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The presence of road cracks is an important indicator of damage. Deep learning is a prevailing method for detecting cracks in road surface images because of its detection ability. Previous research works focused on supervised convolutional neural networks (CNNs) without non‐crack features or unsupervised crack analysis with limited accuracies. The novelty of this study is the addition of background classification. By increasing the number of non‐crack categories, CNNs are driven to learn non‐crack features and improve crack detection performances. Non‐crack images are preprocessed, and their features are extracted in an unsupervised way by a deep convolutional autoencoder. A self‐organizing map clusters features to obtain non‐crack categories. This study focusses on classification though the method can be adopted in parallel with the latest segmentation algorithms. Using common road crack datasets, modified deep CNN models significantly improved accuracy by 1%–4% and f‐measure by 3%–8%, compared to previous models. The modified visual geometry group (VGG) 16 showed the top‐level performance, 96% accuracy and 84%–85% f‐measure. The models drastically reduced false detection cases while maintaining their crack detection abilities. [ABSTRACT FROM AUTHOR]

An efficient tractor path-tracking method can increase the operational accuracy, improve land utilisation, and more efficiently provide services for autonomous tractors and precision agriculture. In this study, a model-free adaptive predictive control-proportional-integral-derivative (MFAPC-PID) method was devised. Based on preview theory, a tracking system based on course deviation angle was designed. After the tracking system was linearised, an MFAPC tracking system was developed, and its inherent defects were analysed. Referring to the control structure of an incremental PID algorithm, the MFAPC was considered as an adaptive integral item, and the MFAPC-PID controller was obtained by adding adaptive proportional and differential terms. To verify the proposed controller, collaborative simulation and hardware-in-the-loop tests were performed. The controller was simulated and tested under different paths, road surfaces, and tractor models. Compared with other methods, the MFAPC-PID path-tracking method exhibits superior comprehensive performance, adaptability, universality, and robustness. Moreover, the MFAPC-PID method is insensitive to external interference and model changes of controlled objects. • A model-free path-tracking method is developed. • MFAPC–PID controller was designed to improve the structure of the MFAPC algorithm. • The Algorithm is suitable for different paths and road surfaces. • The algorithm has adaptability, universality, robustness. [ABSTRACT FROM AUTHOR]

Because of the gradual depletion of gravel and river sand resources and restrictions on quarrying activities to maintain the ecological balance, limestone crushed sand with a high content of limestone fillers is being explored to be used, as an alternative source of supply, in Portland cement concrete (PCC) for rigid pavement. This paper aims to first present mechanical test results carried out on three types of PCC formulated with crushed limestone sand as fine aggregates, devoid of superplasticizers. Since shrinkage cracking is an important problem for rigid pavement, the shrinkage behaviour of these concretes is then experimentally investigated. Furthermore, three existing models (ACI-209, Eurocode2, and triple-sphere models) and a novel-developed model are used to estimate the mixtures' shrinkage. Results showed that the designed mixtures exhibited normal shrinkage values. The shrinkage modelling revealed that the proposed model well fitted the experimental data. Besides, Eurocode2 and ACI-209 models may be adapted for the case of limestone concretes containing a high content of limestone fillers and then used to accurately estimate their shrinkage strains. However, the triple-sphere model was unsuitable for these concretes and overestimated their ultimate shrinkage since it does not take into account their high filler content. Nevertheless, by adjusting the paste shrinkage expression, the ultimate shrinkage strain values of the tested mixtures were improved. [ABSTRACT FROM AUTHOR]

Modal parameter estimation of a bridge with the vibration responses measured from an instrumented vehicle moving at a controlled speed is an active area of research. It is challenging to determine bridge natural frequencies and their associated mode shapes, as the measured output-only responses of an instrumented vehicle contain the desired dynamic responses of the bridge, along with the other confounding components related to vehicle dynamics, driving frequency component, and dynamics associated with the roughness profile of the road. These bridge responses are often masked by the dynamic responses associated with the vehicle, and road surface profile. Measurement noise components add to the existing problem of separating bridge frequency from various other said components. In this paper, an attempt has been made to extract bridge frequencies and mode shapes through the output-only responses collected from a traversing vehicle and using singular value decomposition (SVD) combined with the Teager-Kaiser energy operator (TKEO). Numerical investigations are made on the proposed SVD-TKEO-based modal identification technique in the presence of measurement noise. Parametric studies are conducted to investigate the influence of vehicle speed and road surface roughness on the quality of the identified bridge modal parameters using the proposed technique Numerical simulations carried out, show that the proposed SVD-TKEO-based algorithm performs well in identifying bridge mode shapes, even with relatively higher vehicle traveling speed, and handles even roughness of the road surface profile reasonably well. Lab-level experimental studies using vehicle bridge interaction setup, are also carried out using the SVD-based modal parameter estimation technique to explore its practical use. [ABSTRACT FROM AUTHOR]

