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
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
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
Ensuring highway safety relies heavily on pavement friction resistance.To enable network-level pavement skid resistance monitoring and management,this study proposes a non-contact three-dimensional laser surface testi...Ensuring highway safety relies heavily on pavement friction resistance.To enable network-level pavement skid resistance monitoring and management,this study proposes a non-contact three-dimensional laser surface testing method to obtain detailed aggregate surface data.The existing contact-based skid resistance measurement methods suffer from poor reproducibility and repeatability,hindering their application for network-level management.In this research,traditional multiple linear regression and four machine learning methods,support vector machine(SVM),random forest(RF),gradient boosting decision tree(GBDT),and convolutional neural network(CNN),are utilized to evaluate and predict pavement frictional performance.To assess the proposed methods,data from 45 pavement sites in Oklahoma,including 6 major preventive maintenance(PM)treatments and 7 typical types of aggregates,are collected.Parallel data acquisition is conducted at highway speeds using a grip tester and a high-speed texture profiler to measure pavement skid resistance and surface macro-texture,respectively.Aggregate properties are captured in 3D using a portable ultra-high-resolution 3D laser imaging scanner,leading to the calculation of four types of 3D aggregate parameters characterizing the micro-texture of aggregate surfaces.The relationship between pavement surface friction and texture is explored using machine learning models.The results reveal that the random forest and gradient boosting decision tree models exhibit the highest accuracy,SVM and CNN perform moderately,while the traditional linear regression method fares the worst.By assessing the importance of the 38 parameter variables,the most critical 21 variables were selected for model development.Test results demonstrate that the GBDT model exhibits the best predictive performance,with an explanatory capability of 87.4%for road friction performance.The findings demonstrate the feasibility of replacing contact-based pavement friction evaluation with non-contact texture measurements,offering promising prospects for a network-level pavement skid resistance monitoring and management system.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
The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical appr...The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.展开更多
The use of hot recycled asphalt mixture(HRAM)allows for a reduction in the depletion of non-renewable resources and presents environmental benefits.However,concerns arise regarding the performance of HRAM due to the l...The use of hot recycled asphalt mixture(HRAM)allows for a reduction in the depletion of non-renewable resources and presents environmental benefits.However,concerns arise regarding the performance of HRAM due to the lower degree of blending(DOB)of virgin and aged asphalt(V&A asphalt).This paper aims to provide an up-to-date review on the DOB of V&A asphalt in HRAM.Initially,the paper introduces the DOB of V&A asphalt,followed by an analysis of the blending theory,evaluation methods,and influencing factors of DOB.Subsequently,the effect of DOB on the performance of HRAM is investigated,and molecular dynamic simulation is utilized to analyze the blend of V&A asphalt.Finally,methods for improving DOB are summarized.It was found that the use of high-resolution microscopy with tracer methods such as SEM/EDS was an effective way to characterize DOB.Furthermore,the chemical composition and colloid structure between virgin and aged asphalt are crucial to DOB.Additionally,improving DOB by utilizing the coupling effect of time and temperature during transportation,paving,and compaction stages is promising.Future research should focus on standardizing test methods,refining field simulation models,and developing intelligent construction technologies to achieve more efficient and durable blending.This review provides theoretical guidance and practical references for improving the DOB of V&A asphalt and promoting sustainable pavement construction.展开更多
Road pavements in tunnels are usually made of asphalt mixtures,which,unfortunately,are flammable materials.Hence,this type of pavement could release heat,and more specifically smoke,in the event of a tunnel fire,there...Road pavements in tunnels are usually made of asphalt mixtures,which,unfortunately,are flammable materials.Hence,this type of pavement could release heat,and more specifically smoke,in the event of a tunnel fire,thereby worsening the environmental conditions for human health.Extensive research has been conducted in recent years to enhance the fire reaction of traditional asphalt mixtures for the road pavements used in tunnels.The addition of the Flame Retardants(FRs)in conventional asphalt mixtures appears to be promising.Nevertheless,the potential effects of the FRs in terms of the reduction in consequences on tunnel users in the event of a large fire do not seem to have been sufficiently investigated by using fluid dynamics analysis as a computational tool.Given