Field Of Pavement Engineering Research Articles

Characterization of Air Voids and Permeability in Porous Asphalt Mixtures with Eggshell Ash as an Eco-Friendly Additive

Recently, there has been a lot of interest in the use of eco-friendly additives in asphalt mixtures in the field of pavement engineering. Such additives have been found to have the potential to enhance pavement performance while also mitigating the environmental impact of road construction. Eggshells are a restaurant waste product that is one of the additives that have been investigated in this study. The research aims to investigate the effect of eggshell ash (EA) on the air voids and permeability of porous asphalt mixtures. Two temperatures, 100°C (EA100) and 200°C (EA200), were used to prepare EA with varying percentages of 2%, 5%, 7%, 10% and 12% by weight of bitumen. The samples were then prepared and compacted by applying 50 blows on each side to measure air voids, and permeability testing was performed in the laboratories. The results indicated that the use of EA had impacted the permeability and air voids of the porous asphalt mixture. The amount of air voids in the mixes had a direct impact on the permeability coefficient, as the value of the permeability coefficient decreased with decreasing air voids. EA can serve as an additive to improve the permeability of asphalt mixtures, which may increase the pavement's durability and longevity. Furthermore, the use of EA has the potential to reduce the environmental impact of the egg industry while improving the sustainability of road construction

Research on flexural mechanical properties and mechanism of green ecological coir fiber foamed concrete (CFFC)

To explore the effect and mechanism of coir fiber on the performance of foamed concrete, the flexural performance test, pore characteristics and microstructure test of coir fiber foamed concrete with different content were carried out. First, Image-Pro Plus (image processing software) was used to study the pore morphology, porosity, average pore diameter, and pore roundness of CFFC with various fibers dosage (0, 0.5%, 1.0%, 1.5%, 2.0%, 2.5%) by binarization processing method. Then, a total of eighteen specimens, divided into six groups, were used to investigate the effect of CF dosage on flexural strength, toughness, energy absorption, and failure patterns of FC through a three-point flexural test. Furthermore, the microscopic properties of coir fiber foamed concrete (CFFC) were observed by scanning electron microscope (SEM) and energy dispersive X-ray detector (XRD) to explain the influence mechanism of CF on FC flexural properties. According to the research, CF can affect the pore characteristics of CFFC and improve its flexural performance. When CF content is 1.5–2.0%, the porosity, diameter and roundness of CFFC have lower values of 68.6%, 1.96 mm and 1.29. After the fiber dosage reaches 1.5%, the CFFC failure mode changed to plastic damage, the flexural strength increased from 0.33 to 0.73 MPa, and the toughness energy absorption value was increased from 0.05 to 1.4 J. The optimum dosage of coir fiber is 2.0% for improving the flexural mechanical properties of FC. CF affects the process of hydration reaction of CFFC, but does not change the type of hydration product. However, the flexural performance of FC would decrease with excessive dosage of CF (> 2.0%) due to accelerating the formation of Ca(OH)2. CFFC can solve problems such as brittleness and easy cracking existing in traditional foamed concrete, and it can be used in the field of pavement engineering, foundation backfill and lightweight wall structure with CF dosage of 15–2.0%.

Influence of preparation parameters on rheological properties and relation analysis of waste rubber modified bitumen mastic

Waste rubber modified bitumen has gained significant attention as a sustainable and innovative material in the field of pavement engineering. This study aims to evaluate the performance of rubber modified bitumen mastic by considering its rheological properties, specifically focusing on preparation parameters, i.e., rubber content, mesh number, and filler to bitumen ratio. From the experimental results, the rheological properties of rubber modified bitumen mastic were significantly influenced by preparation parameters. Increasing the rubber powder content in bitumen mastic results in higher viscosity. Increasing the rubber content improves high-temperature rutting resistance to a certain extent, however, excessive rubber powder content would result in weakened high-temperature performance improvement. The rutting factor decreases gradually with an increase in the rubber mesh number. A ratio of filler to bitumen of 0.95 exhibits the best resistance to rutting at high temperatures. Higher rubber content and larger mesh number correspond to stronger low-temperature crack resistance in bitumen mastic. As the ratio of filler to bitumen increases, the low-temperature deformation capacity gradually decreases, resulting in weaker low-temperature crack resistance. Based on the grey relation analysis, the ratio of filler to bitumen has the greatest impact on the high and low-temperature rheological properties of bitumen mastic, followed by the rubber content. The rubber mesh number has a relatively lower impact. It is crucial to control the ratio of filler to bitumen to avoid excessive values. When possible, a higher rubber powder content should be used while meeting process requirements. These findings provide valuable insights into the design and optimization of rubber modified bitumen mastic, which can contribute to the development of sustainable and high-performance bitumen mixtures, promoting the use of recycled rubber in pavement engineering.

