Multivariate Regression of Road Segments’ Accident Data in Italian Rural Networks

Increasing traffic flows on road infrastructures and the associated comfort and safety problems have led to an increased risk of accidents for road users. To take the proper corrective actions, it is fundamental to analyze the accident phenomenon in all its aspects. The purpose of the current paper was the development of an accident prediction model for rural road segments of Friuli-Venezia Giulia (FVG) Region. The model predicts the accident frequency as a function of Annual Average Daily Traffic (AADT), segment length, and both geometrical and environmental features related to the targeted road segment. The procedure is based on the Empirical Bayes (EB) method. The statistical model used to express the road segments’ safety was the multivariate regression structure of the Safety Performance Functions. Results of a CURE plots analysis verified that the model is highly reliable in predicting the accident dataset for AADT up to 12500 vehicles per day

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

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

Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

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

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

Foamed Bitumen Mixtures for Road Construction Made with 100% Waste Materials: A Laboratory Study

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◦ C of 0.62 MPa (well above the Italian technical acceptance limits), a stiffness modulus at 25◦ C equal to 6171 MPa, and a number of cycles to failure at 20◦ C equal to 6989 for a stress level of 300 kPa

Sorption studies of strontium on carbon nanotubes using the Box-Behnken design

WOS: 000343091300008Adsorption of Sr on multi-walled carbon nanotubes (MWCNTs) was investigated to explore their possible use as an efficient adsorbent for nuclear waste streams. MWCNTs were purified and oxidized with HNO3 prior to testing adsorption. Oxidized MWCNTs were then employed in batch experiments as sorbent of Sr from aqueous solutions. The Box-Behnken experimental design was used to suitably vary the parameters of interest, i.e., temperature, initial Sr2+ concentration, and shaking time. Langmuir, Freundlich and Dubinin-Radushkevich models were applied to fit the adsorption isotherms. The Dubinin-Radushkevich model exhibited the best agreement. Adsorption kinetics was also studied; it was well described by a pseudo-second-order rate model. Adsorption thermodynamics was investigated in the temperature range 293-333 K; the variations of the standard free energy (Delta G degrees), standard enthalpy (Delta H degrees) and standard entropy (Delta S degrees) were obtained. Oxidized MWCNTs show the potential to be a promising candidate for the preconcentration and solidification of Sr from large volumes of aqueous solution

High-temperature Thermodynamics by Laser-vaporization Mass Spectrometry: an Approach Based on Statistical Mechanics

The problem of correlation between the temperature of the target surface and the mass-spectrometer signal in laser-vaporization mass spectrometry has been analyzed theoretically. An approach based on statistical mechanics has been applied in order to describe the transient vaporization into vacuum of molecules effused from the area of the target surface struck by a laser pulse of moderate power density and time duration of some tens of ms (Langmuir vaporization). In particular, an expression for the intensity of the output signal of the mass spectrometer, I(l,t), has been derived as a function of the detection time, t, and of the distance, l, of the ionizing chamber of the spectrometer from the target. A simple numerical method for the calculation of I(l,t) according to the time profile of the target temperature is also provided. By fitting experimental I(t) values with the theoretical expression one can retrieve thermodynamic quantities involved in the sublimation/evaporation process of the molecular species analyzed, such as enthalpy and equilibrium vapor pressure (or, alternatively, vaporization coefficient). As an illustration, this fitting was performed on experimental measurements of pyrolytic graphite sublimation in the temperature range 3200¿3700 K. The analysis developed will be useful for the interpretation of experimental datasets in order to retrieve high-temperature thermodynamic data, especially on high-melting materials. Research in this domain is being launched for nuclear materials, particularly for Generation IV advanced fuels.JRC.E.2-Hot cell