Resurfacing Research Articles

A Small Robot to Repair Asphalt Road Potholes

As part of the Horizon 2020 InfraROB project aimed at enhancing road safety through innovative robotic solutions, a compact autonomous vehicle has been developed to repair asphalt potholes. Central to this system is a 3D printer capable of extruding a novel cold-asphalt mixture, specifically designed for patching road surfaces. The printer is mounted on a small robot that autonomously navigates to potholes, while the human operator controls the operation from a secure location outside the traffic area. The system’s development involved several key steps: designing the repair mixture, constructing the 3D printer for mixture extrusion, implementing a photogrammetric technique to accurately measure pothole geometry for printing, and integrating the extrusion system with the robotic platform. Two preliminary tests were conducted in controlled environments at Sapienza University of Rome to check the reliability of calculation of the amount of material needed to fill in the potholes. Finally, the entire procedure was tested on an Italian motorway, demonstrating the system’s functionality without encountering operational issues.

SUGARCANE BAGASSE AS AN ALTERNATIVE MATERIAL FOR THE REUSE OF SCARIFIED PAVEMENT MATERIAL

The increasing amount of sugarcane fibrous waste presents environmental problems. The reconstruction of existing highways due to modern globalization results in a larger volume of scarified pavement materials that are often discarded as waste. This study attempts to evaluate the geotechnical characteristics of reclaimed pavement materials that can be stabilized with sugarcane bagasse ash (SCBA). Finding an economical and sustainable way of reusing reclaimed pavement soils instead of using verging laterite soil for road construction could potentially unlock a new dimension. The preliminary results show that sugarcane bagasse ash can potentially improve the reclaimed soil’s strength characteristics, with an increased proportion of sugarcane bagasse ash, Unconfined Compressive Strength (UCS), and California Bearing Ratio (CBR) was found to increase by more than 350% and 100% respectively. The overall geotechnical characteristics are significantly improved by increasing the amount of sugarcane bagasse ash, which make reclaimed soil a potential suitor in relaying and repair maintenance of pavements.

Investigation of the Effect of Compression Force on the Tensile Strength and Infiltration Rate of Pervious Concrete Blocks

Pervious concrete is widely used as a paving material. Pervious pavement is generally constructed by pouring fresh pervious concrete and compacting. However, it has some difficulties such as finishing and curing. In addition, the road has to be closed, until the pervious concrete gains enough strength. Pervious concrete block is a new material that can overcome these difficulties. In this study, the effect of compression force on the strength and infiltration rate of pervious concrete blocks has been investigated. The compaction process was different from traditional methods in this study, and was applied according to predesignated compression forces on fresh pervious concrete mixtures sensitively. Within the scope of the study, 36 different mixtures were produced by applying four different compression forces (25, 50, 75, and 100 kN) in three different sample thicknesses (60, 80, and 100 mm) with three different aggregate sizes (2–4, 4–8, and 8–16 mm). As a result, it was found that while the increase in the compression force increases the splitting tensile strength of pervious concrete blocks with 2–4 and 4–8 mm aggregate, it causes a decrease in the strength due to the aggregate crushing phenomenon in mixtures with 8–16 mm aggregate, 6 cm thick samples. In this study, it was seen that the expectation that the increase in compaction would always cause an increase in strength is not valid, contrary to the literature. The infiltration rate decreased as the compression force increased, as expected. It was determined that the new infiltration rate measurement method has been found effective. Considering the strength requirement in the TS 2824 EN 1338 standard, pervious concrete blocks produced with 4–8 mm aggregate, compressed with 75 kN force and having 80 mm thickness have been determined as the optimum block type.

Quantifying Benefits of Universal Design in Public Transport.