Review of Research on Tire–Pavement Contact Behavior

This article presents the latest progress in research on tire–pavement contact behavior. Firstly, the tire–pavement contact characteristics and their influencing factors are summarized. Then, the measurement methods and theoretical research on tire–pavement contact behavior are reviewed, and the advantages and shortcomings of different methods are compared and analyzed. Finally, analysis in the field of pavement engineering is summarized based on contact behavior. This article suggests a few key research directions: Tire–pavement contact behavior is influenced by multiple factors; therefore, multi-physical field-coupling analyses need to be carried out. Tire–pavement contact tests are mostly static and non-standardized, and it is a future trend to develop high-precision, low-cost, and standardized instruments that can measure dynamic contact. Theoretical research models rarely involve environmental factors; a contact model of the tire, pavement, and environment needs to be constructed that can truly describe the contact process. There is a relationship between contact characteristics and pavement performance; pavement performance evaluation indexes need to be established based on tire–pavement contact characteristics in the future.

Assessing recycled asphalt pavement: Impact of waste cooking oil and waste engine oil as rejuvenators on mechanical properties

Sustainable pavement construction is an extensively researched subject within the field of pavement engineering. Due to its environmental and commercial advantages, the construction of pavements utilizing recycled asphalt pavement (RAP) is progressively gaining popularity worldwide. Owing to the aging process, binder extracted from RAP is inherently harder than virgin binder and requires rejuvenation for application in high RAP blends. This paper investigates the performance characteristics of Waste Cooking Oil (WCO) and Waste Engine Oil (WEO) as rejuvenators in RAP-incorporated asphalt mixtures to assess their suitability for pavement use. The optimal rejuvenator content is determined based on the performance of physical properties. The study indicates that asphalt mixes rejuvenated with Waste Cooking Oil (WCO) demonstrate higher Marshall Stability values compared to mixes rejuvenated with Waste Engine Oil (WEO) at both 30% and 60% Recycled Asphalt Pavement (RAP) levels. Additionally, mixes incorporating WCO exhibit increased flow values. Furthermore, WCO outperforms WEO in Tensile Strength Ratio (TSR) at 30% RAP, with a slight decrease at 60% RAP. Results from Marshall and Indirect Tensile Strength (ITS) tests suggest that both WEO and WCO are effective rejuvenators, playing a critical role in aligning the properties of RAP-inclusive asphalt mixes with those of virgin mixes.

Effect of Filler to Bitumen Weight Ratio on Low-temperature Cracking Performance of Asphalt Mixture

The effect of mineral filler weight ratio on the low-temperature performance of hot asphalt mixtures with continuous gradation has been a subject of significant interest in the field of pavement engineering. This study focused on testing mastic, which is a bitumen mixture with mineral filler passing through a No. 200 sieve, using various tests, such as the bending beam rheometer test (BBR), direct tensile (DT) test, Infrared spectroscopy test (FTIR), X-ray diffraction (XRD) test, and semi-circular bending (SCB) test. Different weight ratios of mineral filler to useful bitumen ranging from 0.4 to 1.6 were investigated for PG 70-16 bitumen. According to the SHRP-A-407 standard, the weight ratio of mineral filler to useful bitumen plays a crucial role in determining the low-temperature performance of asphalt mixtures. The research findings confirm this fact, indicating that the weight ratio of mineral filler to bitumen plays a crucial role in determining the low-temperature performance of asphalt mixtures. As the filler-to-bitumen ratio increased, the stiffness of the bituminous mastic increased, leading to reduced fracture strain and decreased low-temperature cracking life. The optimal filler-to-bitumen ratio was recommended to be a maximum of 0.8 to balance performance and control low-temperature behavior. The results of this study underscore the importance of considering the rheological properties of bitumen mastic in addition to bitumen and asphalt mix when evaluating lowtemperature performance. Keywords: Asphalt mixing, Filler-to-bitumen ratio, Cracking performance, Lowtemperature, cracking.

