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Dr. Nicola Baldo
University of Udine

Basic Info


Research Keywords & Expertise

0 Road Safety
0 Pavement Materials and Design
0 Driver behavior
0 Machine and Deep Learning
0 Highway Engineering

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Short Biography

He graduated in Civil Engineering, Transport, at the University of Padua (December 2000), and obtained a PhD in Geodesy and Geomatics, at the Politecnico di Milano (May 2006). He is currently Associate Professor at the University of Udine (December 2018 - today), for which he previously served as Assistant Professor (November 2010 - November 2018). He serves at the Polytechnic Department of Engineering and Architecture - DPIA (January 2016 - today). Coordinator of the M.Sc. in Environmental Engineering (October 2016 - today). Lecturer of Road, Railways and Airports Construction (BS.C. in Civil and Environmental Engineering), and Transport Infrastructures Design (MS.C. in Civil Engineering). Head of the research group "Road Infrastructures" and of the Car Driving Simulator Lab. of DPIA. His main research topics are: analysis and modeling of road safety, study of driving behaviors in complex infrastructural, environmental and cognitive conditions, analysis and modeling of experimental data related to the mechanical characterization of road infrastructure materials, also with advanced computational approaches based on Machine Learning algorithms. Speaker at numerous international scientific workshops and conferences. Reviewer and member of the editorial board of international scientific journals in civil, environmental and road engineering.

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Journal article
Published: 28 August 2021 in Sustainability
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Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle–pedestrian (V2P) interaction state but also proactively anticipate the event’s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to 3 s ahead in time, the severity level of V2P encounters, depending on the current scene representation drawn from on-board radars’ data. A car-driving simulator experiment has been designed to collect sequential mobility features on a cohort of 65 licensed university students who faced different V2P conflicts on a planned urban route. To accurately describe the pedestrian safety condition during the encounter process, a combination of surrogate safety indicators, namely TAdv (Time Advantage) and T2 (Nearness of the Encroachment), are considered for modeling. Due to the nature of these indicators, multiple recurrent neural networks are trained to separately predict T2 continuous values and TAdv categories. Afterwards, their predictions are exploited to label serious conflict interactions. As a comparison, an additional Gated Recurrent Unit (GRU) neural network is developed to directly predict the severity level of inner-city encounters. The latter neural model reaches the best performance on the test set, scoring a recall value of 0.899. Based on selected threshold values, the presented models can be used to label pedestrians near accident events and to enhance existing intelligent driving systems.

ACS Style

Matteo Miani; Matteo Dunnhofer; Christian Micheloni; Andrea Marini; Nicola Baldo. Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit. Sustainability 2021, 13, 9681 .

AMA Style

Matteo Miani, Matteo Dunnhofer, Christian Micheloni, Andrea Marini, Nicola Baldo. Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit. Sustainability. 2021; 13 (17):9681.

Chicago/Turabian Style

Matteo Miani; Matteo Dunnhofer; Christian Micheloni; Andrea Marini; Nicola Baldo. 2021. "Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit." Sustainability 13, no. 17: 9681.

Journal article
Published: 06 August 2021 in Sustainability
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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.

ACS Style

Nicola Baldo; Matteo Miani; Fabio Rondinella; Clara Celauro. A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data. Sustainability 2021, 13, 8831 .

AMA Style

Nicola Baldo, Matteo Miani, Fabio Rondinella, Clara Celauro. A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data. Sustainability. 2021; 13 (16):8831.

Chicago/Turabian Style

Nicola Baldo; Matteo Miani; Fabio Rondinella; Clara Celauro. 2021. "A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data." Sustainability 13, no. 16: 8831.

Conference paper
Published: 10 December 2020 in IOP Conference Series: Materials Science and Engineering
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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.

ACS Style

Nicola Baldo; Jan Valentin; Evangelos Manthos; Matteo Miani. Numerical Characterization of High Modulus Asphalt Concrete Containing RAP: A Comparison among Optimized Shallow Neural Models. IOP Conference Series: Materials Science and Engineering 2020, 960, 022083 .

AMA Style

Nicola Baldo, Jan Valentin, Evangelos Manthos, Matteo Miani. Numerical Characterization of High Modulus Asphalt Concrete Containing RAP: A Comparison among Optimized Shallow Neural Models. IOP Conference Series: Materials Science and Engineering. 2020; 960 (2):022083.

