Alternative Fillers in Asphalt Concrete Mixtures: Laboratory Investigation and Machine Learning Modeling towards Mechanical Performance Prediction

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

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

Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction

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

Synthesis, crystallographic characterization, and mechanical behavior of alumina chromia alloys

Powder mixtures of Alumina and Chromia, blended in different proportions (1, 3, 5 and 10%wt) by attrition milling, were fired either by pressureless sintering in air and hot pressing under vacuum. The resulting materials, characterized by X-ray diffraction, Raman spectroscopy, SEM, hardness and fracture toughness showed that all the compositions form complete solid solution which maintain the same crystal structures of corundum; chromia addition retards materials' densification of pressureless fired samples but not that of hot-pressed samples. Data from Raman spectroscopy and SEM/EDXS showed the appearance of Ti- and Mn-based impurities near the indentation print, in particular on fractured grains. The addition of chromia improves hardness, but does not affect toughness which is, on the other hand, greatly influenced by materials\u2019 residual porosity

The use of ALD and PVD coatings as defect sealants to increase the corrosion resistance of thermal spray coatings

Thermal spray coatings are widely used to improve the surface properties of materials, in particular the wear and oxidation resistance. Nevertheless, the corrosion resistance is slightly increased due to the fact that this type of coatings present some internal defects (pores, cracks) that allow the corrosive media to penetrate up to the substrate, that undergoes to corrosion degradation. The amount of these defects is strongly influenced by both the deposition technique and the material deposited. The aim of this work is to seal the internal porosities of the thermal spray coatings by the use of both PVD and ALD coatings or the combination of the two. The thermal spray coating analysed in this work is a pure alumina coating, deposited by Air Plasma Spray (APS) technique, that has been sealed with CrN coating, deposited by PVD (Physical Vapour Deposition) technique, and/or TiO2 coatings, deposited by ALD (Atomic Layer Deposition). The substrate used is a common medium C steel. The samples were then characterized in order to determine the microstructure (SEM+EDXS, light microscope) and the chemical composition (Rf-GDOES elemental profiling), that is important to determine the depth of penetration of the PVD and/or ALD coating inside the thermal spray deposit. Afterwards, a detailed electrochemical characterization in 3,5wt% NaCl aqueous solution was performed to verify the efficiency of the sealant treatment. In detail, a monitor in function of the time of the OCP potential was performed up to 24h of immersion time. In addition, potentiodynamic tests were performed using a 3 electrode electrochemical cell (CE: Pt wire, RE: Ag/AgCl). The same tests were then performed on the same samples that present an artificial defect produced by Rf-GDOES. The main goal of these tests is to determine the maximum depth of a defect that can allow the corrosive media to penetrate the thermal spray coating. Preliminary results showed that the use of PVD and ALD coatings as sealants can reduce the permeation of the corrosive media on the substrate

Microhardness and Young's modulus of high burn-up UO2 fuel

Vickers microhardness (HV0.1) and Young's modulus (E) measurements of LWR UO2 fuel at burn-up > 60 GWd/tHM are presented. Their ratio HV0.1/E was found constant in the range 60-110 GWd/tHM. From the ratio and the microhardness values vs porosity, the Young's modulus dependence on porosity was derived and extended to the full radial profile, including the high burn-up structure (HBS). The dependence is well represented by a linear correlation. The data were compared to fuel performance codes correlations. A burn-up dependent factor was introduced in the Young's modulus expression. The modifications extend the experimental validation range of the TRANSURANUS correlation from unirradiated to irradiated UO2 and up to 20% porosity. First simulations of LWR fuel rod irradiations were performed in order to illustrate the impact on fuel performance. In the specific cases selected, the simulations suggest a limited effect of the Young's modulus decrease due to burn-up on integral fuel performance

Factors affecting adherence to guidelines for antithrombotic therapy in elderly patients with atrial fibrillation admitted to internal medicine wards

Current guidelines for ischemic stroke prevention in atrial fibrillation or flutter (AFF) recommend Vitamin K antagonists (VKAs) for patients at high-intermediate risk and aspirin for those at intermediate-low risk. The cost-effectiveness of these treatments was demonstrated also in elderly patients. However, there are several reports that emphasize the underuse of pharmacological prophylaxis of cardio-embolism in patients with AFF in different health care settings. AIMS: To evaluate the adherence to current guidelines on cardio-embolic prophylaxis in elderly (> 65 years old) patients admitted with an established diagnosis of AFF to the Italian internal medicine wards participating in REPOSI registry, a project on polypathologies/polytherapies stemming from the collaboration between the Italian Society of Internal Medicine and the Mario Negri Institute of Pharmacological Research; to investigate whether or not hospitalization had an impact on guidelines adherence; to test the role of possible modifiers of VKAs prescription. METHODS: We retrospectively analyzed registry data collected from January to December 2008 and assessed the prevalence of patients with AFF at admission and the prevalence of risk factors for cardio-embolism. After stratifying the patients according to their CHADS(2) score the percentage of appropriateness of antithrombotic therapy prescription was evaluated both at admission and at discharge. Univariable and multivariable logistic regression models were employed to verify whether or not socio-demographic (age >80years, living alone) and clinical features (previous or recent bleeding, cranio-facial trauma, cancer, dementia) modified the frequency and modalities of antithrombotic drugs prescription at admission and discharge. RESULTS: Among the 1332 REPOSI patients, 247 were admitted with AFF. At admission, CHADS(2) score was ≥ 2 in 68.4% of patients, at discharge in 75.9%. Among patients with AFF 26.5% at admission and 32.8% at discharge were not on any antithrombotic therapy, and 43.7% at admission and 40.9% at discharge were not taking an appropriate therapy according to the CHADS(2) score. The higher the level of cardio-embolic risk the higher was the percentage of antiplatelet- but not of VKAs-treated patients. At admission or at discharge, both at univariable and at multivariable logistic regression, only an age >80 years and a diagnosis of cancer, previous or active, had a statistically significant negative effect on VKAs prescription. Moreover, only a positive history of bleeding events (past or present) was independently associated to no VKA prescription at discharge in patients who were on VKA therapy at admission. If heparin was considered as an appropriate therapy for patients with indication for VKAs, the percentage of patients admitted or discharged on appropriate therapy became respectively 43.7% and 53.4%. CONCLUSION: Among elderly patients admitted with a diagnosis of AFF to internal medicine wards, an appropriate antithrombotic prophylaxis was taken by less than 50%, with an underuse of VKAs prescription independently of the level of cardio-embolic risk. Hospitalization did not improve the adherence to guideline

Degradation Mechanisms Occurring in PTFE-Based Coatings Employed in Food-Processing Applications

The application of polytetrafluoroethylene (PTFE) coatings to metal surfaces is a well-known procedure carried out to avoid fouling phenomena on food-processing surfaces. Fluorine-based polymers are generally chemically and thermally stable, thus allowing them to be the preferred choice when designing anti-stick coatings in the food service industry. Their lifespan, however, depends on the environmental conditions. It is well known that thermal ageing can affect the properties of PTFE polymers and reduce their mechanical, thermal, and chemical properties causing failures and contaminating food. The main goal of the study is to identify the different failure mechanisms occurring in PTFE-based coatings, using both SEM/EDXS and ATR FT-IR data to reveal the starting point of degradation phenomena in food processing applications. The results from this research reveal that the preferential points for failures are mainly the polymer/substrate interfaces, the polymer/filler interfaces, or the polymer matrix itself

A machine learning approach to determine airport asphalt concrete layer moduli using heavy weight deflectometer data

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

An experimental analogue for direction of induced currents

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