Rigid airport pavements are a critical part of the Australian airport pavement inventory. The thickness design of rigid airport pavements in Australia was previously based on Australian developed charts and a conservative characteristic concrete strength, but in recent years this has changed to reliance on the software FAARFIELD provided by the US. Australian designers have used FAARFIELD in combination with the historical and conservative definition of concrete strength. That conflicts with the intended use of a less conservative Australian concrete strength value for inputting into FAARFIELD. The unintended consequence of this change in Australian practice is an additional 44 to 74 mm of concrete slab thickness, depending on the aircraft type, subgrade support, and concrete strength used. This additional thickness costs an additional AUD 185,000 to AUD 429,000 and 154,000 to 333,000 kg.eCO2 for a modest 100×100-m rigid airport pavement development. This additional financial and environmental burden is not justified, and Australian airport pavement designers should adjust their thickness design calculations to avoid this unintended overdesign in the future. This example also serves as a lesson regarding the potential consequence of mixing elements from different systems for pavement thickness design. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess the impact of mineral grain size and grain size distribution of aggregates on the deterioration of pavement surface microtexture. For the study, aggregates were collected from nine different sources to cover a wide variation in mineralogical type. The grain size parameters were obtained by detecting mineral grain boundaries using scanning electron microscope electron back scattered diffraction (SEM EBSD) analysis on thin sections prepared from the aggregates. Scanning electron microscope (SEM) analysis was also carried out on thin sections of all aggregate types to determine their mineralogical composition. Microtexture deterioration was measured by collecting the British pendulum number (BPN) of the aggregate after every hour of polishing for six hours using an accelerated polishing machine. From the mineral composition analysis, it was observed that the aggregates in the study belonged to three rock types: granite, amphibolite, and basalt (three sources of each type). The BPN values of the polished stones indicated that aggregates with finer grains offer more resistance to polishing. Statistical analysis of the test results also suggests that a wide variation in grain sizes in aggregates positively contributes to resistance against polishing. The study confirmed that, for a given rock type, coarse aggregates with finer grains and greater variation in grain size can better withstand microtexture deterioration caused by mechanical polishing from vehicular loads when used in the surface course of pavements. [ABSTRACT FROM AUTHOR]

Existing approaches to infrastructure management neglect the impact of infrastructure condition on safety performance, even though poorly maintained roadways can contribute to increased crash frequencies. This is perhaps because very few studies have quantified the impact of infrastructure condition on safety performance. Hence, this work develops safety performance function (SPF) models that relate pavement condition—specifically, roughness, as measured using the international roughness index (IRI)—and other roadway factors with observed crash frequencies. These SPFs are then combined with pavement roughness deterioration models to better understand how maintenance decisions can be used to improve roadway safety performance. Data from two-lane rural roadways in Pennsylvania are used for this effort. The SPF estimation results reveal that accounting for IRI in SPF development significantly improves model fit, and that IRI has a significant and positive impact on crash outcomes. The results further suggest that the IRI has a different impact on total crash frequency as compared to fatal and injury crash frequency, and rear-end crashes. This is likely due to how roughness of the roadway can impact travel speeds. Finally, the results also show that maintenance activities that reduce IRI can have significant safety benefits. This suggests that pavement management decisions should also consider the anticipated safety benefits, in addition to other factors. [ABSTRACT FROM AUTHOR]

Pavement roughness has long been linked to both vehicle fuel efficiency and pavement structural degradation. Perceptions of pavement roughness, however, may vary among users of different sociodemographic features and therefore potentially impact the allocation of resources for highway maintenance and rehabilitation when equity is considered. It is necessary to investigate the major factors that influence the user perception of pavement roughness. Considering the complexity in structure and relationship of field data, this study applied three state-of-the-art machine learning and statistical methods, including a classification tree, a random-parameter ordered-probit model with a random effect (Model 1), and a correlated random-parameter ordered-probit model (Model 2), to the analysis of user perceived roughness. Data were collected from a prior study that conducted in-vehicle tests involving individual user, pavement, and vehicle. The analysis identified more key factors influencing roughness ranking than previous research and accounted for the heterogeneity in individuals and interactions among random parameters. The results indicate that, whereas physical measurements of pavement roughness (e.g., International Roughness Index), visible distresses such as patches and faulting, and joints provided a strong indication of user roughness ranking, other factors (i.e., particular regularly used route, participants' age, income, and gender, number of household infants, and interior vehicle noise levels) were also statistically significant. Two-way group random effects were statistically significant in the data, which should be accounted for in future studies. Results from this study fill an important gap between making accurate prediction and uncovering underlying causality in research of physical infrastructure measurement with user perceptions of infrastructure conditions. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMS) are considered to be crucial tools for agencies in managing their pavement networks and using accessible budgets in an appropriate technique. In this paper, a newly developed condition rating index [modified pavement condition rating (MPCR)] was used to assess the current and future pavement performance. The pavement future performance in terms of MPCR was predicted using two models: (1) a stochastic Markov chain model, and (2) an artificial neural network (ANN) model. In Markov chain modeling, the Long-Term Pavement Performance (LTPP) database was used to develop the transition probability matrix (TPM). An ANN model for future pavement performance prediction was developed based on the same LTPP data. For both models, 245 data points from the LTPP data were used for deterioration modeling. Both models were verified using LTPP data and data obtained from the General Authority of Roads, Bridges and Land Transport (GARBLT), Egypt. The Markov chain and ANN results were compared. The Markov chain model performed better than the ANN model, with a coefficient of determination, R2 , of 0.9 and RMS Error (RMSE) of 0.1268. the ANN model yielded an R2 of only 0.53 and a RMSE of 0.2205. Increasing the number data points did not lead to a significant improvement in the ANN model accuracy. A multiobjective particle swarm optimization (PSO) model is used for fund allocation to maximize the average pavement condition and minimize the maintenance cost. The developed optimization model was tested on two benchmark optimization problems to ensure its validity for real-life applications. The developed optimization model was used on a network of roads and was found to be capable of generating optimal or near-optimal solutions for the maintenance decisions to keep the pavement in good workable condition. As such, the current study presents a comprehensive development tackling different modules of a pavement management system. [ABSTRACT FROM AUTHOR]