Chicago/Turabian Style

Nicola Baldo; Jan Valentin; Evangelos Manthos; Matteo Miani. 2020. "Numerical Characterization of High Modulus Asphalt Concrete Containing RAP: A Comparison among Optimized Shallow Neural Models." IOP Conference Series: Materials Science and Engineering 960, no. 2: 022083.

Conference paper
Published: 10 December 2020 in IOP Conference Series: Materials Science and Engineering
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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 first action to stop at shorter distances from the pedestrian crossing. This suggests a delayed perception of the presence of the pedestrian. In addition, the fluctuation in speed after the distracted driver had released the accelerator pedal reached a statistical significance compared to the control group. These findings suggest that the distraction induced by the use of the mobile phone through the earphones may adversely affect driving behaviour and raise significant safety concerns.

ACS Style

Nicola Baldo; Andrea Marini; Matteo Miani. Effects of Cognitive Distraction on Driver’s Stopping Behaviour: A Virtual Car Driving Simulator Study. IOP Conference Series: Materials Science and Engineering 2020, 960, 022082 .

AMA Style

Nicola Baldo, Andrea Marini, Matteo Miani. Effects of Cognitive Distraction on Driver’s Stopping Behaviour: A Virtual Car Driving Simulator Study. IOP Conference Series: Materials Science and Engineering. 2020; 960 (2):022082.

Chicago/Turabian Style

Nicola Baldo; Andrea Marini; Matteo Miani. 2020. "Effects of Cognitive Distraction on Driver’s Stopping Behaviour: A Virtual Car Driving Simulator Study." IOP Conference Series: Materials Science and Engineering 960, no. 2: 022082.

Journal article
Published: 01 October 2020 in Behavioral Sciences
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In this study, a cohort of 78 university students performed a driving experience in a virtual urban scenario, by means of a car driving simulator, to examine effects of a planned hands-free mobile phone conversation on young drivers’ braking behaviors. To this aim, a control group was left free to drive without any imposed cognitive task. An experimental group faced the same scenario while engaged in a phone call. The conversation via earphones was arranged to diminish the amount of cognitive resources allocated to the driving task. For both groups, the analyses focused on the moment at which a child entered a pedestrian crossing from a sidewalk. The results of a mixed two-way ANOVA showed the presence of a significant difference for distracted and non-distracted drivers with the absence of gender-related differences across the two groups. Distracted participants assumed lower initial speeds, took the first action to stop at shorter distances from the zebra crossing, and had more difficulty in keeping speed variations under control. These findings suggest that the distraction induced by the use of earphones may induce risk compensation behaviors and delay pedestrian perception. Moreover, the effects on the participants' braking behavior suggest that the procedure adopted to increase cognitive load, based on a story retelling, is an effective method to analyze the impact of hands-free cellphone use on driving skills in a car simulation experiment.

ACS Style

Nicola Baldo; Andrea Marini; Matteo Miani. Drivers’ Braking Behavior Affected by Cognitive Distractions: An Experimental Investigation with a Virtual Car Simulator. Behavioral Sciences 2020, 10, 150 .

AMA Style

Nicola Baldo, Andrea Marini, Matteo Miani. Drivers’ Braking Behavior Affected by Cognitive Distractions: An Experimental Investigation with a Virtual Car Simulator. Behavioral Sciences. 2020; 10 (10):150.

Chicago/Turabian Style

Nicola Baldo; Andrea Marini; Matteo Miani. 2020. "Drivers’ Braking Behavior Affected by Cognitive Distractions: An Experimental Investigation with a Virtual Car Simulator." Behavioral Sciences 10, no. 10: 150.

Conference paper
Published: 30 August 2019 in Proceedings of EECE 2020
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The paper deals with a laboratory study of bitumen emulsion bound mixtures for road pavements with an aggregate structure totally composed of waste materials, i.e. reclaimed asphalt pavement (RAP), steel slag, coal ash and glass wastes, combined in different ratios. The investigation was divided into a preliminary environmental and physical analysis of each waste material and a subsequent mechanical characterization of the bitumen emulsion bound mixtures, by means of indirect tensile strength, stiffness modulus and repeated load axial tests. Indirect tensile strength tests were also performed in wet conditions to evaluate the moisture resistance of the mixes. The main outcomes of the trial (indirect tensile strength at 25 °C on dry samples up to 0.37 MPa; stiffness modulus at 25 °C and 2 Hz up to 4,266 MPa, depending on the mixture) were compared with the requisites for acceptance of the main Italian Contract Specifications, which demonstrated that the analyzed marginal materials are suitable for use as integral substitutes of natural aggregates in the production of bitumen emulsion bound mixtures for road pavements.

