Long-life pavement has been introduced to address the urgent need for durable and reliable transportation infrastructure.This review overviews the development of aggregates for long-life pavements and summarizes futur...Long-life pavement has been introduced to address the urgent need for durable and reliable transportation infrastructure.This review overviews the development of aggregates for long-life pavements and summarizes future research directions.The review indicates that natural aggregates,being non-renewable resources,are steadily declining in availability and may need to meet future demands.Construction solid waste aggregates are rapidly developing,with fine separation of reclaimed asphalt pavement(RAP)and reinforcement of cementbased recycled aggregates serving as key strategies to enhance their application.Industry solid waste aggregates possess properties suitable for long-life pavements and offer additional functionalities such as cooling,conductivity,and reflectivity,demonstrating significant development potential.While artificial aggregates exhibit superior performance,their large-scale application requires consideration of economic and environmental impacts.Current aggregate evaluation methods need to address the needs of long-life pavements.Aggregate performance requirements should be graded based on mechanical stress and temperature distribution,with corresponding evaluation methods and indices developed.Evaluating the mechanical properties of aggregates should align more closely with actual stress states.Tests such as triaxial,repeated load,and wheel abrasion polishing are better suited for assessing the strength and durability of long-life pavement aggregates.Similarly,evaluating aggregates'physicochemical properties should be based on studies correlating these properties with road performance,with proposed evaluation criteria.Morphological characteristics of aggregates significantly influence asphalt mixture performance,and efficient evaluation of their profile,angularity,and texture will be a key focus of future research.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
In situ density and moisture content of asphalt pavement are essential controlling parameters that require accurate measurement for quality control and quality assurance purposes.The ground-penetrating radar(GPR)techn...In situ density and moisture content of asphalt pavement are essential controlling parameters that require accurate measurement for quality control and quality assurance purposes.The ground-penetrating radar(GPR)technique could provide non-destructive,non-contact,and full-coverage estimations of pavement density and moisture content.However,the technical readiness and drawbacks,including prediction models,signal processing algorithms,and testing hardware,remain unclear for agencies and construction practitioners,impeding large-scale implementations.This paper aims to provide a thorough review of the theoretical background and current practices of using GPR for non-destructive measurements of asphalt pavement density and moisture content during construction,thereby allowing for real-time correction of over-or under-compaction on site.The principles and applications of GPR-based density and moisture content prediction models were comprehensively summarized.Their strengths and limitations were discussed.Cutting-edge GPR equipment suitable for such applications was introduced,including their system components,application scenarios,and inherent limitations.Factors affecting prediction accuracy were analyzed.Advanced signal processing algorithms were discussed in the end,along with the in-place calibration procedure for aggregate dielectric constants.The reviewed technique could be a guiding tool for real-time monitoring of asphalt pavement density and moisture content using GPR,offering practical insights for future development and standardized deployment in construction quality management.展开更多
The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the p...The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the pavement structure increases the load-carrying capacity of unbound pavement layers by providing lateral restraint,improving vertical stress distribution,and enhancing bearing capacity.Such reinforcement typically aims to either improve the service life of pavements or achieve equivalent performance with a reduced granular cover.Previous and ongoing research quantifies geosynthetic performance in pavement reinforcement using various testing methods.Among these,laboratory model box tests subjected to cyclic loading are pivotal,as they closely replicate real-world traffic conditions.Hence,these studies are essential for understanding how geosynthetics distribute loads and enhance pavement durability.This facilitates the development of optimized geosynthetic design and installation practices,accelerating the loading process to simulate years of traffic wear in a shorter period.This review discusses the improved rutting resistance of unbound pavements reinforced with geosynthetic materials,specifically drawing on data from cyclic plate load tests conducted on laboratory model boxes,as highlighted in the literature.Key variables such as optimum geosynthetic placement,geosynthetic material properties,performance of different geosynthetic materials and the effects of aperture shape and size on rutting resistance are discussed.Furthermore,the review assesses various predictive rutting models,analysing their applicability and accuracy in forecasting the rutting performance of geosynthetic-reinforced unbound pavements.This comprehensive literature review aids pavement engineers and researchers,in guiding the selection and design of geosynthetics to optimize pavement durability and functionality under repetitive traffic loads.展开更多
