper summarizes the working principle of existing cooling measures, and ascertains the relationship between pavement damage and ground temperature distribution of permafrost under roadbed. Based on the relationship, geotemperature distribution criteria and requirements for the cooling measures are proposed, and some suitable composite structures and measures are suggested. The results reported in this paper can provide a valuable guidance for the design and construction of the QTE in the future.

Study on the damage mechanics model of asphalt pavement under high temperature and the optimization design of finite element parameters - Taking Guangle Expressway as an example

In this paper, based on the reliability of the control index APPDI under different base asphalt pavement structures, the research on the correlation analysis between the damage of asphalt pavement and the material strength under high temperature conditions is carried out. Relying on the Guangle Expressway as the project, the distribution of APPDI in the pavement structure was analyzed, and the parameters of the asphalt pavement material were optimized by adjusting the cohesion c and tensile strength ?t of the pavement material, and compared with the results of the Marshall test to determine the optimal parameters of the pavement material. Safety and rationality, comprehensive experimental analysis: It is suggested that the design parameters that should be used in pavement design are cohesion of 0.1630MPa, elastic modulus of 681.41MPa, and Poisson's ratio of 0.35. This research can provide a new idea for the design of structural materials for asphalt pavement.

Enhancing rutting depth prediction in asphalt pavements: A synergistic approach of extreme gradient boosting and snake optimization

This study investigates the development of pavement performance models, focusing on the predictive analysis of pavement distress using ensemble machine learning methods. We employ the Research Institute of Highway Ministry of Transport track dataset to construct predictive models for pavement distress, comparing various methodologies including Random Forest (RF) regression, Extreme Gradient Boosting (XGB) machines, Snake Optimizer-Random Forest (SO-RF), and Snake Optimizer-Extreme Gradient Boosting (SO-XGB). The approach is conducted by multiple experiments and a comprehensive comparative analysis leveraging the repeated K-Fold cross-validation method to ensure the robust assessment for these models. Our findings show that the SO-XGB method is particularly effective in predicting rutting depth. The strength of the SO-XGB model lies in its excellent performance in several performance metrics, such as coefficient of determination (R2), the mean absolute error (MAE), root mean square error (RMSE), the mean absolute percentage error (MAPE), performance index (PI), index of agreement (IA), variance accounted for (VAF) and objective function (OBJ). These results not only illustrate the model’s accuracy but also the practical applicability of SO-XGB model in automatically forecasting pavement damage. Thus, the SO-XGB method stands out as a direct, powerful, and efficient tool in the field of pavement performance prediction.

Special Issue on Fatigue, Performance, and Damage Assessments of Concrete

This Special Issue covers the latest research on fatigue, performance, and damage assessments of concrete. Concrete fatigue is known as a phenomenon characterized by the repetition of loads. The fatigue-based performance of concrete may be affected by a combination of diverse loads, mechanical strength properties, and environmental changes. These factors eventually lead to poor concrete performance. In this Special Issue, nine papers were accepted, divided into three subgroups: two papers on fatigue, two papers on performance, and five papers on damage assessment. Unlike articles published in past Special Issues, our Special Issue contains papers that address concrete performance in concrete pavement applications. In particular, six of the articles contained in this issue concentrate on pavement performance and damage assessments, especially in terms of the Pavement Condition Index. Together with a brief introduction, the keywords, research significance and potential impacts of each article are summarized in this Editorial.

A Case Study of Pavement Foundation Support and Drainage Evaluations of Damaged Urban Cement Concrete Roads

