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Silica fume as a surrogate filler in asphalt concrete mixtures: Laboratory investigation and a machine learning-based prediction

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AIP Conference Proceedings 2023年第1期2928卷

作者： Nitin Tiwari Fabio Rondinella Neelima Satyam Nicola Baldo Department of Civil Engineering Indian Institute of Technology Indore India. Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy.

Transport infrastructure is a key asset in the global development of a society. Its improvement, both from the technologies and materials point of view, is a precondition to achieve sustainable and resilient progress. Under these conditions, it is necessary to investigate the feasibility of using waste materials (also called marginal materials) into a pavement mix composition in order to accomplish two main goals: adding useful life to a material that otherwise would be just a waste and saving on virgin materials supply. The presented paper aims at investigating the feasibility of using industrial waste silica fume (SF) as a surrogate filler instead of Ordinary Portland Cement (OPC). Mixtures were produced with different percentages of SF and OPC and their performance was investigated by using the results of Marshall and Indirect Tensile Strength (ITS) tests. Laboratory testing requires not only highly qualified technicians but also multiple samples in order to define functional relationships between the variables involved. In this regard, soft-computing techniques can be useful in reducing this workload and the resulting costs by identifying the aforementioned relationships by means of artificial intelligence. Therefore, the experimental data collected have been processed using Shallow Neural Networks (SNNs) that provided predictive models of the mixtures’ mechanical and volumetric parameters. Resampling and synthetic data generation techniques successfully addressed the difficulties caused by the relatively small dataset size. Results showed that the use of SF resulted in mixture performance comparable to that achieved by mixtures produced using OPC, occasionally even better. In addition, the proposed neural model performed remarkably well and thus could be used in the asphalt mixture optimization without the need for additional laboratory tests.

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Corrigendum to “100% renewable wastewater treatment plants: Techno-economic assessment using a modelling and optimization approach” [Energy Convers. Manage. 239 (2021) 114214]

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Energy Conversion and Management 2021年 244卷

作者： Pietro Elia Campana Matia Mainardis Alessandro Moretti Mattia Cottes School of Business Society & Engineering Mälardalen University Västerås Sweden Department Polytechnic of Engineering and Architecture (DPIA) Via del Cotonificio 108 University of Udine 33100 Udine Italy

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Recycling of Waste Materials Using Bitumen Emulsion for Road Pavement Stabilized Base Courses: a Laboratory Investigation

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IOP Conference Series: Materials Science and engineering 2021年第2期1203卷

作者： Nicola Baldo Matteo Miani Fabio Rondinella Marco Pasetto Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy Department of Civil Environmental and Architectural Engineering (DICEA) University of Padua Via Marzolo 9 35131 Padua Italy

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.

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Performance Prediction of Fine-Grained Asphalt Concretes with Different Quarry Fillers by Machine Learning Approaches

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IOP Conference Series: Materials Science and engineering 2021年第2期1203卷

作者： Nicola Baldo Matteo Miani Fabio Rondinella Pavla Vacková Jan Valentin Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy Faculty of Civil Engineering Czech Technical University in Prague Thákurova 7 Prague 166 29 Czech Republic

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 di

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Artificial Neural Network Prediction of Airport Pavement Moduli Using Interpolated Surface Deflection Data

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IOP Conference Series: Materials Science and engineering 2021年第2期1203卷

作者： Nicola Baldo Matteo Miani Fabio Rondinella Clara Celauro Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy Department of Civil Environmental Aerospace and Materials Engineering (DICAM) University of Palermo Via delle Scienze - Parco D'Orleans – Building n°8 90128 Palermo Italy

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.

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Numerical Characterization of High Modulus Asphalt Concrete Containing RAP: A Comparison among Optimized Shallow Neural Models

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IOP Conference Series: Materials Science and engineering 2020年第2期960卷

作者： Nicola Baldo Jan Valentin Evangelos Manthos Matteo Miani Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy Faculty of Civil Engineering Czech Technical University in Prague Thákurova 7 166 29 Prague Czech Republic Department of Civil Engineering Aristotle University of Thessaloniki University Campus 54124 Thessaloniki Greece

Knowing the relationship between the stiffness modulus and the empirical mechanical characteristics of asphalt concrete, road engineers may predict the expected results of costly laboratory tests and save both time and financial resources in the mix design phase. In fact, such a model would make it possible to assess a priori whether the stiffness of a specific mixture, characterised in the laboratory only by the common Marshall test, is suitable for the level of service required by the road pavement under analysis. In this study, 54 Marshall test specimens of high modulus asphalt concrete were prepared and tested in the laboratory to determine an empirical relationship between the stiffness modulus and Marshall stability by means of shallow artificial neural networks. Part out of these mixtures was characterised by different types of bitumen (20/30 or 50/70 penetration grade) and percentages of used reclaimed asphalt (RAP at 20% or 30%); a polymer modified bitumen was used in the preparation of the remaining Marshall test specimens, which do not contain RAP. For the complex and laborious identification of the neural model hyperparameters, which define its architecture and algorithmic functioning, the Bayesian optimization approach has been adopted. Although the results of this methodology depend on the predefined hyperparameters variability ranges, it allows an unbiased definition of the optimal neural model characteristics to be performed by minimizing (or maximizing) a loss function. In this study, the mean square error on 5 validation folds was used as a loss function, in order to avoid a poor performance evaluation due to the small number of samples. In addition, 3 different neural training algorithms were applied to compare results and convergence times. The procedure presented in this study is a valuable guide for the development of predictive models of asphalt concretes' behaviour, even for different types of bitumen and aggregates considered here.

