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46 results on '"Jiménez-del-Barco, A."'

1. Life Cycle Assessment of Bio-Reused Asphalt Pavements

Author

Jiménez del Barco Carrión, Ana, Mantalovas, Konstantinos, Pouget, Simon, Porot, Laurent, Williams, Chris, Blanc, Juliette, Hornych, Pierre, Planche, Jean-Pascal, Chailleux, Emmanuel, Lo Presti, Davide, Vasconcelos, Kamilla, editor, Jiménez del Barco Carrión, Ana, editor, Chailleux, Emmanuel, editor, and Lo Presti, Davide, editor

Published
2024
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3. Summary of Rilem Technical Committee TC 279-WMR Activities

Author

Lily D. Poulikakos, Emiliano Pasquini, Marjan Tušar, Jorge Pais, Augusto Cannone Falchetto, Fernando Moreno Navarro, Davide Lo Presti, Ana Jiménez del Barco Carrion, and Di Wang

Subjects
Asphalt, Bitumen, Waste, LCA, Performance, Building construction, TH1-9745
Abstract

Several alternative paving materials, namely plastic, crumb rubber, steel slag, and construction and demolition waste, were incorporated into the construction of road pavements, highlighting their favorable environmental and economic outcomes. Despite the considerable demand for such sustainable practices, the effort is mainly confined to research activities and within a limited number of countries, preventing further advancements and broader validation capable of reaching full acceptance at the market level for alternative road materials. RILEM TC 279-WMR "Valorisation of Waste and Secondary Materials for Roads" sought to promote the adoption of these alternative paving materials based on inter-laboratory activities addressing the behavior of the bituminous blends designed with marginal materials aiming to define tailored methodologies targeting selection, preparation, and use at the production level. Experimental results indicate that using alternative materials can result in similar performance compared to the corresponding virgin materials, keeping in mind that mix designs might need to be adjusted, for example, using higher binder content. In addition, adopting appropriate test procedures is essential as such new composite can be susceptible to specific failure modes or scale effects. Furthermore, despite the mechanical performance, environmental and life cycle assessments need to be evaluated to ensure that such materials are sustainable and valuable.

Published
2024
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4. Identification of the optimal implementation of the microwave self-healing technology for pavements based on sustainability assessment and technical parameters

Author

Ana María Rodríguez-Alloza, Miguel Sol-Sánchez, Daniel Garraín, and Ana Jiménez del Barco Carrión

Subjects
Asphalt pavement, Self-healing technology, Microwave treatment, Steel slag, Maintenance strategy, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Self-healing is a preventive maintenance technique developed to extend the service life of road pavements, accordingly, increasing their sustainability: energy consumption savings, lower emissions, and a decrease in the use of non-renewable resources. Microwave (MW) self-healing technology consists of heating the binder so it can flow and fill the cracks in the pavement. To promote and enhance this asphalt self-healing capacity, electrically conductive susceptible particles like steel slag can be added as aggregates in the asphalt mixture, and a combination of heat and re-compaction energy (thermomechanical treatment) can be applied to improve healing rates. However, one of the key issues is determining how to take full advantage of asphalt mixtures self-healing capacity to maximize the extended life span of asphalt pavements in order to avoid the use of a great number of non-renewable resources and energy in the rehabilitation of deteriorated pavements. Although some different factors that affect self-healing effectiveness are being studied, the optimal implementation conditions and moment to apply the MW treatment have yet to be determined. Hence, this paper aims to identify variables and establish an optimal implementation of the MW self-healing technology including steel slags and a thermomechanical treatment (MW heating and re-compaction). For this purpose, the influence of the implementation conditions, the moment of application, and the mixture design have been assessed by Life-Cycle Greenhouse Gas emissions, Cumulative Energy Demand, and cost evaluation. Results have shown that the optimal moment to apply the MW treatment would be around the half-life span of the asphalt pavement, being the most beneficial solution when applied over asphalt mixtures using steel slag as this allows reducing energy requirement during the MW application process and minimise costs.

Published
2024
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11. Rheological investigation on the ageing performance of bio-recycled asphalt binders and mixtures

Author

Ana Jiménez del Barco Carrión, Davide Lo Presti, Emmanuel Chailleux, Gordon D. Airey, Jimenez del Barco Carrion A., Lo Presti D., Chailleux E., and Airey G.D.

Subjects
Settore ICAR/04 - Strade, Ferrovie Ed Aeroporti, ageing, Asphalt, biomaterials, recycling, sustainability, Civil and Structural Engineering
Abstract

The current need to move towards more sustainable technologies in the construction sector has promoted the investigation of using alternative materials in asphalt mixtures for pavements. Biomaterials, used as biobinders, have shown their potential as partial replacement of bitumen in asphalt mixtures as solution to decreasing the demand for fossil-fuel-based binders as well as CO2 emissions. However, more research is needed to increase the replacement of bitumen in asphalt mixtures. In this investigation,biobinders are used as full replacement of virgin bitumen and as recycling agent within asphalt mixtures with high Reclaimed Asphalt (RA) content. Blends of biobinders and RA binders, and bio-recycled asphalt mixtures, were produced, subjected to ageing and rheologically characterised. The results show that the rheological properties of binders’ blends seem adequate for their use, while the bio-recycled asphalt mixtures seem to have a faster ageing than conventional ones, hence their full-scale application still remains a concern.

