0000000000644433

AUTHOR

Alan Carter

0000-0003-3915-8385

showing 3 related works from this author

Experimental Investigation on Water Loss and Stiffness of CBTM Using Different RA Sources

2021

Cold recycling of reclaimed asphalt (RA) is a promising technique to build or to maintain roads, combining performance and environmental advantages. Although this technique has been extensively used worldwide, there is no unique and internationally-shared method to characterize cold recycled mixtures. The previous work of the RILEM TC 237-SIB TG6 successfully attempted to characterize different RA sources with both traditional parameters (gradation, bitumen content and geometrical properties) and non-conventional properties (fragmentation and strength testing). The current RILEM TC 264-RAP TG1 mainly focuses on the influence of different RA sources on physical and mechanical characteristics…

CementCold recyclingCuring (food preservation)Materials scienceCompactionStiffnessWater lossFoamed bitumenAsphaltUltimate tensile strengthmedicineStrength testingGradationStrengthmedicine.symptomComposite materialEngineering sciences. TechnologyReclaimed asphalt
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RILEM TC 279 WMR round robin study on waste polyethylene modified bituminous binders: advantages and challenges

2022

inter-laboratory experiments were designed to evaluate the impact of plas- tic waste blended directly in bitumen and to assess the properties, using conventional and advanced bituminous binder testing. The blends tar- geted 5% of plastic waste in 95% bitumen, using two types of polyethy- lene (PE) primary (pellets) and secondary (shreds) waste. The experiments showed that the addition of PE waste to bitumen does not alter the chem- istry of the bitumen, the blending is physical. The DSR results indicate a strong dependency on the testing temperature as at low temperatures the composite material bitumen and PE behave both elastically whereas, at higher temperatures, the bitumen becomes visco…

polyethyleneAsphalt; performance; plastics; polyethylene; wasteplasticAsphalt waste polyethylene plastics performanceSettore ICAR/04 - Strade Ferrovie Ed AeroportiAsphaltwasteplasticsperformanceCivil and Structural Engineering
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Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement

2022

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 20…

Intel·ligència artificial - Aplicacions a la medicinaArtificial neural networks:Natural Science Disciplines::Mathematics::Data Analysis [DISCIPLINES AND OCCUPATIONS]:disciplinas de las ciencias naturales::matemáticas::análisis de datos [DISCIPLINAS Y OCUPACIONES]Asphalt pavementsIndirect tensile strengthBuilding and ConstructionHot mix asphaltReclaimed asphalt pavementMechanics of Materials:Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning [PHENOMENA AND PROCESSES]Machine learningAprenentatge automàticDegree of binder activity:conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático [FENÓMENOS Y PROCESOS]AsfaltSettore ICAR/04 - Strade Ferrovie Ed AeroportiRecyclingGeneral 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 strengthRandom forestCivil and Structural Engineering
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