As a step toward revolutionizing and optimizing freight transport, autonomous truck platooning technology (i.e., a set of connected heavy-duty trucks traveling closely at specific headway intervals) has been the center of research over last few years. Connected autonomous vehicles (CAV), in the form of truck platooning, can be implemented as a novel contribution to sustainable freight transport with the potential of offering the following synergistic benefits, among others: road safety improvements, enhanced economic prospects, and environmental preservation. In this study, the impacts of fixed-path truck platooning on pavement performance were modeled and numerically quantified using elastic and dynamic-viscoelastic finite-element (FE) methods. Factors including pavement layers' geometrical and mechanical characteristics, traffic characteristics and counts, axle configuration, and seasonal temperature are analyzed to address the platooning effect in this study. Yet, other possible wandering patterns between the fixed-path and normally distributed wandering patterns, as well as the effects of speed variations, driving behaviors, and tire pressure (i.e., variation in dynamic loading), can be further studied. The mechanical responses (namely displacements, strains, and stresses) obtained from the FE modeling were used to predict the effects of truck platooning on the pavement performance due to limited wandering (lateral movement of truck tires). The FE modeling results indicated that the channeling effects (i.e., limited tire wandering) of truck platooning have negative effects on the pavement performance with respect to decay in fatigue life and increase in permanent deformation. With fixed-path platooning, the fatigue life is reduced in the range of 13.9% to 34.5% in terms of the number of load cycles over the 20-year design life. The permanent deformation (rutting) value over a fixed period is increased by a factor of 1.2 to 2.9. [ABSTRACT FROM AUTHOR]

Self-melting ice asphalt pavement materials inhibit pavement freezing and improve driving safety. This paper aims to study the long-term salt release characteristics of self-melting ice asphalt mixtures and the impact on pavement after complete salt release. Firstly, a method to accelerate the rapid release of salt based on the Los Angeles abrasion tester. Then, long-term salt release patterns were elucidated under the influence of deicing agent dosage, type of asphalt, and type of gradation. Finally, a quantitative analysis of the pavement performance after complete salt release is conducted. The results indicate that the release efficiency of the Los Angeles abrasion tester method has increased by 91 times compared to the magnetic stirrer immersion flushing method and by 114 times compared to the natural soaking method. The SBS-modified self-melting ice asphalt mixture possesses a longer duration of salt release, but the uniformity of salt release is inferior. Salt release duration is directly proportional to the dosage of deicing agents. SMA-13 self-melting ice asphalt mixture exhibits poorer uniformity in salt release. After complete salt release, high-temperature stability of self-melting ice asphalt mixtures decreased by 31.6%, low-temperature performance decreased by 15.4%, water stability decreased by 26.7%, and fatigue life decreased by 35.9%. [ABSTRACT FROM AUTHOR]

The composite of a phase change material (PCM) and bitumen or asphalt as a matrix is expected as a new, advanced material for road construction. The main motivation for this article was to show the new possibilities and perspectives of developing the pavement with the usage of PCMs. Incorporating PCMs into paving materials can improve their properties, including allowing the regulation of the pavement temperature, enhancement of the pavement durability, and avoiding the phenomenon of a heat-island on the road. The main purpose of this article was to evaluate contemporary investigations in the area of the application of PCMs in pavement materials, especially asphalt and bitumen; to summarize the advantages and disadvantages of the implementation of PCM for road construction; and to discuss further trends in this area. This manuscript explored the state of the art in this area based on research in the literature. It shows the possible material solutions, presenting their composition and discussing their key properties and the manufacturing technologies used. The possibilities for further implementations are considered, especially economic issues. [ABSTRACT FROM AUTHOR]

This article presents a systematic review of the most cutting-edge research on precast pavement technology for the first time. Firstly, precast pavement is divided into two categories, precast cement concrete pavement and precast carpeted flexible pavement, according to the application of precast technology in pavement engineering. Subsequently, the structural characteristics, advantages, and disadvantages of various precast pavement systems are compared and analyzed; technical problems in precast pavement systems are explained; and future development directions are identified. In addition, the text specifically mentions the great contribution of precast carpeted flexible pavement technology in reducing the harmful effects of asphalt fumes on humans and the environment. This work will promote the application of prefabrication in road engineering and provide suggestions and references for subsequent research. [ABSTRACT FROM AUTHOR]

