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The temperature profile significantly influences the structural performance of asphalt pavement since it influences various mechanical parameters such as stiffness, strength, and fatigue life. The present study aims to achieve two main objectives. Firstly, to explore the potential of Machine Learning (ML) approaches in predicting asphalt pavement profile temperature in the western Europe climate. Secondly, to determine the impact of different weather parameters on the effectiveness of the prediction models. Therefore, three ML algorithms are used to develop asphalt temperature prediction models: autoencoder network, Feedforward Neural Network (FFNN), and Long Short-Term Memory (LSTM).The performance of different ML algorithms is assessed by comparing the accuracy of the prediction models. The analysis results indicated that the autoencoder network is the most accurate algorithm in predicting asphalt pavement temperatures at various depths. Regarding the weather parameters' input dimension, a direct relationship was observed between the number of input dimensions and the performance of the prediction models, where the model using all weather data performs best. The accuracy of the developed ML and a validated FE model were compared with the collected experimental data in terms of Mean Absolute Error (MAE). The average MAEs were calculated between 0.25 and 0.31 °C and 1.05–1.19 °C for ML and FEM approaches, respectively. The results showed the ability of ML algorithms to predict the temperature of asphalt pavement with a higher degree of accuracy compared to numerical models with no extra information about material properties or boundary conditions.

Reclaimed asphalt pavement (RAP) is a recyclable aggregate produced during the demolition of old flexible pavements and consists of natural aggregates (NA) coated with aged bitumen. The detrimental effect caused by the bitumen coating on strength and porosity has limited the use of RAP on traditional cementitious systems. This study investigates the potential use of fine RAP to substitute NA in the production of alkali-activated slag mortars (AAM). The effect of different activator dosages was assessed, i.e., either 4% or 6% Na2O (wt. slag) combined with a modulus of silica equal to 0, 0.5 and 1.0. The characterisation of 100% RAP-AAM consisted of hydration kinetics (Isothermal Calorimetry), pore size distribution (Mercury Intrusion Porosimetry), mechanical performance (Compressive and Flexural strength), and microstructure analysis (Scanning Electron Microscopy and Confocal Laser Scanning Microscopy). The results show that RAP aggregates do not compromise the reaction of the matrices; however, it causes a significant strength loss (compressive strength of RAP-mortars 54% lower than reference NA-mortar at 28 days). The higher porosity at the interface transition zone of RAP-AAM is the main responsible for the lower strength performance. Increasing silicate dosages improves alkaline activation, but it has little impact on the adhesion between aggregate and bitumen. Despite the poorer mechanical performance, 100% RAP-AAM still yields enough strength to promote this recycled material in engineering applications.

The longevity of asphalt pavements is a key focus of road engineering, which closely relates to the self-healing ability of bitumen. Our work aims to establish a CGMD model and matched force field for bitumen and break through the limitations of the research scale to further explore the microscopic mechanism of bitumen self-healing. In this study, a CGMD mapping scheme containing 16 kinds of beads is proposed, and the non-bond potential energy function and bond potential energy function are calculated based on all-atom simulation to construct and validate a coarse-grained model for bitumen. On this basis, a micro-crack model with a width of 36.6nm is simulated, and the variation laws of potential energy, density, diffusion coefficient, relative concentration and temperature in the process of bitumen self-healing are analyzed with the cracking rate parameter proposed to characterize the degree of bitumen crack healing. The results show that the computational size of the coarse-grained simulation is much larger than that of the all-atom, which can explain the self-healing mechanism at the molecular level. In the self-healing process, non-bonded interactions dominate the molecular movement, and differences in the decreased rate of diffusion among the components indicate that saturates and aromatics play a major role in self-healing. Meanwhile, the variations in crack rates reveal that healing time is inversely proportional to temperature. The impact of increasing temperature on reducing healing time is most obvious when the temperature approaches the glass transition temperature (300 K).

Pavement design is essentially and usually a structural long-term evaluation process which is needed to ensure that traffic loads are efficiently distributed at all levels of the total road structure. Furthermore, to get a complete analysis of its durability behavior, long-term monitoring should be facilitated, not only from the top by falling weight deflectometer (FWD) or core drilling but preferably from inside the structure and at exactly the same positions during a long-time interval. Considering that it is very hard to devise an efficient method to determine realistic in-situ mechanical properties of pavements, the determination of strain at the bottom of asphalt pavement layers through non-destructive tests is of a great interest. As it is known, fiber Bragg grating (FBG) sensors are the most promising candidates to effectively replace conventional strain gauges for a long-term monitoring application in a harsh environment. The main goals of this paper are to compile an overview of the recent developments worldwide in the application of fiber optics sensors (FOS) in asphalt pavement monitoring systems; to find out if those systems provide repeatable and suitable results for a long-term monitoring; if there are certain solutions to validate an inverse modelling approach based on the results of FWD and FOS.