Universal design (UD) relates to many aspects of public transport (PT), comprising the vehicles/wagons, stops/stations, and access/egress to/from stops. While some UD elements are decisive for relatively few, others enhance the overall PT quality and subsequently improve the well-being of many users. If the service quality is improved, some users will be willing to pay more, either in terms of ticket price or travel time. Improved UD elements in PT therefore increase social benefits for public transport users, in economic terms, even if ticket costs are maintained unchanged. PT users in Norway have been asked to choose between journey alternatives with varying levels of UD elements between the alternatives. These UD elements comprised: a) on-board facilities and driver quality; b) stop/station qualities: shelter, seating, safety, lighting, information; and c) the qualities of the area surrounding stops/stations: road surface quality / icing, lighting, facilities/amenities, information. In total, 25 UD qualities were assessed, in addition to travel time. Fare was one of the varying elements in the journey alternatives, enabling estimation of willingness to pay (WTP) for UD quality improvements. The WTP yields benefit estimates of improvements that can be assessed against the costs of upgrading, maintaining, and running these UD-related elements in PT. More than 2000 respondents participated in our web-based survey. Almost one in five reported special needs when travelling by PT. Comparing those reporting special needs against those not reporting needs/difficulties, we find that their WTP is similar for most UD qualities, but WTP differs on some particular UD elements. Those reporting needs/difficulties show higher WTP for improved cleaning and air quality on board, and for benches and highly visible real-time departure info at stops/stations. All PT users have high WTP for improved access, without detours, to stops/stations, that have shelter and de-iced road surface. Overall, our findings provide a case for increased focus on UD in economic assessments of PT.

Optimizing Perceived Jogging Supportiveness for Enhanced Sustainable Greenway Design Based on Computer Vision: Implications of the Nonlinear Influence of Perceptual and Physical Characteristics

Active transportation and lifestyles are important components of a sustainable city. Greenways play a crucial role in providing conducive environments for jogging. To investigate the influence of micro-scale characteristics on perceived jogging supportiveness (PJS), 230 video clips of greenways within Fuzhou City were collected as samples. PJS was evaluated using a Likert scale, perceptual characteristics were assessed through a semantic difference scale, and physical characteristics were computed via semantic segmentation. By employing SHAP values and dependence plots within an XGBoost framework, the findings reveal the following: (1) Regarding perceptual characteristics, continuity, culture, and facility affordance exhibit the highest relative importance to PJS (|SHAP| ≥ 0.1). Continuity, naturalness, and vitality generally have positive impacts on PJS, while disturbance is negative. Facility affordance, scale, culture, openness, and brightness demonstrate more complex nonlinear influences that suggest optimal value ranges. (2) Concerning physical characteristics, fences, motor vehicles, and surface material are deemed most influential (|SHAP| ≥ 0.1). The presence of fences, walls, and construction generally negatively affect PJS, while excessive openness is also unfavorable. Comfortable road surfaces are associated with higher levels of PJS. Natural elements and the presence of people and vehicles have promoting effects up to certain thresholds, but beyond that point, they exert opposite influences. Finally, suggestions for designing greenways that encourage jogging are proposed. This study provides practical references for optimizing greenway design to promote active transportation and lifestyles, reinforcing the contribution of green infrastructure to public health in sustainable cities.

A lightweight convolutional neural network for road surface classification under shadow interference

The development of intelligent driving, especially in the intelligent control of active suspension, heavily relies on the predictive perception of upcoming road conditions. To achieve accurate real-time road surface classification and overcome shadow interference, a lightweight convolutional neural network (CNN) based on a novel data augmentation method is proposed and an improved cycle-consistent adversarial network (CycleGAN) is developed to generate shadowed pavement data. The CycleGAN network structure is optimized using the texture self-supervised (TSS) mechanism and the learned perceptual image patch similarity (LPIPS) function, with label smoothing applied during training. The images produced by this data augmentation method closely resemble real-world images. Furthermore, Efficient-MBConv, which offers the advantages of fewer parameters and higher precision, is proposed. Finally, the Light-EfficientNet architecture, based on Efficient-MBConv, is developed and trained on the augmented dataset. Compared with EfficientNet-B0, the number of parameters in Light-EfficientNet is reduced by 61.94 %. The Light-EfficientNet model trained with data augmentation demonstrates an average classification accuracy improvement of 5.76 % on the test set with shadows, compared with the model trained without data augmentation. This approach effectively reduces the impact of shadows on road classification at a lower cost, while also significantly reducing the computational resources required by the CNN, providing real-time and accurate road surface information for the control of active suspension height and damping.