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone's perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. [ABSTRACT FROM AUTHOR]

To address the shortage of the aggregate used in a hot mix asphalt (HMA) pavement in Guangxi, properties such as the aggregate crushing, polished stone, and Los Angeles abrasion values of a type of hard sandstone aggregate used in HMA were tested after various conditioning treatments. The hard sandstone aggregate met the technical requirements for aggregate in HMA. In addition, the influence of the Marshall compaction on the hard sandstone aggregate-combined grading was tested. The combined grading curve changed a little, and the aggregate satisfied the corresponding technical requirements. Therefore, according to the abovementioned results, the hard sandstone aggregate can be used as a coarse aggregate in HMA. [ABSTRACT FROM AUTHOR]

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called "progressive pretraining (PPT)." We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular. [ABSTRACT FROM AUTHOR]

Pavement asset management system (PAMS) is a stakeholder and data-driven approach that uses engineering tools to select strategies for rehabilitation and maintenance interventions. However, the impact of maintenance interventions on stakeholders' satisfaction is seldom considered in decision-making. For a given road network, the degree of interest, power, and influence of stakeholders on the suggested maintenance decision vary and hence causes dissatisfaction due to inefficient stakeholder management practices. Thus, the objective of this study was to develop a multi-criteria decision-making approach-based optimization model to select pavement maintenance interventions with an aim to endeavor a balance between PAMS decisions and stakeholders' satisfaction. First, engineering analysis was performed to ascertain the maintenance interventions without stakeholder participation. Second, a systematic stakeholder-centric analysis on pavement maintenance interventions was performed. Finally, an optimization tool that balances engineering analysis and stakeholder satisfaction was developed using the analytic hierarchy process and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. A set of four road sections as a case study in the State of Andhra Pradesh, India, was used to develop and test the model. It is anticipated that the optimization tool could be used to ascertain optimum maintenance intervention that equilibrates stakeholder needs and manages budgetary constraints. Also, this study is envisioned to provide evidence that the proposed methodology will be a rational approach to improve stakeholder participation in the overall PAMS process. [ABSTRACT FROM AUTHOR]

As part of this research, the shear process during asphalt paving with the paver screed was investigated in the laboratory. An extensive international literature search revealed that the influence of the shear process on the compaction success during asphalt paving has not been investigated to date. In addition, only limited data on the coefficient of friction between the asphalt mix and the screed plate exist from in-situ measurements. The investigations in the laboratory were carried out with a customized frame shear device of the Institute of Geotechnics at the Technical University of Darmstadt. The asphalt mix types AC 11 DS, SMA 11 DS and AC 16 BS (according to the German guideline TL Asphalt-StB) were used for this purpose. In addition, the influence of temperature changes and grading curve changes was investigated using the AC 11 DS asphalt mix. On the basis of the results, it was possible to prove for the first time that a significant increase in density takes place as a result of the shear process under constant normal stress. Where the influence on the compaction success depends on the asphalt composition. In addition, in-situ measurements could be validated based on determined sliding friction coefficients. The friction coefficients showed a material and temperature-dependent behavior. [ABSTRACT FROM AUTHOR]

The pre-eminence of concrete as the constitutional material used for rigid pavement construction has emanated through the millennia thereby amplifying the concern associated with its adverse effect on the environment. In this context, geopolymer concrete not only proposes a sustainable solution but also addresses the problems associated with extensive industrial waste generation and its imprudent disposal. This study aims to highlight the relevance of fly ash (FA) and red mud (RM) as geopolymer synthesizing source materials for pavement quality concrete (PQC). Investigations were designed to study the effect of varying replacement levels of FA (10%, 20%, 30%, and 40%) with RM in conjunction with different concentrations of sodium hydroxide in terms of molarity (10 M, 12 M, 14 M). The specimens subjected to ambient curing and oven curing at 60 °C were tested for compressive strength, flexural strength, and split tensile strength. Results portray that in the case of ambient cured FA-RM specimens, the highest compressive strength and flexural strength that could be achieved were 31.5 MPa and 4.32 MPa, respectively. Thereby, indicating inadequate strength development as per the requirements of PQC, even at a higher concentration of sodium hydroxide. Moreover, replacing FA with RM is mostly not effective at replacement values greater than 20% in both compression and tension. Furthermore, a defined study for assessing durability properties was also conducted by immersing the specimens in a 5% sulfuric acid solution. 11.46% and 7.25% reduction in compressive strength of control mixes was observed for specimens corresponding to 10 M and 14 M respectively, thereby substantiating decreased strength loss at higher molarity. [ABSTRACT FROM AUTHOR]

This paper presents a semi-analytical approach to assess the structural responses of low-volume roads using StabilRoad stabilizer and its application with cement for low-volume roads. A finite-element response model is developed using ABAQUS to catechize the suitability of this stabilizer on the base and subgrade layer of low-volume flexible pavement sections. Aspects related to strength characteristics, rutting deformation, microscopic behaviour are analyzed. Vertical subgrade stress and surface deflections are considerably reduced with the inclusion of StabilRoad stabilized base. More importantly, this mechanistic-empirical approach clearly demonstrates the potentiality of this stabilizer to be effectively applied in unbound layers of such pavement systems. As of the increase in the intensity of C–S–H peaks, the strength properties of the cement-modified soil mixture are improved with the incorporation of this stabilizer. Considerable reduction in the thickness of the pavement was observed with the introduction of the stabilized base by offsetting to the tune of about 40–50% to that of conventional pavement and thereby saving the overall cost of pavement. [ABSTRACT FROM AUTHOR]