Precast concrete pavements(PCPs)represent an innovative solution in the construction industry,addressing the need for rapid,intelligent,and low-carbon pavement technologies that significantly reduce construction time ...Precast concrete pavements(PCPs)represent an innovative solution in the construction industry,addressing the need for rapid,intelligent,and low-carbon pavement technologies that significantly reduce construction time and environmental impact.However,the integration of prefabricated technology in pavement surface and base layers lacks systematic classification and understanding.This paper aims to fill this gap by introducing a detailed analysis of discretization and assembly connection technology for cement concrete pavement(CCP)structures.Through a comprehensive review of domestic and international literature,the study classifies prefabricated pavement technology based on discrete assembly structural layers and presents specific conclusions(i)surface layer discrete units are categorized into bottom plates,top plates,plate-rod separated assemblies,and prestressed connections,with optimal material compositions identified to enhance mechanical properties;(ii)base layer discrete units include block-type,plate-type,and beam-type elements,highlighting their contributions to sustainability by incorporating recycled materials(iii)planar assembly connection types are assessed,ranking them by load transfer efficiency,with specific dimensions provided for optimal performance;and(iv)vertical assembly connections are defined by their leveling and sealing layers,suitable for both new constructions and repairs of existing roads.The insights gained from this review not only clarify the distinctions between various structural layers but also provide practical guidelines for enhancing the design and implementation of PCP.This work contributes to advancing sustainable and resilient road construction practices,making it a significant reference for researchers and practitioners in the field.展开更多
Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road...Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.展开更多
This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories.This review emphasizes th...This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories.This review emphasizes the most representative models and methods of analysis in the existing literature and illustrates all of them by numerical examples.Thus,13 such examples are presented here in some detail.Both flexible and rigid(concrete)pavement models involving simple and elaborate cases with respect to geometry and material behavior are considered.Thus,homogeneous or layered half-spaces with isotropic or cross-anisotropic and elastic,viscoelastic or poroelastic properties are considered.The vehicles are modeled as simple point or distributed loads or discrete spring-mass-dashpot system moving with constant or variable velocity.The dynamic response of the above pavement-vehicle systems is obtained by analytical/numerical or purely numerical methods of solution.Analytical/numerical methods have mainly to do with Fourier transforms or complex Fourier series with respect to both space and time.Purely numerical methods involve the finite element method(FEM)and the boundary element method(BEM)working in time or frequency domain.Critical discussions on the advantages and disadvantages of the various pavement-vehicle models and their methods of analysis are provided and the effects of the main parameters on the pavement response are determined through parametric studies and presented in the examples.Finally,conclusions are provided and suggestions for future research are made.展开更多
Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict...Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.展开更多
In order to improve the surface performace of epoxy asphalt pavement （EAP） for steel bridge deck, an epoxy asphalt chip seal （ ECS） covered by a cationic emulsified asphalt fog seal （i. e., fog-sealed chip seal）...In order to improve the surface performace of epoxy asphalt pavement （EAP） for steel bridge deck, an epoxy asphalt chip seal （ ECS） covered by a cationic emulsified asphalt fog seal （i. e., fog-sealed chip seal） isproposed and a laboratory study is conducted to design and evaluate te fog-sealed chip seal. First, the evaluation indices and methods of te chip seal on steel bridge deck pavement were proposed. Secondly, the worst pavement conditions during te maintenance time were simulated by te small traffic load simulation system MMLS3 and the short-term aging test for minimizing the failure probability of chip seal. Finally, the design parameters of fog-sealed chip seal were determined by the experimental analysis and the performance of the designed fog-sealed chip seal was evaluated in thelaboratory. Results indicate that the proposed simulation method of pavement conditions is effective and the maximal load repetitions on the EAPslab specimen are approximately 925 300 times. Moreover, the designed fog-sealedchip sealcan provide a dense surface with sufficient skid resistance,aggregate-asphalt aahesive performance and interlayer shearing resistance.展开更多
Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
The occurrence of top-down(TD)cracking has gradually become a prevalent issue in semi-rigid base asphalt pavements after prolonged service.A coupled simulation model integrating the finite difference method(FDM)and di...The occurrence of top-down(TD)cracking has gradually become a prevalent issue in semi-rigid base asphalt pavements after prolonged service.A coupled simulation model integrating the finite difference method(FDM)and discrete element method(DEM)was employed to investigate the mechanical behavior of asphalt pavement containing a pre-existing TD crack.The mesoscopic parameters of the model were calibrated based on the mixture modulus and the static mechanical response on the MLS66 test road.Finally,an analysis was performed to assess how variations in TD crack depth and longitudinal length affect the distribution patterns of transverse tensile stress,vertical shear stress,and vertical compressive stress.The results indicate that the vertical propagation of TD crack significantly increases both the tensile stress value and range on the middle surface,while the longitudinal development of TD crack has minimal impact.This phenomenon may result in more severe fatigue failure on the middle surface.With the vertical and longitudinal development of TD crack,the vertical shear stress and compressive stress show obvious"two-stage"characteristics.When the crack's vertical length reaches 40 mm,there is a sharp increase in stress on the upper surface.As the crack continues to propagate vertically,the growth of stress on the upper surface becomes negligible,while the stress in the middle and lower layers increased significantly.Conversely,for longitudinal development of TD crack,any changes in stress are insignificant when their length is less than 180 mm;however,as they continue to develop longitudinally beyond this threshold,there is a sharp increase in stress levels.These findings hold great significance for understanding pavement structure deterioration and maintenance behavior associated with TD crack.展开更多
Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