ACS Style

Marco Pasetto; Nicola Baldo. Cold Recycling with Bitumen Emulsion of Marginal Aggregates for Road Pavements. Proceedings of EECE 2020 2019, 155 -163.

AMA Style

Marco Pasetto, Nicola Baldo. Cold Recycling with Bitumen Emulsion of Marginal Aggregates for Road Pavements. Proceedings of EECE 2020. 2019; ():155-163.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2019. "Cold Recycling with Bitumen Emulsion of Marginal Aggregates for Road Pavements." Proceedings of EECE 2020 , no. : 155-163.

Journal article
Published: 25 August 2019 in Applied Sciences
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The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation (R) and mean square errors; in particular, R values were within the range 0.965–0.919 in the training phase and 0.881–0.834 in the CV testing phase, depending on the predicted parameters.

ACS Style

Nicola Baldo; Evangelos Manthos; Matteo Miani. Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation. Applied Sciences 2019, 9, 3502 .

AMA Style

Nicola Baldo, Evangelos Manthos, Matteo Miani. Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation. Applied Sciences. 2019; 9 (17):3502.

Chicago/Turabian Style

Nicola Baldo; Evangelos Manthos; Matteo Miani. 2019. "Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation." Applied Sciences 9, no. 17: 3502.

Research article
Published: 12 July 2018 in Advances in Civil Engineering
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The current paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using artificial neural networks (ANNs). The asphalt concrete mixes considered in this study have been prepared with a diabase aggregate skeleton and two different types of bitumen, namely, a conventional bituminous binder and a polymer-modified one. The asphalt concretes were produced both in a road materials laboratory and in an asphalt concrete production plant. The mechanical behaviour of the mixes was investigated in terms of Marshall stability, flow, quotient, and moreover by the stiffness modulus. The artificial neural networks used for the numerical analysis of the experimental data, of the feedforward type, were characterized by one hidden layer and 10 artificial neurons. The results have been extremely satisfactory, with coefficients of correlation in the testing phase within the range 0.98798–0.91024, depending on the considered model, thus demonstrating the feasibility to apply ANN modelization to predict the mechanical and performance response of the asphalt concretes investigated. Furthermore, a closed-form equation has been provided for each of the four ANN models developed, assuming as input parameters the production process, the bitumen type and content, the filler/bitumen ratio, and the volumetric properties of the mixes. Such equations allow any other researcher to predict the mechanical parameter of interest, within the framework of the present study.

ACS Style

Nicola Baldo; Evangelos Manthos; Marco Pasetto. Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks. Advances in Civil Engineering 2018, 2018, 1 -17.

AMA Style

Nicola Baldo, Evangelos Manthos, Marco Pasetto. Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks. Advances in Civil Engineering. 2018; 2018 ():1-17.

Chicago/Turabian Style

Nicola Baldo; Evangelos Manthos; Marco Pasetto. 2018. "Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks." Advances in Civil Engineering 2018, no. : 1-17.

Journal article
Published: 01 January 2018 in Environmental Engineering and Management Journal
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ACS Style

Marco Pasetto; Nicola Baldo. RE-USE OF INDUSTRIAL WASTES IN CEMENT BOUND MIXTURES FOR ROAD CONSTRUCTION. Environmental Engineering and Management Journal 2018, 17, 417 -426.

AMA Style

Marco Pasetto, Nicola Baldo. RE-USE OF INDUSTRIAL WASTES IN CEMENT BOUND MIXTURES FOR ROAD CONSTRUCTION. Environmental Engineering and Management Journal. 2018; 17 (2):417-426.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2018. "RE-USE OF INDUSTRIAL WASTES IN CEMENT BOUND MIXTURES FOR ROAD CONSTRUCTION." Environmental Engineering and Management Journal 17, no. 2: 417-426.