Conducting and repeating large-scale cyclic load tests are challenging, as these tests are time-consuming, labour-intensive and demanding advanced laboratory facilities. As a screening test method to select the geosynthetic-reinforced pavement configurations for further investigations on a large scale, small-scale cyclic load tests by using laboratory experimental setups with lesser dimensions, therefore, are a preferred starting point for any cyclic loading tests. This paper presents the outcomes of a series of small-scale cyclic loading tests, evaluating the rutting performance of unreinforced and geogrid-reinforced unpaved roads using a circular mould with a 305 mm-diameter and 520 mm height. The test apparatus was capable of conducting a greater number of tests and provided better statistical confidence with relatively minimal effort compared to larger box tests reported in the literature. These cyclic loading tests were conducted on a granular base of varying thicknesses, placing the selected geogrid-reinforcement types (i.e. composite or biaxial) within the granular layer and/or at the base-subgrade interface. Test results show geogrid reinforcement placed at the base–subgrade interface can significantly reduce permanent-vertical deformation when the granular base thickness is relatively thin. Results also show biaxial geogrid in the middle of the granular base performs well in reducing rut depth compared with composite or biaxial geogrid placed at the base–subgrade interface. The potential benefits of geogrid reinforcement diminish with an increase in base thickness because the reinforcement acts as a rigid boundary, and failure would occur above the top layer reinforcement. Better rut resistance in unpaved granular pavements was exhibited first by double reinforcement, then biaxial geogrid at the middle of the granular base, then composite geogrid at the middle of the granular base, followed by composite geogrid at the base–subgrade interface and then biaxial geogrid at the base–subgrade interface. The findings of this experimental study are beneficial for pavement researchers in designing and planning large-scale cyclic loading laboratory or field tests for future research by optimising the number of tests, thus reducing the required amount of materials, cost and effort. [ABSTRACT FROM AUTHOR]

This paper presents a new accurate method to compute the mechanical response of pavement structures using an Artificial Neural Network (ANN) model coupled with Multi-Layer Elastic Analysis (MLEA). The ANN model is used to improve the numerical integration of the response function used in the MLEA method. It requires four inputs: total pavement thickness, the diameter of the contact area, radial distance, and depth of the response point; and it was trained on one million hypothetical pavement structures. The developed method has been validated by a comparative analysis against boundary conditions, finite element analysis, and available MLEA solutions using various hypothetical pavement structures. The results demonstrate that the developed solution gives excellent response in the vicinity of the pavement surface together with a significant improvement in computational efficiency. [ABSTRACT FROM AUTHOR]

During the compaction process of HMA pavement, it is common to spray cold water on the wheel of a road roller to prevent the mixture from sticking to the wheel, which might deteriorate the bonding strength between the asphalt binder and aggregate, and consequently lead to surface polishing of the pavement. This paper aims to demonstrate whether the water used during the compaction process affects the surface performance of HMA pavement. In this study, the black pixel ratio and mass loss ratio were used to evaluate the water effect on the surface performance of asphalt pavement, considering the water consumption, molding temperature and long-term ageing process. The test results indicated that the water used during the compaction process would increase the risk of surface polishing of HMA pavement. This adverse effect became more significant if the HMA samples were prepared using greater water consumption, a greater molding temperature and a long-term ageing process. Moreover, there exists a certain correlation between the black pixel ratio and mass loss ratio, and their relationships were demonstrated by the experimental results in this study. It is recommended that further research concentrates on the influencing mechanism and the treatment strategy for the adverse effect caused by the water used during the compaction process. The use of more types of asphalt binders, aggregate and methodologies is also recommended in further studies. [ABSTRACT FROM AUTHOR]

With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire–road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, known as the contact patch, is a key parameter for determining a vehicle's effectiveness in accelerating, braking, and steering at various velocities. Road unevenness from features such as potholes and cracks results in large fluctuations in the contact patch surface area. Such conditions can eventually require the driver to perform driving maneuvers unorthodox to normal traffic patterns, such as excessive pedal depressions or large steering inputs, which can escalate to hazards such as the loss of control or impact. The integration of sensors into the inner liner of a tire has proven to be a promising method for extracting real-time tire-to-road contact patch interface data. In this research, a tire model is developed using Abaqus/CAE and analyzed using Abaqus/Explicit to study the nonlinear behavior of a rolling tire. Strain variations are investigated at the contact patch in three major longitudinal slip driving scenarios, including acceleration, braking, and free-rolling. Multiple vertical loading conditions on the tire are applied and studied. An intelligent tire prototype called KU-iTire is developed and tested to validate the strain results obtained from the simulations. Similar operating and loading conditions are applied to the physical prototype and the simulation model such that valid comparisons can be made. The experimental investigation focuses on the effectiveness of providing usable and reliable tire-to-road contact patch strain variation data under several longitudinal slip operating conditions. In this research, a correlation between FEA and experimental testing was observed between strain shape for free-rolling, acceleration, and braking conditions. A relationship between peak longitudinal strain and vertical load in free-rolling driving conditions was also observed and a correlation was observed between FEA and physical testing. [ABSTRACT FROM AUTHOR]

