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In recent years, due to the reduction in available natural resources, the attention of many researchers has been focused on the reuse of recycled materials and industrial waste in common engineering applications. This paper discusses the feasibility of using seven different materials as alternative fillers instead of ordinary Portland cement (OPC) in road pavement base layers: namely rice husk ash (RHA), brick dust (BD), marble dust (MD), stone dust (SD), fly ash (FA), limestone dust (LD), and silica fume (SF). To exclusively evaluate the effect that selected fillers had on the mechanical performance of asphalt mixtures, we carried out Marshall, indirect tensile strength, moisture susceptibility, and Cantabro abrasion loss tests on specimens in which only the filler type and its percentage varied while keeping constant all the remaining design parameters. Experimental findings showed that all mixtures, except those prepared with 4% RHA or MD, met the requirements of Indian standards with respect to air voids, Marshall stability and quotient. LD and SF mixtures provided slightly better mechanical strength and durability than OPC ones, proving they can be successfully recycled as filler in asphalt mixtures. Furthermore, a Machine Learning methodology based on laboratory results was developed. A decision tree Categorical Boosting approach allowed the main mechanical properties of the investigated mixtures to be predicted on the basis of the main compositional variables, with a mean Pearson correlation and a mean coefficient of determination equal to 0.9724 and 0.9374, respectively.

For the purpose of the Airport Pavement Management System (APMS), in order to optimize the maintenance strategies, it is fundamental monitoring the pavement conditions’ deterioration with time. In this way, the most damaged areas can be detected and intervention can be prioritized. The conventional approach consists in performing non-destructive tests by means of a Heavy Weight Deflectometer (HWD). This equipment allows the measurement of the pavement deflections induced by a defined impact load. This is a quite expensive and time-consuming procedure, therefore, the points to be investigated are usually limited to the center points of a very large mesh grid. Starting from the measured deflections at the impact points, the layers’ stiffness moduli can be backcalculated. This paper outlines a methodology for predicting such stiffness moduli, even at unsampled locations, based on Machine Learning approach, specifically on a feedforward backpropagation Shallow Neural Network (SNN). Such goal is achieved by processing HWD investigation and backcalculation results along with other variables related to the location of the investigation points and the underlying stratigraphy. Bayesian regularization algorithm and k-fold cross-validation procedure were both implemented to train the neural model. To enhance the training, a data analysis technique commonly referred to as data augmentation was used in order to increase the dataset by generating additional data from the existing ones. The results obtained during the model testing phase are characterized by a very satisfactory correlation coefficient, thus suggesting that the proposed Machine Learning approach is highly reliable. Notably, the proposed methodology can be implemented to evaluate the performance of every paved area.

Nowadays, budget restrictions for road construction, management, and maintenance require innovative solutions to guarantee the user acceptable service levels respecting environmental requirements. Such goals can be achieved by the re-use of various waste materials at the end of their service life in the pavement structure, therefore avoiding their disposal in landfill. At the same time, significant savings are achieved on natural aggregate by replacing it with such waste materials, improving the economic and environmental sustainability of road constructions. The purpose of this study is to discuss a laboratory investigation about foamed bitumen-stabilized mixtures for road foundation layers, in which the aggregate structure was entirely made up of industrial by-products and civil wastes, namely metallurgical slags such as electric arc furnace (EAF) and ladle furnace (LF) slags, coal fly (CF) ash, bottom ash from municipal solid waste incineration (MSWI), glass waste (GW) and reclaimed asphalt pavement (RAP). Combining these recycled aggregates in different proportions, six foamed bitumen mixtures were produced and investigated in terms of indirect tensile strength, stiffness modulus, and fatigue resistance. The leaching test carried out on the waste materials considered did not show any toxicological issue and the best foamed bitumen mixture's composition was characterized by 20% of EAF slags, 10% of LF slags, 20% of MSWI ash, 10% of CF ash, 20% of GW, and 20% of RAP. Its mechanical characterization presented a dry indirect tensile strength at 25 degrees C of 0.62 MPa (well above the Italian technical acceptance limits), a stiffness modulus at 25 degrees C equal to 6171 MPa, and a number of cycles to failure at 20 degrees C equal to 6989 for a stress level of 300 kPa.

This paper presents a study about a Machine Learning approach for modeling the stiffness of different high-modulus asphalt concretes (HMAC) prepared in the laboratory with harder paving grades or polymer-modified bitumen which were designed with or without reclaimed asphalt (RA) content. Notably, the mixtures considered in this study are not part of purposeful experimentation in support of modeling, but practical solutions developed in actual mix design processes. Since Machine Learning models require a careful definition of the network hyperparameters, a Bayesian optimization process was used to identify the neural topology, as well as the transfer function, optimal for the type of modeling needed. By employing different performance metrics, it was possible to compare the optimal models obtained by diversifying the type of inputs. Using variables related to the mix composition, namely bitumen content, air voids, maximum and average bulk density, along with a categorical variable that distinguishes the bitumen type and RAP percentages, successful predictions of the Stiffness have been obtained, with a determination coefficient (R2) value equal to 0.9909. Nevertheless, the use of additional input, namely the Marshall stability or quotient, allows the Stiffness prediction to be further improved, with R2 values equal to 0.9938 or 0.9922, respectively. However, the cost and time involved in the Marshall test may not justify such a slight prediction improvement.