The fatigue performance and healing ability of asphalt pavements are important for mixture design, rehabilitation, and maintenance of the roads. By analyzing these parameters in an asphalt mixture, it is possible to predict the crack formation and propagation in asphalt pavements and healing of these cracks during the rest periods. The healing effect in asphalt mixtures has been observed and verified by many researchers and different methods exist to evaluate this phenomenon. However, current methods are still inadequate to fully observe and quantify the healing phenomenon in asphalt mixtures. In this study, a digital image correlation (DIC) method is used to calculate the strain map on the surface of cylindrical asphalt specimens during the indirect tensile fatigue test. This strain map is used to detect the location of crack initiations and development of the microcracks during the experiments. Next, the specimens are unloaded and the temperature of the climate chamber is increased to 60 °C for 18 h to investigate the healing phenomenon on the surface of the specimens. It is observed that the strain reduces near the microcrack areas especially at the tip of the microcracks. Furthermore, using DIC it is possible to observe the healing rate and the minimum time required to heal the microcracks on the surface of the specimens.

In this study, the feasibility of a natural peat fiber and finely ground peat powder as a modifier for bitumen was investigated. Initially, the as-received peat material was characterized in detail: the material was ground to various degrees, separated into fiber and powder fractions, and the gradation of the powder fraction as well as the size of the fibers were determined. A possible solubility in bitumen, the moisture content, and the density of both fractions were evaluated, and a limited chemical characterization of the fibers was conducted. Secondly, the rheological behavior of the powder and the fibers when blended with bitumen was evaluated. Additionally, a limited asphalt study was conducted. The rheological data showed the stiffening effects of the powder fraction and the presence of a fiber network, which were obvious as a plateau modulus towards lower frequencies. The fiber network was strain-dependent and showed elastic effects. This was further confirmed by the multiple stress creep recovery (MSCRT) tests. These tests also indicated that the fibers should improve the rutting resistance, although it was not possible to confirm this in asphalt rutting tests. Asphalt drainage tests demonstrated that adding dry peat, whether this is ground or not, is effective in reducing the binder drainage. However, the data also revealed that the amount of added peat fibers and powder should be limited to avoid difficulties in the compaction of these asphalt mixes.

A laser Doppler vibrometer (LDV) is a noncontact optical measurement device to measure the vibration velocities of particular points on the surface of an object. Even though LDV has become more popular in road engineering in recent years, their signal-to-noise ratio (SNR) is strongly dependent on light scattering properties of the surface which, in some cases, needs to be properly conditioned. SNR is the main limitation in LDV instrumentation when measuring on low diffusive surfaces like pavements; therefore, an investigation on the SNR of different LDV devices on different surface conditions is of great importance. The objective of this research is to investigate the quality of two types of commercially available LDV systems—helium⁻neon (He⁻Ne)-based vibrometers and recently developed infrared vibrometers—on different surface conditions, i.e., retroreflective tape, white tape, black tape, and asphalt concrete. Both noise floor and modal analysis experiments are carried out on these surface conditions. It is shown that the noise floor of the He⁻Ne LDV is higher when dealing with a noncooperative dark surface, such as asphalt concrete, and it can be improved by improving the surface quality or by using an infrared LDV, which consequently improves the modal analysis experiments performed on pavement materials.

In the present study, a new approach to the installation of fiber Bragg grating (FBG) sensors in three asphalt pavement layers (the surface layer and both base layers) was implemented for the first time in Belgium. Fiber Bragg grating sensors (FBGs) are diagnostic tools that accurately and efficiently monitor in situ structural behavior. However, nowadays, this technology is not commonly used in asphalt due to its application restrictions under installation and service conditions. FBGs are fragile and break easily under loading. Therefore, there is a need for suitable protection of FBG sensors if they are to be installed during the rough construction process and exposed to heavy-duty loading afterwards. The main objective of the present study is to show the FBG results only for the initial construction process, and, if successful, to continue studying this FBG monitoring system and to plan the next research step by adjusting the system for its application in heavy-duty pavements. Two approaches to FBG installation in three asphalt layers (placed at the bottom of each layer) were tested in the present study: (1) installation of FBGs in prefabricated asphalt specimens in the base layer, directly on the base, and (2) installation of FBGs on the surface of the previously constructed asphalt layer. Both innovative approaches allow the implementation of FBGs without sawing the whole layer into two parts. The obtained results proved a survival rate of 100% for the FBGs. It can be concluded that these new described methods of FBG installation—using a cross-section configuration to carry out strain measurements in two directions (transverse and longitudinal)—can be applied for the monitoring of heavy-duty pavements, while providing the possibility to further re-evaluate current pavement design methods used in Flanders (Belgium).