This study concerns road lighting for pedestrians. Many experiments have been conducted to determine how changes in lighting affect the ability to make interpersonal evaluations, usually considering variations in light level or light spectrum. Here, we consider an alternative approach, predicting performance using an existing model, Relative Visual Performance. The results show that face evaluation ability is affected by adaptation luminance, pavement surface reflectance, observer age, and skin tone of the observed person. Previous experimental studies have tended to use young test participants to evaluate Caucasian or Asian faces: if the situation instead involved an elderly person evaluating a face of South African skin tone, then the current analysis predicts that for optimal performance the light level would need to be doubled. [ABSTRACT FROM AUTHOR]

Dementia is a global public health priority. Physical activity has myriad health benefits, including for reducing dementia risk. To increase physical activity, detailed understanding of influencing factors is needed. Socioeconomic deprivation affects many aspects of health and wellbeing. Qualitative research with older people experiencing socioeconomic deprivation is needed to explore barriers and enablers to engaging in physical activity, with the view to co-designing interventions for implementation trials. A whole of society approach is pivotal to improving effectiveness of physical activity interventions for older adults with cognitive impairment, and target support for people experiencing socioeconomic deprivation, to improve their health outcomes. [ABSTRACT FROM AUTHOR]

This study investigated the use of recycled glass aggregate (RG) as unbound base/subbase material. The experimental tests included compaction, Los Angeles, Micro-Deval, and California bearing ratio (CBR) tests. In this regard, a fine proportion of course limestone aggregate (MG20) was replaced by RG with size ranging from 0 to 5 mm based on the volumetric method. Adding RG to the coarse aggregate improved durability in wet conditions, which is more representative of the field condition of Canada/Quebec, but decreased in dry conditions. CBR values decreased with increasing RG inclusion, but all blends with 0%–100% RG in the fine fraction of MG20 met minimum requirements for unbound granular layers in Quebec. A simple model predicting the resilient modulus values of these materials based on CBR values at different stress levels was suggested. This equation estimates Mr values of various aggregate-RG blends under a wide range of mean stresses based on their CBR values. [ABSTRACT FROM AUTHOR]

In the planning stage of a BOT highway project, the future traffic demand plays a pivotal role on designing contract factors. Considering that the traffic demand generally has variations with time, the constant toll during the concession period obviously is not a good choice. And the different toll charges classified by vehicles types definitely affect the traffic composition and maintenance cost. To address this practical issue, this study proposes the mixed integer nonlinear programming models to select the period-dependent toll charges for multi-type vehicles and highway capacity, by considering both the objectives of the government and private firms. Then two solution algorithms are provided to solve our proposed models. Finally, numerical experiments are conducted to assess the applicability and efficiency of our models and algorithms. • Based on an empirical formula, pavement rehabilitation costs are estimated by the time-varying traffic demand. • Mixed integer nonlinear programming models are proposed to select the optimal highway capacity and toll charges by vehicle type. • Two solution algorithms are provided to solve our proposed model. • Numerical experiments are conducted to assess our proposed methodology. [ABSTRACT FROM AUTHOR]

Rapid road network expansion has heightened the importance of surface condition information for traffic accident prevention and route optimization. This article introduces a laser diode-based sensor that identifies seven surface conditions and accurately measure ice, water, and snow film thicknesses on roads. An optical module was developed to detect weak optical signals based on the characteristic absorption spectrum of the target surface. The module used three laser diodes (1310, 1440, and 1550 nm wavelengths) as light sources. Additionally, a road classification algorithm that is adaptable to foggy weather was developed using a multi-wavelength processing protocol. The sensor was subjected to numerous calibration and performance verification experiments. During thick foggy measurements, the processed spectra displayed a maximum variation of 2.372% across a 600 to 25,000 m visibility range with a relative standard deviation of only 0.328%. This demonstrated effective weakening of the effects of visibility variations. During winter field testing, the sensor classified road conditions effectively and accurately measured ice, snow, and water film thicknesses, with a correlation coefficient of 0.97444. The accuracy of the measurements was less than 0.5 mm. The sensor's effectiveness for long-term field-based road testing has been verified. [ABSTRACT FROM AUTHOR]

In this investigation, four types of sands as alternatives to river sand, namely, crushed sand (CS), marine sand, fine recycled concrete aggregate (FRCA), and fine reclaimed asphalt pavement (FRAP), were studied for their potential for cement-concrete pavement applications. Initially, the physicochemical and morphological characteristics of these sands were understood and later linked to the hydration kinetics and the behavior of cement-mortar systems in both the fresh and hardened states. Due to their evenly graded particle size distribution, CS and FRCA exhibited higher packing densities despite having more gradient angularity, whereas the interlocking capacity of FRAP was found to be affected by the agglomerated particles. Morphology, fineness modulus, and chemical composition of the sands were observed to be the dominating parameters affecting the hydration kinetics and thus the performance of the mortar mixes. Improving these parameters, especially the grain size distribution, could help in enhancing the potential of these sands for pavement applications. [ABSTRACT FROM AUTHOR]