this gap of knowledge,this article aims to quantitatively evaluate whether the use of flame-retarded asphalt mixtures,as opposed to traditional ones without FRs,might mitigate the adverse effects on the safety of evacuees and fire brigade by performing numerical analyses in the case of a tunnel fire.To achieve this goal,3D Computational Fluid Dynamics(CFD)models,which were executed using the Fire Dynamics Simulator(FDS)tool,were established in the case of a major fire of a Heavy Goods Vehicle(HGV)characterized by a maximum Heat Release Rate(HRRmax)of 100 MW.The people evacuation process was also simulated,and the Evac tool was used.Compared to the traditional asphalt pavements without FRs,the simulation findings indicated that the addition of the FRs causes a reduction in CO and CO_(2)levels in the tunnel during the aforementioned fire,with a minor number of evacuees being exposed to the risk of incapacity to self-evacuate,as well as certain safety benefits for the operability of the firefighters entering the tunnel downstream of the fire when the tunnel is naturally ventilated.展开更多
The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road...The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.展开更多
The elevated temperatures adversely affect the durability and lifespan of pavement.Understanding the factors that influence asphalt pavement temperature offers valuable insights for creating climate-friendly cities wi...The elevated temperatures adversely affect the durability and lifespan of pavement.Understanding the factors that influence asphalt pavement temperature offers valuable insights for creating climate-friendly cities with cooler pavement surfaces.In this study,three aggregates of varying types and colors,two types of bitumen(one without pigment and one with the addition of red pigment,Fe2O3),and two levels of mean texture depth(MTD),high and low,were utilized to create asphalt samples using Marshall's method.A total of 38 thermocouple sensors were employed to simultaneously record temperatures in three areas within the samples,as well as the temperatures in shaded and sunlit conditions over a period of 17 days.Furthermore,a comprehensive evaluation was conducted to assess the impact of each factor on the solar reflectance index(SRI).Twelve general linear models(GLMs)were developed using a full factorial design of experiment,and five models with an R2 greater than 95%were evaluated and analyzed.The analysis,based on the coefficients derived from the GLMs,indicates that the mean MTD is the most significant parameter affecting surface temperature.Pigment color emerged as the second most influential factor affecting both surface and bottom temperatures.Additionally,the findings revealed that MTD has the greatest impact on the SRI,followed by pigment color and aggregate color.It was also determined that the interaction between density,pigment color,and aggregate color plays a crucial role in determining the temperatures of both the surface and bottom of the specimens.展开更多
The performance of roller compacted concrete(RCC)was greatly influenced by variations in material proportion,optimum moisture content,density of mixes and methodology adopted making it different from conventional conc...The performance of roller compacted concrete(RCC)was greatly influenced by variations in material proportion,optimum moisture content,density of mixes and methodology adopted making it different from conventional concrete mixes.Even though RCC has gained popularity,the complex phenomenon involved in developing the RCC mixes limits it from large-scale applications.In this study,reclaimed asphalt pavement(RAP)incorporated roller-compacted geopolymer concrete(RGC)mixes were developed herein with different compaction techniques such as vibratory hammer(VH),modified proctor(MP),vibration table(VT)and compression machine(CM)are studied and compared with control mixes of natural aggregates.Initially,the effect of alkali solutions such as sodium hydroxide(SH)and sodium silicate(SS)on the physical properties.During,the second phase mechanical properties such as dry density,compressive,flexural and split-tensile strength,modulus of elasticity and microstructure properties will be investigated.The test results revealed that compaction efforts were greatly influenced by the alkali solution.Furthermore,the poor bond characteristics between RAP and the binder matrix had a significant effect on strength properties.Also,the various compaction techniques affected the mechanical properties of mixes developed herein.In Comparison with various compaction efforts,VH and MP produced comparable results,whereas the VT method underestimated and overestimated the various strength properties.Although,the CM method reports comparable results but difficult to maintain consistency in strength aspects.Therefore,optimization of various parameters influencing the concrete properties needs to be achieved for field density.展开更多
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%.
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study is under the research project“development of aggregate characteristics-based preventive maintenance treatments using 3D laser imaging and aggregate imaging technology for optimized skid resistance of pavements”sponsored by the Oklahoma Department of Transportation(ODOT SPR 2275).