A Novel Multi-Scale Feature Enhancement U-Shaped Network for Pixel-Level Road Crack Segmentation

Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement crack detection. This network model uses a Residual Detail-Enhanced Block (RDEB) instead of a conventional convolution in the encoder–decoder process. The block combines Efficient Multi-Scale Attention to enhance its feature extraction performance. The Multi-Scale Gating Feature Fusion (MGFF) is incorporated into the skip connections, enhancing the fusion of multi-scale features to capture finer crack details while maintaining rich semantic information. Furthermore, we created a pavement crack image dataset named China_MCrack, consisting of 1500 images collected from road surfaces using smartphone-mounted motorbikes. The proposed network was trained and tested on the China_MCrack, DeepCrack, and Crack-Forest datasets, with additional generalization experiments on the BochumCrackDataset. The results were compared with those of the U-Net model, ResUNet, and Attention U-Net. The experimental results show that the proposed MFE-UNet model achieves accuracies of 82.95%, 91.71%, and 69.02% on three datasets, namely, China_MCrack, DeepCrack, and Crack-Forest datasets, respectively, and the F1_score is improved by 1–4% compared with other networks. Experimental results demonstrate that the proposed method is effective in detecting cracks at the pixel level.

Numerical Analysis of the Vehicle Damping Performance of a Magnetorheological Damper with an Additional Flow Energy Path

Vehicles experience various frequency excitations from road surfaces. Recent research has focused on active dampers that adapt their damping forces according to these conditions. However, traditional magnetorheological (MR) dampers face a “block-up phenomenon” that limits their effectiveness. To address this, additional flow-type MR dampers have been proposed, although revised designs are required to accommodate changes in damping force characteristics. This study investigates the damping performance of MR dampers with an additional flow path to enhance the vehicle ride quality. An optimization model was developed based on fluid dynamics equations and analyzed using electromagnetic simulations in ANSYS Maxwell software. Vibration analysis was conducted using AMESim by applying a sinusoidal road surface model with various frequencies. Results show that the optimized diameter of the additional flow path obtained from the analysis was 1.1 mm, and it was shown that the total damping force variation at low piston velocities decreased by approximately 56% compared to conventional MR dampers. Additionally, vibration analysis of the MR damper with the optimized additional flow path diameter revealed that at 30 km/h, 37.9% acceleration control was achievable, at 60 km/h, 18.7%, and at 90 km/h, 7.73%. This demonstrated the resolution of the block-up phenomenon through the additional flow path and confirmed that the vehicle with the applied damper could control a wider range of vehicle upper displacement, velocity, and acceleration compared to conventional vehicles.

Method of Forming Road Surface Replicas Using 3D Printing Technology

Method of Forming Road Surface Replicas Using 3D Printing Technology

Single Level Fast Multipole Method for frictionless rough contact problems

A perfectly smooth contact surface does not exist in nature and industrial applications. Even a body, that seems perfectly smooth to the naked eye, will show surface roughness at a higher magnification. Due to the roughness of the surface, there are areas of contact and separation, which increases the complexity of the contact calculation. However, this computational complexity increases further due to the multi-scale nature of road surface texture and large contact area in the case of tyre/road interaction. To reduce the computational complexity of this contact problem, the Single Level Fast Multipole Method (SLFMM) is developed within this paper. The contact problem is based on Boussinesq’s contact theory and for the time being, the influence of friction and lateral displacement are neglected. To validate the accuracy and reduction in computational complexity, the SLFMM was applied to rough surfaces of different complexities and compared to a reference method, the so-called Matrix Inversion Method (MIM). Results indicate that the new method computes the pressure distribution and displacement accurately, with a global error of less than 1%. The advantage of the new method compared to the MIM is the multipole expansion, which clusters adjacent contact points to a single center point. As a result, the computational complexity of the contact calculation is reduced. Overall, the Single Level FMM computes the results faster than the reference method. These results demonstrate that the Fast Multipole Method meets the requirements of accuracy and accelerated computation for rough contact problems.

Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.

Pavement wear generates microplastics in stormwater runoff

Tire and road wear particles are a major source of microplastics to urban stormwater. They are composed of hetero-aggregates of abraded tire and pavement particles that are difficult to distinguish. While tire wear is a known source of microplastics, little is known on the contribution of pavement wear. This two-year field study with complementary lab testing evaluates the effects of pavement degradation on microplastic generation in stormwater from different pavement types: asphalt, concrete, and recycled rubber pavers. Pavement specimens from each site were collected and underwent degradation testing. We directly demonstrated that pavement wear is a source of microplastics in stormwater separate from tire wear. We showed that the rubber pavement released the most microplastics in lab testing, suggesting that the formulation of such novel recycled-tire pavers must undergo thorough testing before wide application. The asphalt pavement was the most susceptible to rutting and released the most microplastics in the field, including a large proportion of tire wear particles. Both land-use and pavement surface characteristics influenced microplastic generation. These results demonstrate the need to consider microplastic generation during pavement material selection and mitigate the spread of microplastics from pavement wear to nearby environments.

Analysis of the Design of Children’s Playgrounds in Urban Communities under the Concept of Nature Education

The article focuses on the design of children’s playgrounds in urban communities. The study points out that urbanization has led to “nature-deficit disorder” among children and the squeezing of playgrounds. This study elaborates on the origin of natural education thoughts both at home and abroad and analyzes the numerous problems faced by domestic children due to the lack of playgrounds, the “learning-only theory”, and so on. Through research, the author puts forward renovation ideas from various aspects such as preserving natural landforms, planning the site, designing traffic flow lines, selecting pavement materials, and configuring plant landscapes, aiming to create an exclusive space that conforms to natural education and helps children grow up healthily.

Examining Open-Top Culverts Impact on Forest Road Surface Deteriorations via UAVs

The life and robustness of forest roads depend on their protection from the harmful effects of water coming into the road surface. In particular, the deterioration of the road surface affects the safe navigation of vehicles and traffic safety. This situation requires that the surface be stable on forest roads. The aim of the study is to examine whether surface deterioration (erosion and accumulation) on forest roads due to the drainage problem of water falling on the road surface can be minimized by open-top culverts and to determine their effectiveness. These are used in three separate trial blocks every 25 m (A parcels; total of 3 parcels), every 50 m (B parcels; total of 3 parcels) and control block (C). Volumetric erosion and accumulation in these blocks was compared by UAV for about 3 years and the effectiveness of the open-top culverts was examined by this method. A 500 m section of the forest road coded 001 of the Kardüz Forest Operations Directorate (Düzce/Türkiye) was examined in the study. As a result, erosion and accumulation in all blocks have been found to have a dynamic course. It was determined that this mobility was greater in the control block than in the blocks with open-top culverts installed at intervals of 25 m and 50 m. The mean Z values for the blocks showed that the deterioration in the control block (C) was higher than in the blocks with 25 m and 50 m open culverts. The volumetric deterioration rate was 5 times higher in the control block than in the block installed at 25 m interval (A plots) and 2 times higher than in the block installed at 50 m interval (B plots). Similarly, the areal deterioration rate was 3.3 times higher in the control block than in the block installed at 25 m interval (A plots) and 1.4 times higher than in the block installed at 50 m interval (B plots). These results showed the effectiveness of open-top culverts and it was also determined that the open-top culverts installed at 25 m intervals were more effective than the open-top culverts established at 50 m intervals. In addition, according to the statistical analysis, a statistically significant difference was found between the erosion volume in the blocks. Open-top culverts should be used against forest road surface deterioration and UAV technology should be used for deterioration detection.