Surface cracks and joint deteriorations are typical premature failures of urban cement concrete pavement. However, traffic loads on the urban pavement are much lower than those on highways. Limited research has been conducted to investigate the causes of accelerated damage in urban cement concrete roads. To investigate the foundation issues that may cause the accelerated damage of urban cement concrete pavements, in this study, field evaluations were conducted to assess pavement foundation support and drainage conditions. Field visual inspections, Ground Penetrating Radar (GPR) survey, Dynamic Cone Penetrometer (DCP) test, and the Core-Hole Permeameter (CHP) test were performed. In urban residential areas with inadequate subgrade bearing capacity, cement concrete pavements are prone to early damage. Foundations with a higher content of coarse particles exhibit a higher CBR value, which can extend the service life of the pavement. The compaction of foundation materials near sewer pipelines and manholes is insufficient, leading to non-uniform support conditions. Moreover, the permeability of the foundation material can influence the service life of pavement surface structures. Foundation materials with fewer fine particles enhance drainage performance, contributing to a longer service life for PCC pavements. In areas with inadequate drainage, water accumulation reduces the bearing capacity of the foundation, thereby accelerating pavement deterioration. The poor bearing capacity and drainage conditions of the foundation lead to cavities between the surface layer and foundation material thus yielding stress concentrations on the pavement surface, which cause the formation of pavement surface cracks.

Influence of volume expansion in NaCl crystallization on the physical properties and molecular structure of asphalt: A MD simulation study

The pavement damage caused by salt-induced volume expansion is becoming increasingly noticeable. Molecular dynamics (MD) method was applied to evaluate the effects of varied volumes of NaCl crystals on physical properties and structure of asphalt. The effects of NaCl crystals on the compatibility between styrene–butadiene–styrene triblock copolymer (SBS) and asphalt, as well as the influence of SBS on the diffusion coefficient of NaCl crystals in the asphalt system, were investigated alongside their effects on the physical modulus and molecular structure of asphalt. The results show that the solubility parameter of the asphalt blends decreases as the volume of NaCl crystals increases. Specifically, the solubility parameter decreases by 52.5 % for the biaxial crystalline growth of NaCl and asphalt blends compared to the matrix asphalt. The analysis of the ion diffusion coefficient reveals that the primary disruption of NaCl crystals in asphalt blends is due to early Cl- and late Na+ diffusion. Furthermore, the rise in shear modulus of the asphalt blends suggests that the salt erosion process of SBS-modified asphalt is advantageous for its high temperature and shear resistance. The changes in peak radial distribution function and radius of gyration indicate that the inclusion of NaCl and SBS into asphalt is a physical aging process. In this case, SBS incorporation acts as a reinforcement to the asphalt to retard the erosion of NaCl.

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection.

Innovative high-strength, high-permeability concrete for large-scale applications in permeable subgrade of highway tunnel

The durability of concrete roads in mountainous regions may be reduced due to underground water infiltration and surface water erosion within highway tunnels. A promising solution is the use of pervious concrete as a drainage subgrade layer to drain away the water, and then to prevent water-induced pavement damage of pavement. For pervious concrete to be an ideal drainage layer for high-traffic loads, it must have compressive strength and permeability coefficient exceeding 25 MPa and 15 mm/s, simultaneously. However, traditional pervious concrete tends to emphasize either in high-strength or high-permeability, depending on its porosity. This study introduces an innovative proportion design method of pervious concrete, which enables higher compressive strength and permeability coefficient by adjusting its skeleton structure, and thus catering to high-traffic load applications. In addition, the workability and setting time of pervious concrete were optimized according to the local environmental conditions, facilitating large-scale and efficient paving of the drainage subgrade. As a result, a drainage subgrade layer with a 28-day compressive strength of 26.43 MPa and permeability coefficient of 18.86 mm/s was successfully applied in a highway tunnel. This study provides a theoretical framework and technical support for widespread application of pervious concrete in drainage subgrade of high-traffic load road.

Positioning and detection of rigid pavement cracks using GNSS data and image processing

Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. Manual inspection is performed by trained inspectors to assess pavement damages conventionally. This can be cost-intensive, time-consuming, and a source of risk for inspectors. An image-based inspection using a smartphone is adopted to overcome such problems. This paper proposes an automatic crack detection and mapping program for rigid pavement, which can automate the visual inspection process. The program uses Global Navigation Satellite System (GNSS) data recorded by smartphones and various image processing techniques to detect crack lengths and areas in images. The performance of the program was evaluated by a field study. A crack quantification process was performed to compare the manually measured values and crack lengths obtained from the program. The results show that the program can detect other types of distress, such as pop-outs and punch-outs. This method can achieve satisfactory performance compared to the effort and costs spent.