文摘Ensuring highway safety relies heavily on pavement friction resistance.To enable network-level pavement skid resistance monitoring and management,this study proposes a non-contact three-dimensional laser surface testing method to obtain detailed aggregate surface data.The existing contact-based skid resistance measurement methods suffer from poor reproducibility and repeatability,hindering their application for network-level management.In this research,traditional multiple linear regression and four machine learning methods,support vector machine(SVM),random forest(RF),gradient boosting decision tree(GBDT),and convolutional neural network(CNN),are utilized to evaluate and predict pavement frictional performance.To assess the proposed methods,data from 45 pavement sites in Oklahoma,including 6 major preventive maintenance(PM)treatments and 7 typical types of aggregates,are collected.Parallel data acquisition is conducted at highway speeds using a grip tester and a high-speed texture profiler to measure pavement skid resistance and surface macro-texture,respectively.Aggregate properties are captured in 3D using a portable ultra-high-resolution 3D laser imaging scanner,leading to the calculation of four types of 3D aggregate parameters characterizing the micro-texture of aggregate surfaces.The relationship between pavement surface friction and texture is explored using machine learning models.The results reveal that the random forest and gradient boosting decision tree models exhibit the highest accuracy,SVM and CNN perform moderately,while the traditional linear regression method fares the worst.By assessing the importance of the 38 parameter variables,the most critical 21 variables were selected for model development.Test results demonstrate that the GBDT model exhibits the best predictive performance,with an explanatory capability of 87.4%for road friction performance.The findings demonstrate the feasibility of replacing contact-based pavement friction evaluation with non-contact texture measurements,offering promising prospects for a network-level pavement skid resistance monitoring and management system.
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%.
文摘The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization.As a result of this shift,there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology(RSM)and machine learning(ML)techniques.The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing,modeling,and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals(SDGs).These include Goals 9(sustainable infrastructure),11(urban resilience),12(sustainable construction strategies),13(climate action through optimized materials),and 17(multidisciplinary interaction).A thorough search of the ScienceDirect,Web of Science,and Scopus databases from 2010 to 2023 yielded 1249 relevant records,with 125 studies closely examined.Over the last thirteen years,there has been significant research growth in RSM and ML applications,particularly in ML-based pavement optimization.The study shows that the topic has a global presence,with notable contributions from Asia,North America,Europe,and other continents.Researchers have concentrated on utilizing sophisticated ML models such as support vector machines(SVM),artificial neural networks(ANN),and Bayesian networks for prediction.Also,the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables.Key contributors include the United States,China,and Malaysia,with global efforts focused on sustainable materials and approaches to reduce impact on the environment.Furthermore,the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability,which contributes significantly to SDGs 9,11,12,13,and 17.Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.
基金supported in part by the key project supported by the Joint Funds of the National Natural Science Foundation of China(grant No.U2433210)Shaanxi Province Postdoctoral Science Foundation(2024BSHSDZZ225)+1 种基金Natural Science Basic Research Program of Shaanxi Province(2025JC-YBQN-595)the Fundamental Research Funds for the Central Universities,CHD(300102215102).
文摘The use of hot recycled asphalt mixture(HRAM)allows for a reduction in the depletion of non-renewable resources and presents environmental benefits.However,concerns arise regarding the performance of HRAM due to the lower degree of blending(DOB)of virgin and aged asphalt(V&A asphalt).This paper aims to provide an up-to-date review on the DOB of V&A asphalt in HRAM.Initially,the paper introduces the DOB of V&A asphalt,followed by an analysis of the blending theory,evaluation methods,and influencing factors of DOB.Subsequently,the effect of DOB on the performance of HRAM is investigated,and molecular dynamic simulation is utilized to analyze the blend of V&A asphalt.Finally,methods for improving DOB are summarized.It was found that the use of high-resolution microscopy with tracer methods such as SEM/EDS was an effective way to characterize DOB.Furthermore,the chemical composition and colloid structure between virgin and aged asphalt are crucial to DOB.Additionally,improving DOB by utilizing the coupling effect of time and temperature during transportation,paving,and compaction stages is promising.Future research should focus on standardizing test methods,refining field simulation models,and developing intelligent construction technologies to achieve more efficient and durable blending.This review provides theoretical guidance and practical references for improving the DOB of V&A asphalt and promoting sustainable pavement construction.