This paper reports a practical pavement overlay design method based on PCI (Pavement Condition Index). Current pavement investigation method (JTJ 073 96) is compared to the ASTM D 5340, which is the standard test met...This paper reports a practical pavement overlay design method based on PCI (Pavement Condition Index). Current pavement investigation method (JTJ 073 96) is compared to the ASTM D 5340, which is the standard test method for airport pavement condition evaluation initially developed for US Air Force. The deficiency in the calculation of PCI based on field data in JTJ 073 is discussed. The proposed design method is compared to AASHTO overlay design method with good agreement. The paper concludes with an example illustrating how the existing pavement structural capacity is related to pavement distress survey results. The presented design method can be used in the design for overlay rehabilitation of pavements of highways, urban streets and airports.展开更多
Expansive soils, prone to being influenced by the environmental conditions, undergo expansion when water is introduced and shrinkage upon drying. This persistent volumetric fluctuation can induce differential movement...Expansive soils, prone to being influenced by the environmental conditions, undergo expansion when water is introduced and shrinkage upon drying. This persistent volumetric fluctuation can induce differential movements and result in cracking of structures erected upon them. The present research focuses on characterizing the behavior of pavements erected on expansive clays subjected to swelling and shrinkage cycles. Direct shear tests and oedometer tests were conducted in the laboratory on samples of expansive soils undergoing swelling-shrinkage cycles. The experimental data reveal a significant decrease in shear strength, evidenced by a reduction in shear parameters (internal friction angle, cohesion) and a decrease in the modulus of elasticity as the number of cycles increases. A numerical model based on the finite element method was developed to simulate the behavior of a pavement on an expansive clay substrate. The model results indicate an increase in total displacements with the increase in the number of shrinkage-swelling cycles, demonstrating a progressive degradation of the soil’s mechanical behavior. This study contributes to a better understanding of the complex phenomena governing the behavior of expansive soils and serves as a foundation for developing effective management and mitigation strategies for road infrastructures.展开更多
This study aims to investigate the failure modes at the interface of semi-flexible pavement(SFP)materials.The cohesive and wetting properties of asphalt materials,as well as two types of grout(early strength cement gr...This study aims to investigate the failure modes at the interface of semi-flexible pavement(SFP)materials.The cohesive and wetting properties of asphalt materials,as well as two types of grout(early strength cement grout-ELS and high strength cement grout-CHS),were evaluated through pull-out tests and contact angle experiments.The rheological properties of the grout/asphalt mortar were assessed using dynamic shear rheometer(DSR)testing.The interaction coefficient,complex shear modulus,and complex viscosity coefficients of the grout/asphalt mortar were calculated to analyze the interaction between the grout and asphalt.Failure modes were identified through image analysis of semi-circular bending test(SCB)specimens.Results indicate that ELS specimens exhibit a lower grout/asphalt interface failure ratio compared to CHS specimens,due to the superior wettability and interaction of ELS grout.As the temperature increases,the proportions of cement fracture and aggregate failure decrease,while the proportion of asphalt cohesive failure surfaces increases.Furthermore,the bonding strength of SBS-modified asphalt with the grout exceeds that of pure asphalt.展开更多
Moisture accumulation within road pavements,particularly in unbound granular materials with or without thin sprayed seals,presents significant challenges in high-rainfall regions such as Queensland.This infiltration o...Moisture accumulation within road pavements,particularly in unbound granular materials with or without thin sprayed seals,presents significant challenges in high-rainfall regions such as Queensland.This infiltration often leads to various forms of pavement distress,eventually causing irreversible damage to the pavement structure.The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation,air temperature,and relative humidity.This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data.Consequently,there is an increasing demand for advanced,technology-driven methodologies to predict moisture variations based on climatic inputs.Addressing this gap,the present study employs five traditional machine learning(ML)algorithms,K-nearest neighbors(KNN),regression trees,random forest,support vector machines(SVMs),and gaussian process regression(GPR),to forecast moisture levels within pavement layers over time,with varying algorithm complexities.Using data collected from an instrumented road in Brisbane,Australia,which includes pavement moisture and climatic factors,the study develops predictive models to forecast moisture content at future time steps.The approach incorporates current moisture content,rather than averaged values,along with seasonality(both daily and annual),and key climatic factors to predict next step moisture.Model performance is evaluated using R2,MSE,RMSE,and MAPE metrics.Results show that ML algorithms can reliably predict long-term moisture variations in pavements,provided optimal hyperparameters are selected for each algorithm.The best-performing algorithms include KNN(the number of neighbours equals to 15),medium regression tree,medium random forest,coarse SVM,and simple GPR,with medium random forest outperforming the others.The study also identifies the optimal hyperparameter combinations for each algorithm,offering significant advancements in moisture prediction tools for pavement technology。展开更多
Isocyanate and its products are playing an increasingly important role in the high-performance development of asphalt pavement,but researchers have always focused on polyurethane(PU),one of the isocyanate products,and...Isocyanate and its products are playing an increasingly important role in the high-performance development of asphalt pavement,but researchers have always focused on polyurethane(PU),one of the isocyanate products,and neglected the other roles of isocyanate-based materials in asphalt pavement.The application of isocyanate-based materials in asphalt pavement is still in the exploratory stage,and the research direction is not clear.It is necessary to summarize and propose research directions for the application of isocyanate-based materials in asphalt pavement.Therefore,this paper reviews the application of isocyanate-based materials in asphalt pavement,classifies the products synthesized from isocyanate for asphalt binder,introduces the application effects of different isocyanate-based materials in asphalt binder,and analyzes the limitations of each material.Meanwhile,the other roles of isocyanate-based materials in asphalt pavement,such as coating materials and adhesive materials,are summarized.Finally,the development direction of isocyanate-based materials in asphalt pavement is prospected.Isocyanate-based materials are expected to significantly increase the service life of asphalt pavement because of their excellent properties.With the advancement of technology,the application of isocyanate-based materials will become more and more common,promoting the sustainable development of road construction.This paper can provide a reference for the development and application of isocyanate-based materials in asphalt pavement.展开更多