In this study a novel procedure is presented for an efficient development of predictive models of road pavement asphalt concretes mechanical characteristics and volumetric properties, using shallow artificial neural networks. The problems of properly assessing the actual generalization feature of a model and avoiding the effects induced by a fixed training-test data split are addressed. Since machine learning models require a careful definition of the network hyperparameters, a Bayesian approach is presented to set the optimal model configuration. The case study covered a set of 92 asphalt concrete specimens for thin wearing layers.

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.

Establishing the structural integrity of an airport pavement is crucial to assess its remaining life and implement strategies or priorities for action. In this context, the elastic modulus represents an effective indicator of the condition of the pavement which can be calculated through back-calculation procedures starting from surface deflections, obtained from a non-destructive test (such as the Heavy Weight Deflectometer). Nevertheless, the conventional inverse engineering analysis involves the use of an axial-symmetric pavement finite-element program able to evaluate stiffness values exclusively at the deflection measuring points. This study presents an alternative methodology for spatial modelling of the load- bearing capacity of the runway surface pavement layer from deflection data measured at specific points, using Shallow Artificial Neural Networks. The search of the optimal neural model hyperparameters has been addressed through a Bayesian Optimization procedure and a 5-fold cross-validation has been implemented for a fair performance evaluation, given the limited number of deflection measures available. Once the optimal model has been defined, the measured surface deflection data were linearly interpolated and resampled gridding data were used as a new input matrix of the neural model to predict the expected value of elastic moduli at non-sampled points on the runway. The optimal BO model has returned very satisfactory results with a value of Pearson Coefficient R averaged over 5-fold equal to 0.96597 and of Mean Squared Error averaged over 5-fold equal to 0.01849. In such a way, a contour map of the runway stiffness has been drawn, to provide a support tool for the planning of intervention priorities.

An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.

In general terms, an artificial neural network is a distributed processor that consists of elementary computational units interconnected. Such structure is inspired by the functioning principles of the biological nervous system and has proven to be effective in identifying complex relationships between an assigned input features vector and an experimental- investigated target vector for any scientific problem. The current paper represents a forward feasibility study on predicting the mechanical response of asphalt concretes prepared with different quarry fillers used as alternatives for traditional limestone filler or portland cement by Machine Learning approaches which consider the chemical properties of the selected fillers and the quarry aggregate types as input variables. In fact, the case study involved several fillers and stone aggregates that were used to produce Marshall specimens of a specific fine-grained asphalt concretes designed originally for the assessment of filler suitability in terms of adhesion phenomenon. The asphalt concrete variants had the same material composition and mix design: all specimens were compacted by 2x50 blows using impact compactor, filler content was fixed at 10% by mass of the mix, the grading curve is roughly the same, the employed bitumen has a 160/220 penetration grade and is about 6% by mass of the mix. The mineralogical composition was investigated by X-ray fluorescence spectrophotometry tests. It represents a non-destructive laboratory analysis that allowed to specify and compare the main oxides composition associated with the employed natural fillers to be identified. Based on the results thus obtained and applying a categorical variable that distinguishes the stone aggregate type, a neural model has been developed that can predict the stiffness modulus of asphalt mixtures: therefore, this study presents a possible procedure for the development of predictive models that can help or improve the mix design process, when different fillers and stone aggregates are available.

The valorisation and reuse of waste materials can enhance the environmental sustainability of road constructions, especially by means of cold recycling techniques, which, moreover, allow to reduce polluting emissions in atmosphere. Among the various technological approaches, the use of bitumen emulsion to stabilize waste materials is very common, especially in case of reclaimed asphalt pavement (RAP) aggregates. However, even other types of waste materials could be considered using a Cold Central Plant Recycling (CCPR) approach. The paper discusses the main results of a laboratory investigation aimed to evaluate the mechanical performance of bitumen emulsion stabilized mixtures for road pavements base courses, prepared with RAP, steel slag, coal ash and glass wastes, used with various percentages. In a first step of the laboratory study, both physical and toxicological properties of each waste material have been investigated, in order to assess their environmental compatibility. Subsequently, an extensive mechanical analysis of the bitumen emulsion stabilized mixtures has been carried out in the laboratory, in terms of indirect tensile strength, indirect tensile stiffness modulus at three temperatures (10°C, 25°C, 40°C) and repeated load axial tests at 30°C. The moisture resistance of the mixes has been also investigated by means of indirect tensile strength tests carried out on soaked specimens. Very good results have been observed, depending on the mix composition: indirect tensile strength at 25 °C on dry specimens up to 0.52 MPa and stiffness modulus up to 4,056 MPa (at 25 °C, for a rise time equal to 124 ms). Therefore, it has been verified that the waste materials considered in the study can be successfully reused to completely substitute conventional aggregates in bitumen emulsion stabilized mixtures for road pavements base courses.