Artificial neural network approach to predict asphalt mixtures’ stiffness modulus based on testing frequency and temperature

To successfully guarantee the proper durability and serviceability of asphalt pavements, it is crucial to investigate asphalt mixtures’ performance and to design an accurate model to predict their mechanical behaviour. In Machine Learning, Artificial Neural Networks (ANNs) consist of a set of layered and interconnected artificial neurons capable of learning a complex function that maps the input to the target output. This study is specifically aimed at their implementation within the field of pavement engineering. The paper thoroughly discusses the development of an ANN-based methodology capable of predicting the stiffness modulus of an asphalt mixture (AM). The AM under investigation was prepared with spilite aggregate, a 50/70 penetration grade bitumen, and limestone filler. The volumetric properties of each specimen were first determined, and then the sequence of a 4-Point Bending Test was carried out under different conditions. Four testing temperatures (0, 10, 20, 30 °C) and eleven loading frequencies (0.1, 1, 2, 3, 5, 10, 15, 20, 30 and 50 Hz) were selected to investigate the asphalt mixture's mechanical behaviour. The resulting stiffness moduli represented the output of the designed neural model. Prediction accuracy was evaluated utilizing several goodness-of-fit metrics, and the results of this feasibility study proved to be very encouraging. They are certainly limited to the asphalt mixture under investigation. However, the high level of accuracy suggests that trained on a larger dataset, the developed methodology could allow the AMs’ mechanical behaviour to be predicted without the need to carry out the conventional tests that are usually expensive and time-consuming.

ADVANCING INFRASTRUCTURE IN DEVELOPING NATIONS: A SYNTHESIS OF AI INTEGRATION STRATEGIES FOR SMART PAVEMENT ENGINEERING

This research focuses on the transformational potential of artificial intelligence (AI) towards the field of pavement engineering, particularly considering underdeveloped nations. This exploratory study aims to shed light on the complex effects AI has on pavement engineering and management processes, as well as various strategic, policy, and ethical perspectives underpinning its implementation. By using a systematic process, the study builds on information from academic papers, case studies, and industry research to create more enriching knowledge on this topic. Through a robust study, it is shown that AI helps improve the composition of pavement materials building and maintenance routines. Such a study shows that AI can improve the level of economic benefits in the pavement infrastructure of underdeveloped countries. These key findings emphasize how AI reduces the costs of design, predictive maintenance models, and sustainable materials use. In addition, the study traverses through the difficult routes of policy and institutional frameworks, stating that the policies ought to be responsive while collaboration is required in promoting AI uptake. The discussion integrates ethics and the environment very carefully, as required by sustainability and equity. Lastly, the research presents an excellent argument for AI as an agent for constructing new and eco-friendly transportation infrastructure in poor countries. These countries’ recommended policies should be flexible, as they promote capacity building and ethics in AI with regard to infrastructure development as part of their developmental paths, which are simultaneously technical and social.
 Keywords: Artificial Intelligence, Pavement Engineering, Developing Nations, Sustainable Infrastructure, Ethical AI, Transportation

Improving the Gradation of Aggregate used in Asphalt Mixtures Using Bailey Method

The bailey method of gradation analysis it is one of the techniques used in pavement engineering for evaluating and designing asphalt mixtures based on the particle size distribution. This paper presents a comprehensive review of research conducted on the Bailey technique, focusing on its application and impact in the field of pavement engineering. The review encompasses studies that have investigated the effectiveness of the Bailey technique as a mix design tool, a quality control tool, and a performance assessment tool. Researchers have explored its ability to optimize asphalt mixtures for desired performance characteristics, such as rutting resistance, stability, and durability. They have also examined its effectiveness in ensuring consistency and conformity to specified requirements during asphalt production. Furthermore, the review highlights the correlation between the Bailey technique's gradation data and various performance properties of asphalt mixtures. Researchers have investigated the relationship between particle size distribution and properties like rutting resistance, moisture susceptibility, drainage, and particle packing. These studies have provided valuable insights into the influence of gradation on the performance of asphalt pavements.

Quantitative evaluation on the energy saving and emission reduction characteristics of warm mix asphalt mixtures