Modeling the Time-Dependent Variation of Road Salt Concentrations Using Analytical and Machine-Learning Approaches to Advance Service Life Predictions for Concrete Structures

The exposure of concrete structures to environmental and climatic conditions is detrimental to their durability. In northern climates, the key contributor to their degradation is corrosion of the reinforcing steel because of chloride ions originating from de-icing salts applied on roadways during the winter season. In consequence, a key input parameter for predicting the time to the initiation of corrosion for concrete elements is the time history of the concentration of chloride ions at their surfaces. To investigate this issue, a specialized mobile monitoring station was deployed along a roadway over several winter seasons to collect data on salting operations, weather conditions, and the temporal variation of chloride ion levels on the roadway. At first, salting operations were monitored, and then exploratory and machine-learning algorithms were applied to develop relationships between weather conditions, road conditions, and chloride ion concentrations. The first proposed model is based on the simulation modeling approach, while the second is based on the machine-learning XGBoost model. The findings demonstrate that both models can predict the variation of salt concentration on the road surface as a function of time after a salting operation. By accounting for the time dependency of surface chloride in service life models, more accurate predictions of corrosion initiation time are possible, since the rate of penetration of chloride ions is highly dependent on wetting/drying cycles throughout the winter.

Life Cycle Cost Analysis and Deterioration Patterns of Limestone Paving

Stone is a durable and high-performance paving material in standard and in intensive service regimes. Stone is thus a preferable material for sidewalk and promenade paving under intensive service regimes, such as touristic promenades and historic sites. Recent studies on the weathering and degradation of stones in buildings have revealed differing analytical approaches among geologists, geo-engineers, and civil engineers. The present research aims to develop a structured analytical–empirical methodology for the assessment of stone pedestrian pavements’ life cycle and life cycle costs. This study presents an integrated methodology that combines diagnostic field surveys, core laboratory tests, and the characterization of deterioration patterns. This approach allows for evaluating how faulty construction methods impact the durability and degradation of natural stone pedestrian pavements. It also assesses their effect on the pavement’s life cycle and associated costs. The diagnostic field survey concentrates on specific construction details, including: (a) Cracks in the paving stones. (b) Peeling of stone layers. (c) Subsidence and cracking at the paving edges. (d) Cracking of filler materials in joints between stone slabs. The laboratory tests focus on five core physical properties for the stone deterioration: (1) apparent density, (2) Water absorption, (3) Compressive strength, (4) Flexural strength, and (5) Abrasion resistance. This study proposes linear and exponential patterns for deterioration. A case study carried out on a Capernaum promenade revealed excessive deterioration patterns caused by the poor core properties of the paving stone and defective construction. The consequences of excessive deterioration on life cycle costs result in additional expenses of 73%, indicating a reduction in the life cycle. The novelty of this research lies in developing and delivering an integrated methodology that enables the assessment of how defective construction methods impact the durability, deterioration, life cycle, and life cycle costs of natural stone pedestrian pavements.

Assessment of the air pollutants on automobiles emission dispersion: the effects of barrier height and the road surface temperature.

In the paper, a mathematical model was constructed that describes the specifications of the wind flow and the dispersion of pollutants, taking into account the variable temperature on the roadway surface, which varies depending on the time for some quarter of the city of Almaty. The impact of the traffic tidal flow was studied based on the data of measuring passing vehicles as a source of pollution by the CFD and on the spatial distribution of pollutants for various types of pollution. A test problem was performed to validate the numerical algorithm and the mathematical model. From the obtained numerical solutions, it was determined that the existing barriers along the road have a positive effect on pedestrian zones regardless of the type of pollution. It was also found that, taking into account the variable temperature on the carriageway, the presence of barriers with a height of 4 m shows favorable behavior on the adjacent areas, in which the average concentration value drops by 6.4 times for the pedestrian zone, which is located on the left side and 2.9 times for the pedestrian zone, which is on the right side compared to the option without a barrier.