The utilization of reclaimed asphalt pavement(RAP)in asphalt mixtures has gained momentum in recent years,yet concerns persist regarding the long-term performance and binder properties of high RAP content mixtures.To ...The utilization of reclaimed asphalt pavement(RAP)in asphalt mixtures has gained momentum in recent years,yet concerns persist regarding the long-term performance and binder properties of high RAP content mixtures.To overcome these challenges,rejuvenators have emerged as a promising solution to enhance the properties of aged asphalt binders and improve the overall performance of asphalt mixtures.This paper provides a comprehensive state-of-the-art review of rejuvenator technology and its potential to enhance the performance and sustainability of asphalt pavements.Rejuvenators are additives used to restore the properties of aged asphalt binders,particularly when incorporating high percentages of RAP.The performance of these additives varies based on their origin,and different methods can be used to analyze the rejuvenation process.Since proper specifications for rejuvenators are not available,blending charts are used to determine the optimum dosage of rejuvenators.However,proper blending must be achieved to maximize results and reduce the effect of black rock.Laboratory tests and some field performance studies on rejuvenated aged asphalt binders and RAP mixtures have shown improved or similar performance compared to virgin materials.Additionally,the use of rejuvenators has been observed to reduce construction costs,suggesting that this is a cost-effective technology for asphalt pavements.While rejuvenators show promise in improving the performance of pavements with recycled materials,challenges remain regarding optimization,long-term durability,and environmental effects.This review paper also identifies key areas for future research,including life-cycle cost analyses,comprehensive environmental impact assessments,and long-term field performance monitoring.展开更多
Biochar,a solid carbonaceous material produced by heating biomass in oxygen-free or low-oxygen conditions(pyrolysis),has been used in various applications,including wastewater treatment,carbon sequestration,and improv...Biochar,a solid carbonaceous material produced by heating biomass in oxygen-free or low-oxygen conditions(pyrolysis),has been used in various applications,including wastewater treatment,carbon sequestration,and improving soil fertility.However,very limited research has been performed to explore its feasibility to improve the expansive clay(EC)subgrade.In this study,fine-grained wood biochar derived from wood waste was used to stabilise and enhance the mechanical performance of the EC as road subgrade.A comprehensive series of geotechnical tests,including unconfined compressive strength(UCS),California bearing ratio(CBR),repeated load triaxial(RLT),and swelling-shrinkage tests,were conducted to investigate the engineering properties of expansive clay mixed with different contents of the fine-grained biochar(FGB)(i.e.0,1%,2%,3%,and 4%by weight of dry soil).Furthermore,X-ray diffraction(XRD),X-ray fluorescence(XRF),X-ray micro-CT,and thermogravimetric analysis(TGA)analyses were performed to study the microchemical modification of the EC-FGB mixtures.The results showed that adding FGB reduced the swelling and shrinkage potential while enhancing the mechanical properties of the EC.The micro-level analysis also supported the enhancement of the geotechnical performance of the EC resulting from the incorporation of FGB.According to the test results,2%FGB was considered the optimum content,increasing UCS,CBR,and resilient modulus by 31.1%,24.1%,and 31.5%,respectively,and decreasing the swell-shrinkage index by 7%.展开更多
Pavement distress detection plays a pivotal role in ensuring roadway safety,serviceability,and cost-effective infrastructure management.With rapid advancements in intelligent transportation systems,computer vision,and...Pavement distress detection plays a pivotal role in ensuring roadway safety,serviceability,and cost-effective infrastructure management.With rapid advancements in intelligent transportation systems,computer vision,and sensing technologies,non-contact detection approaches based on images and point clouds have become increasingly prominent due to their efficiency,objectivity,and scalability.This review systematically examines both image-based and point cloud-based methodologies,structured along the complete detection pipeline encompassing data acquisition,preprocessing,distress extraction,and geometric quantification.Image-based techniques rely on visual cues,such as texture,color,and edge continuity,to identify surface-level anomalies efficiently,benefiting from mature deep learning frameworks for classification,object detection,and pixel-level segmentation.In contrast,point cloud-based methods capture rich three-dimensional geometric and structural information,enabling detailed modeling of crack depth,rutting deformation,and surface irregularities.Although each modality can independently achieve satisfactory performance,their complementary strengths have driven a growing trend toward hybrid frameworks,combining image-based rapid screening with point cloud-based precision modeling,to enhance detection accuracy,robustness,and adaptability across varying conditions.Furthermore,this paper highlights persistent challenges,including multimodal data fusion,high equipment and labeling costs,computational complexity,and the need for standardized benchmarks.By synthesizing current progress and identifying key technical bottlenecks,this review provides a comprehensive foundation and forward-looking perspective for developing intelligent,efficient,and scalable pavement distress detection systems.展开更多
基金sponsored by the National Natural Science Foundation of China(52178420,52408476)Research Project of Liaoning Provincial Transportation Construction Investment Group Co.,Ltd.(202410)+1 种基金Postdoctoral Fellowship Program of CPSF(GZC20242207)the Fundamental Research Funds for the Central Universities(HIT.DZJJ.2023086).