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A machine learning approach for the prediction of very thin wearing layers asphalt concretes volumetric properties and performance

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AIP Conference Proceedings 2023年第1期2928卷

作者： Nicola Baldo Matteo Miani Fabio Rondinella Evangelos Manthos Jan Valentin Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy. Department of Civil Engineering Aristotle University of Thessaloniki University Campus 54124 Thessaloniki Greece. Faculty of Civil Engineering Czech Technical University Thákurova 7 166 29 Prague Czech Republic.

In recent years, many researchers in the field of pavement engineering have worked with the aim of developing a model capable of predicting the mechanical behavior of a mixture starting from its composition’s parameters. This has been done following two different approaches. The first involved the use of advanced constitutive laws based on the materials mechanics; the second, instead of being physically based, was data-driven. The present work belongs to this second context and aims to present, implement and apply a strategy to develop the optimal model for solving an assigned predictive problem. Specifically, a Machine Learning approach, a Feedforward Backpropagation Shallow Neural Network, was investigated. The objective was to correlate stiffness modulus, air voids and voids in the mineral aggregate to the mixture main composition’s parameters identified in: bitumen content, particle size and a categorical variable distinguishing the bitumen type and production site. Since the maximum aggregate size is 10 mm, the sieves considered were of 10, 6.3, 2, 0.5 and 0.063-mm diameters. The present study focused on 92 variants of asphalt concretes for very thin road pavement wearing layers produced both in plant and in laboratory. Despite the wide variation ranges of each parameter considered, the optimal model returns fully satisfactory performance. The overall Pearson correlation coefficient is equal to 0.9490, also by virtue of the innovative algorithms implemented as k-fold Cross-Validation (CV) and Bayesian Optimization (BO). These algorithms have allowed on the one hand the improvement of the model’s predictive performance making them more reliable and, on the other hand, the optimization of hyperparameters and architecture. The methodology developed can become an important reference in this field since it is independent from the specific predictive application. In this sense, it can help other researchers in the fine-tuning of neural models in the field of pavement

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Effects of Cognitive Distraction on Driver's Stopping Behaviour: A Virtual Car Driving Simulator Study

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IOP Conference Series: Materials Science and engineering 2020年第2期960卷

作者： Nicola Baldo Andrea Marini Matteo Miani Polytechnic Department of Engineering and Architecture (DPIA) University of Udine Via del Cotonificio 114 33100 Udine Italy Department of Languages and Literatures Communication Education and Society (DILL) University of Udine Via Margreth 3 33100 Udine Italy Claudiana - Landesfachhochschule für Gesundheitsberufe Bolzano Italy

Drivers are prone to distractions while driving, due to conversations they have with passengers on board, processing their thoughts or using their mobile phones. These distractions result in a mental workload that compromises driving safety and requires the implementation of risk compensatory behaviours. This study examines the effects of hands-free mobile phone conversations on young drivers' stopping manoeuvres when a pedestrian enters a zebra crossing. A cohort of seventy-eight university students, aged 20-30 years old, performed a driving task in a virtual urban environment, by means of a virtual car driving simulator. They formed a control and an experimental group, balanced on age and IQ level. The control group was left free to drive without any imposed cognitive task. The experimental group was asked to drive while making a phone call that was planned to diminish the amount of cognitive resources allocated to the driving experience. For both groups, the analyses focused on a specific moment, i.e., while a child suddenly entered a zebra crossing from a sidewalk. Throughout the simulation, the intensity of the participants' actions on the brake pedal, accelerator, and steering wheel were recorded with a time step of 250 ms. Before the virtual driving experiment, each participant completed a questionnaire on his/her daily driving style, involvement in road accidents, and general mobile phone usage even while driving. A mixed two-way ANOVA with Group as a between-subject factor (1. Control Group; 2. Experimental Group) and Gender (1. Male drivers; 2. Female drivers) as a within-subject factor was performed on the driving parameters as dependent variables. The results showed the presence of a significant difference for distracted and non-distracted drivers with the absence of gender-related differences across the two groups. Participants engaged in a hands-free phone-call while driving assumed lower initial speeds as an element of risk compensation and took the f

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