Prediction-based controller radial neural network for the traction control system

One of the systems needed to improve vehicle safety is the traction control system (TCS). This control mechanism keeps the wheels from slipping too much when the car accelerates, especially when it moves quickly. Because of the significant nonlinear behavior of the tire during acceleration and the unknown impacts of the road surface, it might be difficult to maintain wheel slip in an acceptable range, especially in bad weather. However, some uncertainties, such as unmodeled dynamics and vehicle parameter uncertainty, should be taken into account when designing the controller. Consequently, TCS requires the existence of a strong nonlinear control law. In this study, an analytical design for a TCS controller is made using the method of nonlinear predictive control. The control system’s resistance is then increased by employing an adaptive radial basis neural network (RFNN) to predict the system’s unknown uncertainties. In this study, the controller was designed using half car and quarter car models, respectively. The behavior of the suggested control system for tracking the reference wheel’s slip in the face of uncertainty for various movements is examined in the simulation results that follow. The resulting results have been compared with the simulation results generated from the nonlinear sliding mode controller response used in valid articles in order to provide a more thorough examination of the suggested control system. The findings demonstrate the effective performance of the suggested control mechanism against nonlinear effects and uncertainties.

Intelligent road surface state recognition method based on multi-layer attention residual network

Abstract Data-driven road surface state recognition enhances the efficiency and accuracy of road management, contributing to increased safety and reliability in road traffic. However, traditional machine learning and deep learning-based road surface state recognition typically rely on extensive data for model training, making it challenging to adapt to complex tasks in diverse scenarios. Therefore, this paper proposes a Multi-layer Attention Residual Network (MARN)-based intelligent road surface state recognition method. First, a Residual Convolutional Neural Network (ResNet) is constructed as the backbone model of MARN to mitigate the gradient vanishing problem, allowing the network to extract deeper features. Subsequently, an adaptive multi-layer attention mechanism is introduced in each convolutional layer, enabling adaptive weighting of each feature channel in the dataset to enhance the model’s focus on different features for better feature extraction. Furthermore, a cosine annealing learning rate adjuster is designed to improve the accuracy, robustness, and convergence during the model training process. Finally, the proposed MARN is validated using an image dataset containing six different road surface states. Comparative studies are conducted on the recognition accuracy of the proposed MARN, original ResNet, Visual Geometry Group network (VGG16), and Convolutional Neural Network (CNN). The impact of different batch sizes on the convergence speed of road surface state recognition under MARN is also analyzed. Results demonstrate that MARN achieves a training set accuracy of over 95%, surpassing VGG16 and CNN with accuracies below 85%. Compared to ResNet, MARN exhibits a 1.3% higher training set accuracy and a 0.25 lower validation set loss, showcasing superior accuracy and robustness in road surface state recognition.

Predicting resilient modulus for pavement design: a comprehensive review of artificial neural network applications

ABSTRACT The Resilient Modulus (M R) is an essential parameter describing the mechanical behaviour of pavement materials, but the tests to obtain it are time-consuming and costly. Therefore, machine learning, especially Artificial Neural Networks (ANN), has recently been an effective alternative for predicting M R. Although there has been an increase in articles on ANN for predicting M R, no systematic review has yet categorised the publications, algorithm structures, predictive parameters, and model accuracy. This review provides a comprehensive overview of studies using ANN to predict M R for pavement materials. It examines various ANN subtypes and model structures, addressing a gap in understanding these models and identifying prevalent predictive parameters. Twenty-five peer-reviewed articles published in English-language journals were identified using keywords related to neural networks and M R. For fine soils, all models reviewed use moisture content as a predictive parameter. In contrast, granular soils models typically include stress state and physical characteristics of the materials. For recycled or stabilised materials, there is significant variability in model inputs. This review underscores the potential of ANN in enhancing predictive models for M R in pavement engineering, pointing towards future research and application refinement in this area of study.