In response to global environmental protection policies, and the imperatives of achieving carbon peaking and carbon neutrality goals, warm mix asphalt (WMA) technology garnered increasing prominence in the field of pavement engineering. This study conducted a quantitative assessment of the energy-saving and emission-reduction characteristics of WMA mixtures throughout their lifecycle, encompassing the production and construction phases. To achieve this, 70# asphalt, SBS modified asphalt, Alube@, and ACMP@ WMA mixtures were selected, and their optimal mixing temperatures were determined through the rigorous variable speed mixing test. We formulated calculation models for energy consumption and carbon emissions at each stage, and incorporated a self-developed variable temperature emission test to measure the CO2 emissions resulting from asphalt heating volatilization. The findings revealed that the two warm mix additives effectively lowered the mixing temperature of 70# asphalt mixture by 25–26 °C. Comparatively, it was estimated that WMA mixtures exhibited reductions in energy consumption ranging from 16.5 % to 23.0 %, 11.3 % to 36.5 %, and 15.4 % to 47.4 % during the raw material heating, mixing, and compaction processes, respectively, when compared with the two hot mix asphalt (HMA) counterparts. Moreover, the noteworthy reductions in carbon emissions stemming from fuel combustion and asphalt heating volatilization were observed for the WMA mixtures. On-site measurements further corroborated that the WMA mixtures clearly manifested pronounced energy-saving and emission-reduction effects during the production and spreading phases. In conclusion, WMA is an environmentally friendly technology with promising prospects for widespread applications.

Thermodynamics of healing of asphalt binders with free energy

Inspired by self-healing attributes, one of the key topics in the field of pavement engineering is changing the way that asphalt materials behave for prolonged service life and environmental savings. Extensive experimental approaches, models and technologies have been developed to characterize the evolution of healing influenced by internal and external factors. The purpose of this study is to explore the local kinetics of asphalt binders that energetically favor autonomous self-healing as well as its mechanism using free energy method. With consideration of driving forces including internal stress and interfacial forces of attraction (or surface energy) responsible for energy redistribution during the rest period, the healing of an asphalt binder was first modeled as a function of thermodynamic parameters. New healing indexes were then defined by the total free energy change for healing (ΔGH) and fast-rate response ratio (RH) of initial healing in duration to the total rest time. Thirdly, a fatigue-healing test was performed on #70 asphalt binder and SBS-modified asphalt binder to examine the potential and reactivity of healing affected by different damage levels. Finally, the effectiveness of proposed thermodynamic healing indexes in describing healing ability was discussed by considering their relations with properties measured by mechanical analysis. Results show that more negative ΔGH indicates a greater tendency to spontaneous healing while less negative ΔGH suggests a poor healing potential as crack propagates to a certain length. In contrast to #70 asphalt binder, SBS-modified asphalt binder exhibits a superior healing potential on a shorter crack length scale. RH gradually increases with the increase of crack length for both #70 asphalt binder and SBS-modified asphalt binder, implying limited mobility of molecules. By contrast with mechanistic healing indexes assessing properties before and after healing (i.e. the degree of healing), the thermodynamic healing indexes provide a new vision regarding healing potential, which reveals the true ability of a material to autonomously repair damaged areas.

An Appraisal of Mechanistic-Empirical Models (MEMs) in Pavement Deterioration

This research paper presents an appraisal of Mechanistic-Empirical Models (MEMs) in pavement deterioration assessment. The study examines various MEMs used in the analysis of flexible pavements, including Westergaard's Equation, Boussinesq's Equation, NCHRP 1-37A, AASHTO MEPDG, EICM, and FPO Software. The equations, parameters, limitations, and advantages of each model are thoroughly evaluated. The significance of MEMs in pavement deterioration assessment is highlighted, emphasizing their ability to provide a mechanistic understanding of pavement behavior and enable accurate predictions of performance. The paper offers recommendations for practitioners and researchers, including the adoption and implementation of MEMs, collaboration and data sharing, enhanced model inputs, standardized calibration protocols, continuous model improvement, and training and education. The conclusion emphasizes the value of MEMs in pavement engineering while acknowledging the need for further improvement and research. The findings of this study contribute to advancing the appraisal and utilization of MEMs in pavement deterioration assessment, ultimately leading to improved pavement designs, optimized maintenance strategies, and informed decision-making in the field of pavement engineering.

Volumetric Properties and Stiffness Modulus of Asphalt Concrete Mixtures Made with Selected Quarry Fillers: Experimental Investigation and Machine Learning Prediction.