文摘Long-life pavement has been introduced to address the urgent need for durable and reliable transportation infrastructure.This review overviews the development of aggregates for long-life pavements and summarizes future research directions.The review indicates that natural aggregates,being non-renewable resources,are steadily declining in availability and may need to meet future demands.Construction solid waste aggregates are rapidly developing,with fine separation of reclaimed asphalt pavement(RAP)and reinforcement of cementbased recycled aggregates serving as key strategies to enhance their application.Industry solid waste aggregates possess properties suitable for long-life pavements and offer additional functionalities such as cooling,conductivity,and reflectivity,demonstrating significant development potential.While artificial aggregates exhibit superior performance,their large-scale application requires consideration of economic and environmental impacts.Current aggregate evaluation methods need to address the needs of long-life pavements.Aggregate performance requirements should be graded based on mechanical stress and temperature distribution,with corresponding evaluation methods and indices developed.Evaluating the mechanical properties of aggregates should align more closely with actual stress states.Tests such as triaxial,repeated load,and wheel abrasion polishing are better suited for assessing the strength and durability of long-life pavement aggregates.Similarly,evaluating aggregates'physicochemical properties should be based on studies correlating these properties with road performance,with proposed evaluation criteria.Morphological characteristics of aggregates significantly influence asphalt mixture performance,and efficient evaluation of their profile,angularity,and texture will be a key focus of future research.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金supported by the National Key R&D Program of China(2024YFB2605500)the National Natural Science Foundation of China(52308444)the Fundamental Research Funds for the Central Universities(2242024K40036).
文摘In situ density and moisture content of asphalt pavement are essential controlling parameters that require accurate measurement for quality control and quality assurance purposes.The ground-penetrating radar(GPR)technique could provide non-destructive,non-contact,and full-coverage estimations of pavement density and moisture content.However,the technical readiness and drawbacks,including prediction models,signal processing algorithms,and testing hardware,remain unclear for agencies and construction practitioners,impeding large-scale implementations.This paper aims to provide a thorough review of the theoretical background and current practices of using GPR for non-destructive measurements of asphalt pavement density and moisture content during construction,thereby allowing for real-time correction of over-or under-compaction on site.The principles and applications of GPR-based density and moisture content prediction models were comprehensively summarized.Their strengths and limitations were discussed.Cutting-edge GPR equipment suitable for such applications was introduced,including their system components,application scenarios,and inherent limitations.Factors affecting prediction accuracy were analyzed.Advanced signal processing algorithms were discussed in the end,along with the in-place calibration procedure for aggregate dielectric constants.The reviewed technique could be a guiding tool for real-time monitoring of asphalt pavement density and moisture content using GPR,offering practical insights for future development and standardized deployment in construction quality management.
基金financial and intellectual support provided by Queensland University of Technology(QUT)through its Higher Degree Research Program.
文摘The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the pavement structure increases the load-carrying capacity of unbound pavement layers by providing lateral restraint,improving vertical stress distribution,and enhancing bearing capacity.Such reinforcement typically aims to either improve the service life of pavements or achieve equivalent performance with a reduced granular cover.Previous and ongoing research quantifies geosynthetic performance in pavement reinforcement using various testing methods.Among these,laboratory model box tests subjected to cyclic loading are pivotal,as they closely replicate real-world traffic conditions.Hence,these studies are essential for understanding how geosynthetics distribute loads and enhance pavement durability.This facilitates the development of optimized geosynthetic design and installation practices,accelerating the loading process to simulate years of traffic wear in a shorter period.This review discusses the improved rutting resistance of unbound pavements reinforced with geosynthetic materials,specifically drawing on data from cyclic plate load tests conducted on laboratory model boxes,as highlighted in the literature.Key variables such as optimum geosynthetic placement,geosynthetic material properties,performance of different geosynthetic materials and the effects of aperture shape and size on rutting resistance are discussed.Furthermore,the review assesses various predictive rutting models,analysing their applicability and accuracy in forecasting the rutting performance of geosynthetic-reinforced unbound pavements.This comprehensive literature review aids pavement engineers and researchers,in guiding the selection and design of geosynthetics to optimize pavement durability and functionality under repetitive traffic loads.
基金supported by the Research Program of Wuhan Building Energy Efficiency Office(grant number 202331).
文摘Precast concrete pavements(PCPs)represent an innovative solution in the construction industry,addressing the need for rapid,intelligent,and low-carbon pavement technologies that significantly reduce construction time and environmental impact.However,the integration of prefabricated technology in pavement surface and base layers lacks systematic classification and understanding.This paper aims to fill this gap by introducing a detailed analysis of discretization and assembly connection technology for cement concrete pavement(CCP)structures.Through a comprehensive review of domestic and international literature,the study classifies prefabricated pavement technology based on discrete assembly structural layers and presents specific conclusions(i)surface layer discrete units are categorized into bottom plates,top plates,plate-rod separated assemblies,and prestressed connections,with optimal material compositions identified to enhance mechanical properties;(ii)base layer discrete units include block-type,plate-type,and beam-type elements,highlighting their contributions to sustainability by incorporating recycled materials(iii)planar assembly connection types are assessed,ranking them by load transfer efficiency,with specific dimensions provided for optimal performance;and(iv)vertical assembly connections are defined by their leveling and sealing layers,suitable for both new constructions and repairs of existing roads.The insights gained from this review not only clarify the distinctions between various structural layers but also provide practical guidelines for enhancing the design and implementation of PCP.This work contributes to advancing sustainable and resilient road construction practices,making it a significant reference for researchers and practitioners in the field.