文摘Road pavements in tunnels are usually made of asphalt mixtures,which,unfortunately,are flammable materials.Hence,this type of pavement could release heat,and more specifically smoke,in the event of a tunnel fire,thereby worsening the environmental conditions for human health.Extensive research has been conducted in recent years to enhance the fire reaction of traditional asphalt mixtures for the road pavements used in tunnels.The addition of the Flame Retardants(FRs)in conventional asphalt mixtures appears to be promising.Nevertheless,the potential effects of the FRs in terms of the reduction in consequences on tunnel users in the event of a large fire do not seem to have been sufficiently investigated by using fluid dynamics analysis as a computational tool.Given this gap of knowledge,this article aims to quantitatively evaluate whether the use of flame-retarded asphalt mixtures,as opposed to traditional ones without FRs,might mitigate the adverse effects on the safety of evacuees and fire brigade by performing numerical analyses in the case of a tunnel fire.To achieve this goal,3D Computational Fluid Dynamics(CFD)models,which were executed using the Fire Dynamics Simulator(FDS)tool,were established in the case of a major fire of a Heavy Goods Vehicle(HGV)characterized by a maximum Heat Release Rate(HRRmax)of 100 MW.The people evacuation process was also simulated,and the Evac tool was used.Compared to the traditional asphalt pavements without FRs,the simulation findings indicated that the addition of the FRs causes a reduction in CO and CO_(2)levels in the tunnel during the aforementioned fire,with a minor number of evacuees being exposed to the risk of incapacity to self-evacuate,as well as certain safety benefits for the operability of the firefighters entering the tunnel downstream of the fire when the tunnel is naturally ventilated.
基金jointly supported by the National Natural Science Foundation of China(No.52308332)the China Postdoctoral Science Foundation(Grant No.2022M712787).
文摘The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.
文摘The elevated temperatures adversely affect the durability and lifespan of pavement.Understanding the factors that influence asphalt pavement temperature offers valuable insights for creating climate-friendly cities with cooler pavement surfaces.In this study,three aggregates of varying types and colors,two types of bitumen(one without pigment and one with the addition of red pigment,Fe2O3),and two levels of mean texture depth(MTD),high and low,were utilized to create asphalt samples using Marshall's method.A total of 38 thermocouple sensors were employed to simultaneously record temperatures in three areas within the samples,as well as the temperatures in shaded and sunlit conditions over a period of 17 days.Furthermore,a comprehensive evaluation was conducted to assess the impact of each factor on the solar reflectance index(SRI).Twelve general linear models(GLMs)were developed using a full factorial design of experiment,and five models with an R2 greater than 95%were evaluated and analyzed.The analysis,based on the coefficients derived from the GLMs,indicates that the mean MTD is the most significant parameter affecting surface temperature.Pigment color emerged as the second most influential factor affecting both surface and bottom temperatures.Additionally,the findings revealed that MTD has the greatest impact on the SRI,followed by pigment color and aggregate color.It was also determined that the interaction between density,pigment color,and aggregate color plays a crucial role in determining the temperatures of both the surface and bottom of the specimens.
文摘The performance of roller compacted concrete(RCC)was greatly influenced by variations in material proportion,optimum moisture content,density of mixes and methodology adopted making it different from conventional concrete mixes.Even though RCC has gained popularity,the complex phenomenon involved in developing the RCC mixes limits it from large-scale applications.In this study,reclaimed asphalt pavement(RAP)incorporated roller-compacted geopolymer concrete(RGC)mixes were developed herein with different compaction techniques such as vibratory hammer(VH),modified proctor(MP),vibration table(VT)and compression machine(CM)are studied and compared with control mixes of natural aggregates.Initially,the effect of alkali solutions such as sodium hydroxide(SH)and sodium silicate(SS)on the physical properties.During,the second phase mechanical properties such as dry density,compressive,flexural and split-tensile strength,modulus of elasticity and microstructure properties will be investigated.The test results revealed that compaction efforts were greatly influenced by the alkali solution.Furthermore,the poor bond characteristics between RAP and the binder matrix had a significant effect on strength properties.Also,the various compaction techniques affected the mechanical properties of mixes developed herein.In Comparison with various compaction efforts,VH and MP produced comparable results,whereas the VT method underestimated and overestimated the various strength properties.Although,the CM method reports comparable results but difficult to maintain consistency in strength aspects.Therefore,optimization of various parameters influencing the concrete properties needs to be achieved for field density.