In recent years, the attention of many researchers in the field of pavement engineering has focused on the search for alternative fillers that could replace Portland cement and traditional limestone in the production of asphalt mixtures. In addition, from a Czech perspective, there was the need to determine the quality of asphalt mixtures prepared with selected fillers provided by different local quarries and suppliers. This paper discusses an experimental investigation and a machine learning modeling carried out by a decision tree CatBoost approach, based on experimentally determined volumetric and mechanical properties of fine-grained asphalt concretes prepared with selected quarry fillers used as an alternative to traditional limestone and Portland cement. Air voids content and stiffness modulus at 15 °C were predicted on the basis of seven input variables, including bulk density, a categorical variable distinguishing the aggregates' quarry of origin, and five main filler-oxide contents determined by means of X-ray fluorescence spectrometry. All mixtures were prepared by fixing the filler content at 10% by mass, with a bitumen content of 6% (PG 160/220), and with roughly the same grading curve. Model predictive performance was evaluated in terms of six different evaluation metrics with Pearson correlation and coefficient of determination always higher than 0.96 and 0.92, respectively. Based on the results obtained, this study could represent a forward feasibility study on the mathematical prediction of the asphalt mixtures' mechanical behavior on the basis of its filler mineralogical composition.

Performance of PET and nano-silica modified stone mastic asphalt mixtures

Stone mastic asphalt (SMA) has become widely used in the field of pavement engineering. Using specific additives in SMA is necessary due to climate change and load condition variations. Thus, it is essential to utilise a modern mixture with a high capacity to survive the high traffic loads. The current study aims to use a combination of waste polyethene terephthalate (PET) and Nano-silica (NS) as a newly developed hybrid additive in SMA modification to improve rutting and fatigue performance. Waste PET and NS were blended with the C320 bitumen binder by using the wet-mix process, producing SMA mixtures. The Nano-silica was added to the C320 binder (2%, 4%, 6% and 8% by weight of the binder). The engineering properties was assessed through the Marshall stability, wheel tracking test, indirect tensile strength ratio test, resilient modulus test, and drain-off tests. The results indicated that adding 4–8% of Nano-silica with 6% waste PET plastic improves the rutting performance, stiffness modulus, tensile strength ratio, and fatigue performance of the modified SMA mixtures in comparison to non-modified mixtures. The significant performance of rut depth and stiffness modulus, tensile strength ratio and drain-offs were recorded as 1.2 mm, 6100 MPa, 96.4%, and 0.04%, respectively. Results revealed that mixtures modified with the hybrid additive of 6%PET6%NS have superior performance concerning better stiffness and rutting resistance. The results showed that the increase in Nano-silica content with PET plastic increased the fatigue life to the highest extent, at up to 220,490, for the hybrid additive of 6PET6NS mixtures. Based on the results of rutting and fatigue performance, the hybrid additive of 6PET6NS mixtures reflects the optimum and ideal content. The results demonstrated that the inclusion of NS with PET successfully decreased the drain-off rates, increased the rutting resistance, and improved the fatigue life.

Internet of Things (IoT) and Remote Sensing (RS) Based on Geospatial Information System (GIS) Against Fatigue of Asphalt Bitumen

This work investigated the Geospatial Information System (GIS) as a low-cost, high-precision, and rapid method for identifying the (RAP). This work also showed that (GIS) can be linked to new techniques including Remote Sensing (RS) and the Internet of Things (IoT) which can be serious subjects for research in the field of pavement engineering. The results of the present work showed that Remote Sensing (RS) has reduced fatigue and thermal cracks. Moreover, for modified asphalt samples, tests of Marshall stability, flow, resilience modulus, repeated load axial, uniaxial creep and indirect tensile fatigue were carried out.

Research and applications of artificial neural network in pavement engineering: A state-of-the-art review

Given the great advancements in soft computing and data science, artificial neural network (ANN) has been explored and applied to handle complicated problems in the field of pavement engineering. This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering, including pavement design, construction, inspection and monitoring, and maintenance. This study focused on the papers published over the last three decades, especially the studies conducted since 2013. Through literature retrieval, a total of 683 papers in this field were identified, among which 143 papers were selected for an in-depth review. The ANN architectures used in these studies mainly included multi-layer perceptron neural network (MLPNN), convolutional neural network (CNN) and recurrent neural network (RNN) for processing one-dimensional data, two-dimensional data and time-series data. CNN-based pavement health inspection and monitoring attracted the largest research interest due to its potential to replace human labor. While ANN has been proved to be an effective tool for pavement material design, cost analysis, defect detection and maintenance planning, it is facing huge challenges in terms of data collection, parameter optimization, model transferability and low-cost data annotation. More attention should be paid to bring multidisciplinary techniques into pavement engineering to tackle existing challenges and widen future opportunities. • Frontiers of artificial neural network (ANN) in pavement design, construction, inspection and maintenance were reviewed. • Three mainstream ANN architectures for different application scenarios were summarized. • Five research challenges and prospects of ANN application in pavement engineering were analyzed. • Standardized literature search and classification methods were implemented.