Journal article
Published: 01 September 2017 in International Journal of Pavement Research and Technology
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The paper discusses the results of an experimental study and a statistical analysis on the stiffness and the fatigue performance of recycled asphalt concretes, evaluated by the four-point bending test, at 20 °C and 10 Hz. The laboratory study was conducted on five different base-binder bituminous mixtures, made with recycled aggregates, namely Reclaimed Asphalt Pavement aggregate (RAP) and Electric Arc Furnace (EAF) steel slag, up to 70% by weight of the aggregate. In order to evaluate statistically the influence of the recycled aggregates on the stiffness of the mixes, the analysis of variance (ANOVA) has been performed on the modulus data. The fatigue tests were performed in stress and strain control mode, in order to describe completely the fatigue properties of the mixes. A dissipated energy method, based on the internal damage produced within the asphalt concretes, was used for the fatigue analysis. The damage curves, expressed in terms of the Plateau Value of the Ratio of Dissipated Energy Change, for both the stress and the strain control mode, were elaborated and statistically analyzed in order to unify the fatigue analysis. Compared to the control asphalt concrete, made exclusively with natural aggregate, the resulting mixes with RAP and EAF slag were characterized by improved stiffness and fatigue performance

ACS Style

Marco Pasetto; Nicola Baldo. Dissipated energy analysis of four-point bending test on asphalt concretes made with steel slag and RAP. International Journal of Pavement Research and Technology 2017, 10, 446 -453.

AMA Style

Marco Pasetto, Nicola Baldo. Dissipated energy analysis of four-point bending test on asphalt concretes made with steel slag and RAP. International Journal of Pavement Research and Technology. 2017; 10 (5):446-453.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2017. "Dissipated energy analysis of four-point bending test on asphalt concretes made with steel slag and RAP." International Journal of Pavement Research and Technology 10, no. 5: 446-453.

Book chapter
Published: 20 July 2017 in Bearing Capacity of Roads, Railways and Airfields
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ACS Style

M. Pasetto; E. Pasquini; Giovanni Giacomello; Andrea Baliello; Nicola Baldo; Romel Georgees; R Hassan; R Evans; P Jegatheesan; Andreas Loizos; L Al-Qadi; A (Tom). High-performance synthetic microfibers for the structural reinforcement of hot mix asphalts. Bearing Capacity of Roads, Railways and Airfields 2017, 1183 -1189.

AMA Style

M. Pasetto, E. Pasquini, Giovanni Giacomello, Andrea Baliello, Nicola Baldo, Romel Georgees, R Hassan, R Evans, P Jegatheesan, Andreas Loizos, L Al-Qadi, A (Tom). High-performance synthetic microfibers for the structural reinforcement of hot mix asphalts. Bearing Capacity of Roads, Railways and Airfields. 2017; ():1183-1189.

Chicago/Turabian Style

M. Pasetto; E. Pasquini; Giovanni Giacomello; Andrea Baliello; Nicola Baldo; Romel Georgees; R Hassan; R Evans; P Jegatheesan; Andreas Loizos; L Al-Qadi; A (Tom). 2017. "High-performance synthetic microfibers for the structural reinforcement of hot mix asphalts." Bearing Capacity of Roads, Railways and Airfields , no. : 1183-1189.

Research article
Published: 26 February 2017 in Advances in Materials Science and Engineering
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The paper introduces and analyses the results of an experimental trial on the fatigue resistance of recycled hot mix asphalt for road pavements. Based on the gyratory compaction and the indirect tensile strength test, the mix design procedure has optimized nine different mixes, considering both conventional limestone and Reclaimed Asphalt Pavement (RAP), the latter used at different quantities, up to 40% by weight of the aggregate. A standard bitumen and two polymer modified binders were used for the production of the mixes. The fatigue study was carried out with four-point bending tests, each one performed at 20°C and 10 Hz. The empirical stiffness reduction method, along with the energy ratio approach, based on the dissipated energy concept, was adopted to elaborate the experimental data. Unaged and aged specimens were checked, to analyse the ageing effects on the fatigue performance. In comparison with the control mixes, produced only with limestone, improved fatigue performance was noticed for the mixtures prepared with RAP, especially when made with polymer modified binders, under both aged and unaged conditions. Both the approaches adopted for the experimental data analysis have outlined the same ranking of the mixes.