文摘Pavement condition monitoring and its timely maintenance is necessary to ensure the safety and quality of the roadway infrastructure. The International Roughness Index (IRI) is a commonly used measure to quantify road surface roughness and is a critical input to asset management. In Indiana, the IRI statistic contributes to roughly half of the pavement quality index computation used for asset management. Most agencies inventory IRI once a year, however, pavement conditions vary much more frequently. The objective of this paper is to develop a framework using crowdsourced connected vehicle data to identify and detect temporal changes in IRI. Over 3 billion connected vehicle records in Indiana were analyzed across 30 months between 2022 and 2024 to understand the spatiotemporal variations in roughness. Annual comparisons across all major interstates in Indiana showed the miles of interstates classified as “Good” decreased from 1896 to 1661 miles between 2022 and 2024. The miles of interstate classified as “Needs Maintenance” increased from 82 to 120 miles. A detailed case study showing monthly and daily changes of estimated IRI on I-65 are presented along with supporting dashcam images. Although the crowdsourced IRI estimates are not as robust as traditional specialized pavement profilers, they can be obtained on a monthly, weekly, or even daily basis. The paper concludes by suggesting a combination of frequent crowdsourced IRI and commercially available dashcam imagery of roadway can provide an agile and responsive mechanism for agencies to implement pavement asset management programs that can complement existing annual programs.
文摘This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories.This review emphasizes the most representative models and methods of analysis in the existing literature and illustrates all of them by numerical examples.Thus,13 such examples are presented here in some detail.Both flexible and rigid(concrete)pavement models involving simple and elaborate cases with respect to geometry and material behavior are considered.Thus,homogeneous or layered half-spaces with isotropic or cross-anisotropic and elastic,viscoelastic or poroelastic properties are considered.The vehicles are modeled as simple point or distributed loads or discrete spring-mass-dashpot system moving with constant or variable velocity.The dynamic response of the above pavement-vehicle systems is obtained by analytical/numerical or purely numerical methods of solution.Analytical/numerical methods have mainly to do with Fourier transforms or complex Fourier series with respect to both space and time.Purely numerical methods involve the finite element method(FEM)and the boundary element method(BEM)working in time or frequency domain.Critical discussions on the advantages and disadvantages of the various pavement-vehicle models and their methods of analysis are provided and the effects of the main parameters on the pavement response are determined through parametric studies and presented in the examples.Finally,conclusions are provided and suggestions for future research are made.
基金supported by the National Key Research and Development Program of China(No.2021YFB2600300).
文摘Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.
基金The National Natural Science Foundation of China(No.51378122)
文摘In order to improve the surface performace of epoxy asphalt pavement （EAP） for steel bridge deck, an epoxy asphalt chip seal （ ECS） covered by a cationic emulsified asphalt fog seal （i. e., fog-sealed chip seal） isproposed and a laboratory study is conducted to design and evaluate te fog-sealed chip seal. First, the evaluation indices and methods of te chip seal on steel bridge deck pavement were proposed. Secondly, the worst pavement conditions during te maintenance time were simulated by te small traffic load simulation system MMLS3 and the short-term aging test for minimizing the failure probability of chip seal. Finally, the design parameters of fog-sealed chip seal were determined by the experimental analysis and the performance of the designed fog-sealed chip seal was evaluated in thelaboratory. Results indicate that the proposed simulation method of pavement conditions is effective and the maximal load repetitions on the EAPslab specimen are approximately 925 300 times. Moreover, the designed fog-sealedchip sealcan provide a dense surface with sufficient skid resistance,aggregate-asphalt aahesive performance and interlayer shearing resistance.
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金supported by National Key R&D Program of China(Grant No.2021YFB2601200)Open Fund of National Engineering Research Center of Highway Maintenance Technology(Changsha University of Science&Technology)(No.kfj230207).
文摘The occurrence of top-down(TD)cracking has gradually become a prevalent issue in semi-rigid base asphalt pavements after prolonged service.A coupled simulation model integrating the finite difference method(FDM)and discrete element method(DEM)was employed to investigate the mechanical behavior of asphalt pavement containing a pre-existing TD crack.The mesoscopic parameters of the model were calibrated based on the mixture modulus and the static mechanical response on the MLS66 test road.Finally,an analysis was performed to assess how variations in TD crack depth and longitudinal length affect the distribution patterns of transverse tensile stress,vertical shear stress,and vertical compressive stress.The results indicate that the vertical propagation of TD crack significantly increases both the tensile stress value and range on the middle surface,while the longitudinal development of TD crack has minimal impact.This phenomenon may result in more severe fatigue failure on the middle surface.With the vertical and longitudinal development of TD crack,the vertical shear stress and compressive stress show obvious"two-stage"characteristics.When the crack's vertical length reaches 40 mm,there is a sharp increase in stress on the upper surface.As the crack continues to propagate vertically,the growth of stress on the upper surface becomes negligible,while the stress in the middle and lower layers increased significantly.Conversely,for longitudinal development of TD crack,any changes in stress are insignificant when their length is less than 180 mm;however,as they continue to develop longitudinally beyond this threshold,there is a sharp increase in stress levels.These findings hold great significance for understanding pavement structure deterioration and maintenance behavior associated with TD crack.
