RT info:eu-repo/semantics/conferenceObject
T1 An overview of sustainable concretes with maximized aggregate content: natural limestone versus steel making slags
A1 García Cortés, Verónica
A1 García Sánchez, David
A1 Revilla Cuesta, Víctor
A1 Romera, Jesús María
A1 San José Lombera, José Tomás
K1 Compressible packing model (CPM)
K1 3-Parameter packing desnsity model (3-PM)
K1 Electric Arc Furnace Slag
K1 Natural (limestone) Aggregate (NA)
K1 Concrete design
K1 Materiales de construcción
K1 Building materials
K1 Hormigón-Ensayos
K1 Concrete-Testing
AB The conversion of various industrial by-products from Spanish factories into co-products used in partial substitution of cement and concrete aggregate has been extensively studied since the 1990s. Building on that research effort, the present investigation is focused on improving the packing density of concrete aggregates, with special emphasis on two central objectives: firstly, the reduction of cement and natural aggregate content within concrete; secondly, the validation of their substitution by Electric Arc Furnace Slag (black-slag) aggregate. To do so, several experimental campaigns were conducted, in which 4 compaction procedures were applied under dry conditions to: 4 sieved fractions of natural limestone and 3 sieved fractions of black-slag aggregates. The physical properties of the 7 sieved fractions had previously been characterized and compared with theoretical models, in order to validate their dosing in the experimental tests: Fuller curve, Funk and Dinger curve, Compressible Packing Model, and the 3-Parameter Packing model. The aggregate-packing densities were experimentally and theoretically studied with dry methods. Our findings showed that, unlike natural aggregates, other methods based on aggregate shape are preferable for black-slag mixtures, due to the specific textures and their abrupt particle contours. The conclusions from the investigations were that both the Compressible Packing Model and the 3-Parameter Packing models produced valuable packing-density predictions for the binary mixes.
PB Universidad de Cantabria
SN 978-84-09-42253-1
SN 2386-8198
YR 2022
FD 2022
LK http://hdl.handle.net/10259/10248
UL http://hdl.handle.net/10259/10248
LA eng
NO Trabajo presentado en: 9th Euro-American Congress on Construction Pathology, Rehabilitation Technology and Heritage Management, REHABEND 2022. En Granada, durante los días 13-16 de septiembre de 2022
NO This research work was supported by the Spanish Ministry of Universities, MICINN, AEI, EU, ERDF and NextGenerationEU [grant numbers PID2020-113837RB-I00; 10.13039/501100011033; TED2021-129715B-I00]; the Junta de Castilla y León (Regional Government) and ERDF [grant number UIC-231]; and the University of Burgos [grant number SUCONS, Y135.GI].
DS Repositorio Institucional de la Universidad de Burgos
RD 03-mar-2025