Degradable biomedical elastomers (DBE), characterized by controlled biodegradability, excellent biocompatibility, tailored elasticity, and favorable network design and processability, have become indispensable in tissue repair. This review critically examines the recent advances of biodegradable elastomers for tissue repair, focusing mainly on degradation mechanisms and evaluation, synthesis and crosslinking methods, microstructure design, processing techniques, and tissue repair applications. The review explores the material composition and cross-linking methods of elastomers used in tissue repair, addressing chemistry-related challenges and structural design considerations. In addition, this review focuses on the processing methods of two- and three-dimensional structures of elastomers, and systematically discusses the contribution of processing methods such as solvent casting, electrostatic spinning, and three-/four-dimensional printing of DBE. Furthermore, we describe recent advances in tissue repair using DBE, and include advances achieved in regenerating different tissues, including nerves, tendons, muscle, cardiac, and bone, highlighting their efficacy and versatility. The review concludes by discussing the current challenges in material selection, biodegradation, bioactivation, and manufacturing in tissue repair, and suggests future research directions. This concise yet comprehensive analysis aims to provide valuable insights and technical guidance for advances in DBE for tissue engineering. [ABSTRACT FROM AUTHOR]

This document provides an overview of the importance of road extraction and distress assessment using remote sensing technology. It discusses various methods and techniques used in this field, such as image classification and object detection. The document introduces a special issue of the journal Remote Sensing that focuses on road extraction and distress assessment, summarizing 12 articles published in the issue. These articles cover topics such as rural road extraction, crack detection, and object recognition. The document emphasizes the potential of remote sensing technology in assessing road pavement health conditions and suggests the need for further research in this area. [Extracted from the article]

When an impact load is sufficiently small, its influence on the pavement structure is mainly from the surface layer material. To explore the influence depth of an impact load and back‐calculation of the pavement surface modulus, both numerical calculation and experimental testing were conducted, and the results are presented in this paper. The numerical calculation was performed through a DEM‐FDM‐coupled model. After a new modulus back‐calculation algorithm is formed by analyzing the numerical modeling results, experimental tests were also conducted for verification, and the results were analyzed. The max value of the falling weight impact force was closely related to the elastic modulus of the material, and the influence depth was controllable. Finally, it is proved that the method can be used to calculate the surface layer modulus and estimate the surface layer thickness. [ABSTRACT FROM AUTHOR]

Our aim is to analyze the function of reinforcement elements (dowel bars) in cement concrete pavement to highlight the influence of these elements on the service life of the pavement as a whole and to find possible alternatives to existing products that are freely available on the Czech market. That is, how these elements in the form of dowel bars should work properly so that the function meets the design assumptions of a rigid pavement. We describe the procedure for inserting dowel bars during paving, which is directly related to the possibilities of using different dowel bars. Finally, we summarize the possibilities of using alternative materials, shapes, or sizes of dowel bars and the possibilities of their use in the Czech Republic. [ABSTRACT FROM AUTHOR]

Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network's parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network's feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers' receptive field range. To optimize the model's boundary loss, we employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing's urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models. [ABSTRACT FROM AUTHOR]

This study proposes a deep learning method for pavement defect detection, focusing on identifying potholes and cracks. A dataset comprising 10,828 images is collected, with 8662 allocated for training, 1083 for validation, and 1083 for testing. Vehicle attitude data are categorized based on three-axis acceleration and attitude change, with 6656 (64%) for training, 1664 (16%) for validation, and 2080 (20%) for testing. The Nvidia Jetson Nano serves as the vehicle-embedded system, transmitting IMU-acquired vehicle data and GoPro-captured images over a 5G network to the server. The server recognizes two damage categories, low-risk and high-risk, storing results in MongoDB. Severe damage triggers immediate alerts to maintenance personnel, while less severe issues are recorded for scheduled maintenance. The method selects YOLOv7 among various object detection models for pavement defect detection, achieving a mAP of 93.3%, a recall rate of 87.8%, a precision of 93.2%, and a processing speed of 30–40 FPS. Bi-LSTM is then chosen for vehicle vibration data processing, yielding 77% mAP, 94.9% recall rate, and 89.8% precision. Integration of the visual and vibration results, along with vehicle speed and travel distance, results in a final recall rate of 90.2% and precision of 83.7% after field testing. [ABSTRACT FROM AUTHOR]