ACS Style

Marco Pasetto; Nicola Baldo. Fatigue Performance of Recycled Hot Mix Asphalt: A Laboratory Study. Advances in Materials Science and Engineering 2017, 2017, 1 -10.

AMA Style

Marco Pasetto, Nicola Baldo. Fatigue Performance of Recycled Hot Mix Asphalt: A Laboratory Study. Advances in Materials Science and Engineering. 2017; 2017 ():1-10.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2017. "Fatigue Performance of Recycled Hot Mix Asphalt: A Laboratory Study." Advances in Materials Science and Engineering 2017, no. : 1-10.

Journal article
Published: 19 December 2016 in Materials and Structures
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ACS Style

Marco Pasetto; Nicola Baldo. Unified approach to fatigue study of high performance recycled asphalt concretes. Materials and Structures 2016, 50, 1 .

AMA Style

Marco Pasetto, Nicola Baldo. Unified approach to fatigue study of high performance recycled asphalt concretes. Materials and Structures. 2016; 50 (2):1.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2016. "Unified approach to fatigue study of high performance recycled asphalt concretes." Materials and Structures 50, no. 2: 1.

Journal article
Published: 01 October 2016 in Journal of Traffic and Transportation Engineering (English Edition)
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This paper presents and discusses a visco-elastoplastic constitutive model for the analysis of the creep deformability of asphalt concretes at high service temperature, finalized to improve the interpretation of the permanent deformation phenomenon and the performance design of the road pavements. A three dimensional constitutive visco-elastoplastic model has been introduced, in tensor as well as in numerical form. The associated uniaxial model has been obtained arranging a plastic element in series with a viscoelastic component, the latter defined by an elastic spring in parallel with three Maxwell elements. Three different hardening laws, namely isotropic, kinematic and mixed hardening, were considered in the constitutive model, in order to compare the different creep deformability. The proposed constitutive model has been calibrated and validated on the basis of uniaxial creep-recovery test results at 40°C, performed on a High Performance Hot Mix Asphalt concrete (HP-HMA), at different stress and loading times. The permanent deformation data predicted by the proposed model resulted reasonably consistent with the experimental creep-recovery data, depending on the hardening law considered. A rational constitutive model, physically congruent with the creep phenomenon of the asphalt concretes, has been developed and calibrated, in order to achieve a deeper understanding of the stress-strain response of such materials. It has been demonstrated the fundamental relevance of an appropriate modeling of the plastic response, in the study of the creep behavior of asphalt concretes for highway and road pavements

ACS Style

Marco Pasetto; Nicola Baldo. Numerical visco-elastoplastic constitutive modelization of creep recovery tests on hot mix asphalt. Journal of Traffic and Transportation Engineering (English Edition) 2016, 3, 390 -397.

AMA Style

Marco Pasetto, Nicola Baldo. Numerical visco-elastoplastic constitutive modelization of creep recovery tests on hot mix asphalt. Journal of Traffic and Transportation Engineering (English Edition). 2016; 3 (5):390-397.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2016. "Numerical visco-elastoplastic constitutive modelization of creep recovery tests on hot mix asphalt." Journal of Traffic and Transportation Engineering (English Edition) 3, no. 5: 390-397.

Journal article
Published: 01 April 2016 in Construction and Building Materials
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This paper discusses the results of a study aimed at designing cement bound mixtures for road construction, made with steel slag, ladle furnace slag, waste foundry sand, glass wastes and coal ash. The mixtures were designed by means of Proctor, compression and indirect tensile tests. Their performance was investigated in terms of elastic modulus, through ultrasonic tests at different curing times. Satisfactory results were obtained, compression and indirect tensile strength at 7 days being up to 7.56 MPa and 0.78 MPa respectively, depending on the composition of the mixtures.

ACS Style

Marco Pasetto; Nicola Baldo. Recycling of waste aggregate in cement bound mixtures for road pavement bases and sub-bases. Construction and Building Materials 2016, 108, 112 -118.

AMA Style

Marco Pasetto, Nicola Baldo. Recycling of waste aggregate in cement bound mixtures for road pavement bases and sub-bases. Construction and Building Materials. 2016; 108 ():112-118.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2016. "Recycling of waste aggregate in cement bound mixtures for road pavement bases and sub-bases." Construction and Building Materials 108, no. : 112-118.