Published
2023
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16. Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

Author

Ramon Botella, Davide Lo Presti, Kamilla Vasconcelos, Kinga Bernatowicz, Adriana H. Martínez, Rodrigo Miró, Luciano Specht, Edith Arámbula Mercado, Gustavo Menegusso Pires, Emiliano Pasquini, Chibuike Ogbo, Francesco Preti, Marco Pasetto, Ana Jiménez del Barco Carrión, Antonio Roberto, Marko Orešković, Kranthi K. Kuna, Gurunath Guduru, Amy Epps Martin, Alan Carter, Gaspare Giancontieri, Ahmed Abed, Eshan Dave, Gabrielle Tebaldi, Institut Català de la Salut, [Botella R, Martínez AH, Miró R] Universitat Politècnica de Catalunya – BarcelonaTech, Barcelona, Spain. [Lo Presti D] University of Palermo, Palermo, Italy. [Vasconcelos K] University of Sao Paulo – Polytechnic School, Sao Paulo, Brazil, [Bernatowicz K] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain, Vall d'Hebron Barcelona Hospital Campus, Ramon Botella, Davide Lo Presti, Kamilla Vasconcelo, Kinga Bernatowicz, Adriana H. Mart??nez, Rodrigo Mir??, Luciano Specht, Edith Ar??mbula Mercado, Gustavo Menegusso Pire, Emiliano Pasquini, Chibuike Ogbo, Francesco Preti, Marco Pasetto, Ana Jim??nez del Barco Carri??n, Antonio Roberto, Marko Ore??kovi??, Kranthi K. Kuna, Gurunath Guduru, Amy Epps Martin, Alan Carter, Gaspare Giancontieri, Ahmed Abed, Eshan Dave, Gabrielle Tebaldi, Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, and Universitat Politècnica de Catalunya. MATCAR - Materials de Construcció i Carreteres

Subjects
Intel·ligència artificial - Aplicacions a la medicina, Artificial neural networks, Natural Science Disciplines::Mathematics::Data Analysis [DISCIPLINES AND OCCUPATIONS], disciplinas de las ciencias naturales::matemáticas::análisis de datos [DISCIPLINAS Y OCUPACIONES], Asphalt pavements, Indirect tensile strength, Building and Construction, Hot mix asphalt, Reclaimed asphalt pavement, Mechanics of Materials, Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning [PHENOMENA AND PROCESSES], Machine learning, Aprenentatge automàtic, Degree of binder activity, conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático [FENÓMENOS Y PROCESOS], Asfalt, Settore ICAR/04 - Strade, Ferrovie Ed Aeroporti, Recycling, General Materials Science, Enginyeria civil::Infraestructures i modelització dels transports::Transport per carretera [Àrees temàtiques de la UPC], Hot mix asphalt, Recycling, Reclaimed asphalt pavement, Degree of binder activity, Machine learning, Artificial neural networks, Random forest, Indirect tensile strength, Random forest, Civil and Structural Engineering
Abstract

Part of this research was funded by the project RTI2018-096224-J-I00 that has been cofounded by the Spanish Ministry of Science and Innovation, inside the National Program for Fostering Excellence in Scientific and Technical Research, National Subprogram of Knowledge Generation, 2018 call, in the framework of the Spanish National Plan for Scientific and Technical Research and Innovation 2017-2020, and by the European Union, through the European Regional Development Fund, with the main objective of Promoting technological development, innovation and quality research. Part of this work was financially supported by the Italian Ministry of University and Research with the research Grant PRIN 2017 USR342 Urban Safety, Sustainability and Resilience., This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up, Spanish Government RTI2018-096224-J-I00, European Commission, Ministry of Education, Universities and Research (MIUR) PRIN 2017 USR342