Understanding of asphalt chemistry based on the six-fraction method

Characterization of asphalt chemistry has been a longstanding challenge in the field of pavement engineering. The commonly used SARA approach fails to fully describe the macroscale property of asphalt, and the chemical properties of the individual fractions in asphalt are rarely studied so far. This study contributes to fill such knowledge gaps and propose a molecular model for asphalt binder based on six-fraction separation experiment. The asphalt binder Nynas was separated into six fractions, and the chemical properties of the individual fractions were probed. Based on the chemical analysis, a novel 6-molecule model was proposed for the asphalt binder Nynas. To verify the accuracy of the molecular model, a comparison of density and elastics modulus were conducted, and the results showed that compared with the typical asphalt model, the new model can characterize the density and elastic modulus of the asphalt binder more accurately, which indicates that the new six-fraction model is more suitable for asphalt in this study. The novel six-fraction model proposed in this study provides a new choice for the molecular dynamics study of asphalt binder.

Pavement energy harvesting technologies: a critical review

The world energy consumption is constantly increasing and the research point towards novel energy harvesting technologies. In the field of pavement engineering, the exploitable sources are the solar radiation and the vehicle load. At present, these systems are able to convert the sunlight into electricity thanks to some solar cells placed under a semi-transparent layer (photovoltaic roads), or they can harvest thermal heat by means of solar thermal systems. The thermal gradient of the pavement can be exploited by thermoelectric generators, by heat pipes or by heat-transfer fluids (i.e. water) pumped into a medium (asphalt solar collectors, porous layer or air conduits). The traffic load can be exploited by piezoelectric materials, able to convert the vehicle load into an electrical charge.
 The aim of this paper is to describe the main pavement energy harvesting technologies, pointing out positives and negatives and providing indications for further optimizations. Finally, the systems are compared in terms of initial cost, electrical output, efficiency and technology readiness level.

Application of deep learning-based image recognition technology to asphalt–aggregate mixtures: Methodology

An important factor that affects the performance and durability of asphalt–aggregate mixtures is the size distribution of the aggregate particles. However, there are no accurate and quick methods of obtaining this information currently available. A potential solution to this problem is to use computer-aided image recognition technology, a technique that is being used increasingly more often in the field of pavement engineering. In this paper, asphalt core samples drilled from pavements are used as test objects. The information collected from the side surfaces of these cores is analyzed using neural network technology to help improve the speed and accuracy of the quantitative analysis of the asphalt mixtures. First, a camera is used to make a series of photographic images of the side surface and image reconstruction compositing is adopted to produce an image of the asphalt–aggregate mixtures. This image can be processed using a neural network (U-NET++) to identify the aggregate in the image. The training and recognition effect is improved by using Gaussian blur and secondary labeling methods. The grayscale images can then be binarized, filled, and the aggregate particles are segmented in order to identify holes and areas with adhesion problems. The geometric forms of the particles that appear on the side surfaces of the asphalt mixtures can be accurately obtained. Important geometric parameters (e.g. aspect ratio and roundness) can thus be derived from the image. A method of identifying and analyzing segregation and weakness in the asphalt mixture is proposed. The results show that our composite images are more accurate than traditionally-synthesized panoramic images. Also, the trained U-NET++ is ideal for recognizing and segmenting the aggregate image. The data obtained from the image can thus be used to quickly derive index parameters for the asphalt mixture. Compared with traditional test methods, this method only requires the use of mobile phones and personal computers. It is thus a lightweight and intelligent analysis method for acquiring information about asphalt–aggregate mixtures.

Potential anti-vibration pavements with damping layer: Finite element (FE) modeling, validation, and parametrical studies

Vibrations induced by traffic are of concern for road authorities due to disturbance on the population, and the damage of buildings and structures. In the field of pavement engineering, the anti-vibration paving technologies by the so-called damping layer are under investigation to avoid the generation of excessive vibration and contains propagation. To more fully examine the effectiveness and potential of such a damping layer in the application of anti-vibration pavement, numerical simulations based on a two-dimensional (2D) finite element (FE) model is conducted. The method of determining Rayleigh damping parameters is proposed to more accurately characterize the attenuation of vibration in the road pavements. Sensitivity analysis of varying monitored points and varying loads are performed. Several important parameters such as the damping layer position and thickness, damping ratio are evaluated as well. By the use of this FE simulation to model the vibration response induced by traffic, the costly construction mistakes and field experimentation can be avoided.