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
文摘This paper reports a practical pavement overlay design method based on PCI (Pavement Condition Index). Current pavement investigation method (JTJ 073 96) is compared to the ASTM D 5340, which is the standard test method for airport pavement condition evaluation initially developed for US Air Force. The deficiency in the calculation of PCI based on field data in JTJ 073 is discussed. The proposed design method is compared to AASHTO overlay design method with good agreement. The paper concludes with an example illustrating how the existing pavement structural capacity is related to pavement distress survey results. The presented design method can be used in the design for overlay rehabilitation of pavements of highways, urban streets and airports.
文摘Expansive soils, prone to being influenced by the environmental conditions, undergo expansion when water is introduced and shrinkage upon drying. This persistent volumetric fluctuation can induce differential movements and result in cracking of structures erected upon them. The present research focuses on characterizing the behavior of pavements erected on expansive clays subjected to swelling and shrinkage cycles. Direct shear tests and oedometer tests were conducted in the laboratory on samples of expansive soils undergoing swelling-shrinkage cycles. The experimental data reveal a significant decrease in shear strength, evidenced by a reduction in shear parameters (internal friction angle, cohesion) and a decrease in the modulus of elasticity as the number of cycles increases. A numerical model based on the finite element method was developed to simulate the behavior of a pavement on an expansive clay substrate. The model results indicate an increase in total displacements with the increase in the number of shrinkage-swelling cycles, demonstrating a progressive degradation of the soil’s mechanical behavior. This study contributes to a better understanding of the complex phenomena governing the behavior of expansive soils and serves as a foundation for developing effective management and mitigation strategies for road infrastructures.
基金Funded by the Science and Technology Program Special Fund of Jiangsu Province(Frontier Leading Technology Basic Research)Major Projects(No.BK20222004)the National Natural Science Foundation of China(No.52078241)the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘This study aims to investigate the failure modes at the interface of semi-flexible pavement(SFP)materials.The cohesive and wetting properties of asphalt materials,as well as two types of grout(early strength cement grout-ELS and high strength cement grout-CHS),were evaluated through pull-out tests and contact angle experiments.The rheological properties of the grout/asphalt mortar were assessed using dynamic shear rheometer(DSR)testing.The interaction coefficient,complex shear modulus,and complex viscosity coefficients of the grout/asphalt mortar were calculated to analyze the interaction between the grout and asphalt.Failure modes were identified through image analysis of semi-circular bending test(SCB)specimens.Results indicate that ELS specimens exhibit a lower grout/asphalt interface failure ratio compared to CHS specimens,due to the superior wettability and interaction of ELS grout.As the temperature increases,the proportions of cement fracture and aggregate failure decrease,while the proportion of asphalt cohesive failure surfaces increases.Furthermore,the bonding strength of SBS-modified asphalt with the grout exceeds that of pure asphalt.
基金the financial and intellectual support provided by Queensland University of Technology(QUT),Australia,through its Higher Degree Research Program,which played a crucial role in the successful completion of this research study
文摘Moisture accumulation within road pavements,particularly in unbound granular materials with or without thin sprayed seals,presents significant challenges in high-rainfall regions such as Queensland.This infiltration often leads to various forms of pavement distress,eventually causing irreversible damage to the pavement structure.The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation,air temperature,and relative humidity.This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data.Consequently,there is an increasing demand for advanced,technology-driven methodologies to predict moisture variations based on climatic inputs.Addressing this gap,the present study employs five traditional machine learning(ML)algorithms,K-nearest neighbors(KNN),regression trees,random forest,support vector machines(SVMs),and gaussian process regression(GPR),to forecast moisture levels within pavement layers over time,with varying algorithm complexities.Using data collected from an instrumented road in Brisbane,Australia,which includes pavement moisture and climatic factors,the study develops predictive models to forecast moisture content at future time steps.The approach incorporates current moisture content,rather than averaged values,along with seasonality(both daily and annual),and key climatic factors to predict next step moisture.Model performance is evaluated using R2,MSE,RMSE,and MAPE metrics.Results show that ML algorithms can reliably predict long-term moisture variations in pavements,provided optimal hyperparameters are selected for each algorithm.The best-performing algorithms include KNN(the number of neighbours equals to 15),medium regression tree,medium random forest,coarse SVM,and simple GPR,with medium random forest outperforming the others.The study also identifies the optimal hyperparameter combinations for each algorithm,offering significant advancements in moisture prediction tools for pavement technology。
基金The authors are appreciative of the financial assistance granted by the National Natural Science Foundation of China(No.52378462)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011448).
文摘Isocyanate and its products are playing an increasingly important role in the high-performance development of asphalt pavement,but researchers have always focused on polyurethane(PU),one of the isocyanate products,and neglected the other roles of isocyanate-based materials in asphalt pavement.The application of isocyanate-based materials in asphalt pavement is still in the exploratory stage,and the research direction is not clear.It is necessary to summarize and propose research directions for the application of isocyanate-based materials in asphalt pavement.Therefore,this paper reviews the application of isocyanate-based materials in asphalt pavement,classifies the products synthesized from isocyanate for asphalt binder,introduces the application effects of different isocyanate-based materials in asphalt binder,and analyzes the limitations of each material.Meanwhile,the other roles of isocyanate-based materials in asphalt pavement,such as coating materials and adhesive materials,are summarized.Finally,the development direction of isocyanate-based materials in asphalt pavement is prospected.Isocyanate-based materials are expected to significantly increase the service life of asphalt pavement because of their excellent properties.With the advancement of technology,the application of isocyanate-based materials will become more and more common,promoting the sustainable development of road construction.This paper can provide a reference for the development and application of isocyanate-based materials in asphalt pavement.