Journal article
Published: 01 September 2015 in Construction and Building Materials
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This report introduces a visco-elasto-plastic constitutive model for the characterisation of stress–strain\ud behaviour in bituminous mixtures. With the introduction of the Helmholtz free energy and using the\ud concept of internal variables, it has been possible to express a plastic flow law, characterised by isotropic\ud hardening. The formulation of the constitutive relationship has been developed in such a way as to verify\ud a priori the universal dissipation principle, expressed by the Clausius–Duhem dissipative inequality. The\ud model has been calibrated and validated on the basis of the creep recovery response of two different\ud bituminous mixtures under various stress levels and loading time

ACS Style

Marco Pasetto; Nicola Baldo. Computational analysis of the creep behaviour of bituminous mixtures. Construction and Building Materials 2015, 94, 784 -790.

AMA Style

Marco Pasetto, Nicola Baldo. Computational analysis of the creep behaviour of bituminous mixtures. Construction and Building Materials. 2015; 94 ():784-790.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2015. "Computational analysis of the creep behaviour of bituminous mixtures." Construction and Building Materials 94, no. : 784-790.

Book chapter
Published: 30 August 2015 in RILEM Bookseries
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ACS Style

Marco Pasetto; Nicola Baldo. Fatigue Performance of Stone Mastic Asphalt Designed with the Bailey’s Method. RILEM Bookseries 2015, 11, 1005 -1016.

AMA Style

Marco Pasetto, Nicola Baldo. Fatigue Performance of Stone Mastic Asphalt Designed with the Bailey’s Method. RILEM Bookseries. 2015; 11 ():1005-1016.

Chicago/Turabian Style

Marco Pasetto; Nicola Baldo. 2015. "Fatigue Performance of Stone Mastic Asphalt Designed with the Bailey’s Method." RILEM Bookseries 11, no. : 1005-1016.

Book chapter
Published: 30 August 2015 in RILEM Bookseries
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ACS Style

Nicola Baldo; Evaggelos Manthos; Marco Pasetto; A. F. Nikolaides. Comparative Analysis of Stiffness Modulus and Fatigue Resistance of Asphalt Concretes Containing RAP Materials. RILEM Bookseries 2015, 11, 915 -926.

AMA Style

Nicola Baldo, Evaggelos Manthos, Marco Pasetto, A. F. Nikolaides. Comparative Analysis of Stiffness Modulus and Fatigue Resistance of Asphalt Concretes Containing RAP Materials. RILEM Bookseries. 2015; 11 ():915-926.

Chicago/Turabian Style

Nicola Baldo; Evaggelos Manthos; Marco Pasetto; A. F. Nikolaides. 2015. "Comparative Analysis of Stiffness Modulus and Fatigue Resistance of Asphalt Concretes Containing RAP Materials." RILEM Bookseries 11, no. : 915-926.

Book chapter
Published: 21 May 2015 in Bituminous Mixtures and Pavements VI
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ACS Style

M Pasetto; N Baldo. Moisture damage and low temperature cracking of bituminous mixtures made with recycled aggregates. Bituminous Mixtures and Pavements VI 2015, 617 -621.

AMA Style

M Pasetto, N Baldo. Moisture damage and low temperature cracking of bituminous mixtures made with recycled aggregates. Bituminous Mixtures and Pavements VI. 2015; ():617-621.

Chicago/Turabian Style

M Pasetto; N Baldo. 2015. "Moisture damage and low temperature cracking of bituminous mixtures made with recycled aggregates." Bituminous Mixtures and Pavements VI , no. : 617-621.

Book chapter
Published: 21 May 2015 in Bituminous Mixtures and Pavements VI
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ACS Style

M Pasetto; N Baldo. Fatigue characterization of modified asphalt concretes by means of dissipated energy approaches. Bituminous Mixtures and Pavements VI 2015, 379 -384.

AMA Style

M Pasetto, N Baldo. Fatigue characterization of modified asphalt concretes by means of dissipated energy approaches. Bituminous Mixtures and Pavements VI. 2015; ():379-384.

Chicago/Turabian Style

M Pasetto; N Baldo. 2015. "Fatigue characterization of modified asphalt concretes by means of dissipated energy approaches." Bituminous Mixtures and Pavements VI , no. : 379-384.