Published
2022

17. Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

Author

Botella, Ramon, Lo Presti, Davide, Vasconcelos, Kamilla, Bernatowicz, Kinga, Martínez, Adriana, Miró, Rodrigo, Specht, Luciano, Arámbula Mercado, Edith, Menegusso Pires, Gustavo, Pasquini, Emiliano, Ogbo, Chibuike, Preti, Francesco, Pasetto, Marco, Jiménez del Barco Carrión, Ana, Roberto, Antonio, Orešković, Marko, Kuna, Kranthi, Guduru, Gurunath, Epps Martin, Amy, Carter, Alan, Giancontieri, Gaspare, Abed, Ahmed, Dave, Eshan, Tebaldi, Gabriele, Botella, Ramon, Lo Presti, Davide, Vasconcelos, Kamilla, Bernatowicz, Kinga, Martínez, Adriana, Miró, Rodrigo, Specht, Luciano, Arámbula Mercado, Edith, Menegusso Pires, Gustavo, Pasquini, Emiliano, Ogbo, Chibuike, Preti, Francesco, Pasetto, Marco, Jiménez del Barco Carrión, Ana, Roberto, Antonio, Orešković, Marko, Kuna, Kranthi, Guduru, Gurunath, Epps Martin, Amy, Carter, Alan, Giancontieri, Gaspare, Abed, Ahmed, Dave, Eshan, and Tebaldi, Gabriele

Abstract

This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up.

Published
2022

18. Alcohol Effects on Drivers' Speed Management: The Influence of Visual Performance and Road Complexity.

Author

Casares-López, Miriam, Juan Castro-Torres, José, Ortiz-Peregrina, Sonia, Martino, Francesco, Granados-Delgado, Pilar, and Jiménez del Barco, Luis

Abstract

Objective: The aim of this work is to assess how drivers adapt their driving speed as a self-regulation mechanism when driving under the influence of alcohol, and the influence of alcohol consumption, visual performance, road complexity, and personal traits. Method: Thirty-one volunteers took part in the study. All of them underwent three experimental sessions: baseline (no alcohol), Alcohol 1 (low-moderate dose), and Alcohol 2 (moderate-high dose). Vision was tested using contrast sensitivity and retinal straylight. Driving performance was assessed using a driving simulator. The difference between the driving speed and the speed limit was calculated in 10 road scenarios of different complexity. Results: Drivers adapted their driving speed less (i.e., drove faster) in the Alcohol 1 condition compared with Alcohol 2 (p = .007). This indicates that participants felt more confident under the influence of a low-moderate dose of alcohol. Participants with better contrast sensitivity drove faster (p = .021). The complexity of the road and other factors such as driving experience, sex, or driving under the influence of alcohol frequency also influenced speed choice. Conclusions: Drivers under the influence of a low-moderate dose of alcohol seem to be less aware of the risk. Contrast sensitivity is a good predictor of the speed choice when driving under the influence of alcohol. A better understanding of drivers' behavior under the influence of substance use may be useful to adjust and improve traffic laws and driving regulations. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Review on Biomass Materials for Pavements

Author

He, Liang, Tao, Mengzhe, Liu, Zhuang, Cao, Zhi, Gao, Jie, Van den bergh, Wim, Vasconcelos, Kamilla, editor, Jiménez del Barco Carrión, Ana, editor, Chailleux, Emmanuel, editor, and Lo Presti, Davide, editor

Published
2024
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31. Comparative Analysis of the Rheological Properties of Asphalt Binders Modified by Vegetable Oils from Brazil

Author

de Medeiros Melo Neto, Osires, de Figueiredo Lopes Lucena, Leda Christiane, Silva, Ingridy Minervina, Diniz, Maria Ingridy Lacerda, de Figueiredo Lopes Lucena, Adriano Elísio, de Figueiredo Lopes Lucena, Luciana, Vasconcelos, Kamilla, editor, Jiménez del Barco Carrión, Ana, editor, Chailleux, Emmanuel, editor, and Lo Presti, Davide, editor

Published
2024
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32. Bio-binder Derived from Vegetable Oil and Resin

Author

Leite, Leni Figueiredo Mathias, do Nascimento, Luis Alberto Hermann, Fritzen, Marcos Antonio, Cravo, Margareth Carvalho, Vasconcelos, Kamilla, editor, Jiménez del Barco Carrión, Ana, editor, Chailleux, Emmanuel, editor, and Lo Presti, Davide, editor

Published
2024
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34. Analysis of Stiffness and Moisture Resistance of Asphalt Mixtures with Biomaterials: A Case Study in Brazil

Author

de Medeiros Melo Neto, Osires, de Figueiredo Lopes Lucena, Leda Christiane, Silva, Talita Miranda, Silva, Ingridy Minervina, de Figueiredo Lopes Lucena, Adriano Elísio, de Figueiredo Lopes Lucena, Luciana, Vasconcelos, Kamilla, editor, Jiménez del Barco Carrión, Ana, editor, Chailleux, Emmanuel, editor, and Lo Presti, Davide, editor

Published
2024
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46. Production and Yield of Lignin as a Biomaterial for Paving

Author

da Silva Vieira, Jéssica, Specht, Luciano Pivoto, Brondani, Chaveli, Formentini, Luiz Felipe, Amaral, Ana Victoria, de Carvalho, Leandro Machado, Vasconcelos, Kamilla, editor, Jiménez del Barco Carrión, Ana, editor, Chailleux, Emmanuel, editor, and Lo Presti, Davide, editor

Published
2024
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