基金the Office of Research&Economic Development and Department of Civil,Coastal and Environmental Engineering at University of South Alabama for the support.
文摘The utilization of reclaimed asphalt pavement(RAP)in asphalt mixtures has gained momentum in recent years,yet concerns persist regarding the long-term performance and binder properties of high RAP content mixtures.To overcome these challenges,rejuvenators have emerged as a promising solution to enhance the properties of aged asphalt binders and improve the overall performance of asphalt mixtures.This paper provides a comprehensive state-of-the-art review of rejuvenator technology and its potential to enhance the performance and sustainability of asphalt pavements.Rejuvenators are additives used to restore the properties of aged asphalt binders,particularly when incorporating high percentages of RAP.The performance of these additives varies based on their origin,and different methods can be used to analyze the rejuvenation process.Since proper specifications for rejuvenators are not available,blending charts are used to determine the optimum dosage of rejuvenators.However,proper blending must be achieved to maximize results and reduce the effect of black rock.Laboratory tests and some field performance studies on rejuvenated aged asphalt binders and RAP mixtures have shown improved or similar performance compared to virgin materials.Additionally,the use of rejuvenators has been observed to reduce construction costs,suggesting that this is a cost-effective technology for asphalt pavements.While rejuvenators show promise in improving the performance of pavements with recycled materials,challenges remain regarding optimization,long-term durability,and environmental effects.This review paper also identifies key areas for future research,including life-cycle cost analyses,comprehensive environmental impact assessments,and long-term field performance monitoring.
基金supported by the Australian Research Council Training Centre for Whole Life Design of Carbon Neutral Infrastructure(Grant No.IC230100015).
文摘Biochar,a solid carbonaceous material produced by heating biomass in oxygen-free or low-oxygen conditions(pyrolysis),has been used in various applications,including wastewater treatment,carbon sequestration,and improving soil fertility.However,very limited research has been performed to explore its feasibility to improve the expansive clay(EC)subgrade.In this study,fine-grained wood biochar derived from wood waste was used to stabilise and enhance the mechanical performance of the EC as road subgrade.A comprehensive series of geotechnical tests,including unconfined compressive strength(UCS),California bearing ratio(CBR),repeated load triaxial(RLT),and swelling-shrinkage tests,were conducted to investigate the engineering properties of expansive clay mixed with different contents of the fine-grained biochar(FGB)(i.e.0,1%,2%,3%,and 4%by weight of dry soil).Furthermore,X-ray diffraction(XRD),X-ray fluorescence(XRF),X-ray micro-CT,and thermogravimetric analysis(TGA)analyses were performed to study the microchemical modification of the EC-FGB mixtures.The results showed that adding FGB reduced the swelling and shrinkage potential while enhancing the mechanical properties of the EC.The micro-level analysis also supported the enhancement of the geotechnical performance of the EC resulting from the incorporation of FGB.According to the test results,2%FGB was considered the optimum content,increasing UCS,CBR,and resilient modulus by 31.1%,24.1%,and 31.5%,respectively,and decreasing the swell-shrinkage index by 7%.
基金supported in part by the National Natural Science Foundation of China(52378431,52408454)Fundamental Research Funds for the Central Universities,Chang’an University(300102210302,300102210118)+1 种基金111 Project of Sustainable Transportation for Urban Agglomeration in Western China(B20035)the 111 Project of Low Carbon Smart Road Infrastructure Construction and Maintenance Discipline Innovation and Talent Introduction Base of Shaanxi Province.
文摘Pavement distress detection plays a pivotal role in ensuring roadway safety,serviceability,and cost-effective infrastructure management.With rapid advancements in intelligent transportation systems,computer vision,and sensing technologies,non-contact detection approaches based on images and point clouds have become increasingly prominent due to their efficiency,objectivity,and scalability.This review systematically examines both image-based and point cloud-based methodologies,structured along the complete detection pipeline encompassing data acquisition,preprocessing,distress extraction,and geometric quantification.Image-based techniques rely on visual cues,such as texture,color,and edge continuity,to identify surface-level anomalies efficiently,benefiting from mature deep learning frameworks for classification,object detection,and pixel-level segmentation.In contrast,point cloud-based methods capture rich three-dimensional geometric and structural information,enabling detailed modeling of crack depth,rutting deformation,and surface irregularities.Although each modality can independently achieve satisfactory performance,their complementary strengths have driven a growing trend toward hybrid frameworks,combining image-based rapid screening with point cloud-based precision modeling,to enhance detection accuracy,robustness,and adaptability across varying conditions.Furthermore,this paper highlights persistent challenges,including multimodal data fusion,high equipment and labeling costs,computational complexity,and the need for standardized benchmarks.By synthesizing current progress and identifying key technical bottlenecks,this review provides a comprehensive foundation and forward-looking perspective for developing intelligent,efficient,and scalable pavement distress detection systems.