As fleet electrification progresses, vehicles are continuously becoming heavier, while the used electric motors, with their high torques, enable longitudinal dynamics to be maintained or even increased. This raises the question of what effect electric vehicles have on the emission of tire–road particulate matter (PM). To answer this question, investigations were carried out in this study on a tire internal drum test bench with real road surfaces. In addition to the vertical load, the tire inflation pressure and the driving speed were varied. PM emissions were recorded in real time, resulting in emission factors (emission per kilometer driven) for different load conditions. This allows statements to be made about both the effect on the total emission and on the particle size distribution. It was shown that the PM emission increases linearly with the vertical load at constant longitudinal dynamics. If the tire inflation pressure is increased, the emission also increases linearly, and the increases in emission are equally large for both influences. A clear influence of the driving speed on the emission factor could not be determined. With regard to the particle size distribution, the following correlations were found: higher vertical load and higher tire inflation pressure result in a larger mean particle diameter, while a higher driving speed reduces it. Thus, this study contributes to a better understanding of the expected changes in tire-road PM emissions as a result of electrification. [ABSTRACT FROM AUTHOR]

Adding self-melting snow material to asphalt pavement can improve driving safety in winter weather. Self-melting materials can melt the snow on the road surface, without too much negative impact on asphalt pavement. Two commonly used self-melting snow materials are particle type and powder type. Through indoor experiments, the effects of two self-melting snow materials on the high-temperature performance, low-temperature performance, water stability, and anti-stripping performance of asphalt mixtures are compared. The experiment shows that these self-melting snow materials have little effect on the high-temperature and low-temperature performance of the asphalt mixture, and the low-temperature performance of the mixture decreased after freeze-thaw environment. After freeze-thaw cycle or immersion, the snow melt components in the mixture gradually precipitate, leading to the water stability and anti-spalling performance. [ABSTRACT FROM AUTHOR]

The combination of an absorbing structure and a road is a promising strategy for road deicing using microwaves. In this study, cement mortar (CM) specimens containing a carbon fiber screen (CFS) were prepared to concentrate electromagnetic losses on a road surface. The effect of the size and depth of the CFS on the surface heating efficiency of the microwave was studied and optimized, and a microwave deicing experiment was conducted. The results indicated that the destructive interference produced by the CFS led to the effective surface heating of the CM/CFS specimens. The optimal surface heating rate was 0.83 °C/s when the spacing, depth, and width of the CFS were 5.22, 13.31, and 2.80 mm, respectively. The deicing time was shortened by 21.68% from 83 to 65 s, and the heating rate increased by 17.14% from 0.70 to 0.82 °C/s for the specimen with CFS-1, which was 15 mm depth. Our results demonstrate that CM/CFS composite structures can be effectively applied to increase the capacity and accelerate the development of the microwave deicing of roads. [ABSTRACT FROM AUTHOR]

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management. [ABSTRACT FROM AUTHOR]

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ± 0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle's position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs. [ABSTRACT FROM AUTHOR]

Off-label use of pharmaceuticals involves a wide array of aspects ranging from legal and regulatory ones to clinical to safety considerations. Access to off-label therapies is particularly relevant question for patients in areas of unmet medical need. Simultaneously, off-label use also triggers wider considerations relating to social and economic sustainability of health care systems and access to health. National authorities have adapted different regulatory approaches to off-label use of pharmaceuticals, ranging from (1) "regulatory silence"; to (2) allowing off-label use at the discretion of the treating physician; and to (3) a more stringent approach in which off-label use is subject to third party approval. This article provides a brief overview of these different regulatory approaches from a helicopter perspective, and it discusses benefits and shortcomings these approaches. Finally, it presents ideas for preconditions for sustainable and responsible off-label use of pharmaceutical products to ensure patient safety whilst ensuring their timely access to health. [ABSTRACT FROM AUTHOR]

In rainy and snowy weather, traffic flow is influenced by factors such as visibility, slippery road surface, and driving habits, resulting in significant errors in short-term traffic flow prediction. To address this issue, research was conducted and a knowledge graph was constructed for predicting short-term traffic flow at urban intersections in rainy and snowy weather. Firstly, under the influence of rainy and snowy weather, the characteristic parameters of short-term urban traffic flow were calculated. Subsequently, a knowledge graph for traffic flow prediction was constructed through case analysis, ontology and entity layer construction, and storage and display of knowledge graphs. Finally, based on the constructed knowledge graph and short-term traffic flow prediction results, a short-term traffic flow prediction model was constructed using a relationship graph convolutional neural network. The experimental results show that the prediction method proposed in this paper significantly reduces prediction errors, with the lowest RMSE value being 1.045. [ABSTRACT FROM AUTHOR]

Predicting pavement temperature by depth is crucial for road design, analysis, and maintenance. However, current methods predominantly utilize regression and/or open-form solutions focusing on highways. Additionally, most machine-learning models focus on asphalt layers and do not extend to deeper pavement layers. Therefore, this study provides deep-learning models using weather parameters to predict pavement temperature from surface to sublayers and estimate pavement freezing depth for developing massive apartment complexes. Temperature-by-depth data collected from thin pavements from three locations in South Korea were used. Comparative analyses of long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), and convolutional LSTM were performed. Results showed that CNN-LSTM model performed better with coefficients of determination (R2) of 0.965, 0.987, and 0.981. Additionally, the CNN-LSTM predicted freezing depth with 0.3%–13.1% error margins outperforming the LSTM, Aldrich's, and Korean Ministry of Transport approaches. The proposed approach shows that deep-learning models better estimate the freezing depth of pavements than existing approaches. [ABSTRACT FROM AUTHOR]