RT info:eu-repo/semantics/article
T1 Multi-dimensional modeling of green self-compacting concrete with recycled aggregate through response surface methodology
A1 Revilla Cuesta, Víctor
A1 Skaf Revenga, Marta
A1 Ortega López, Vanesa
A1 Manso Villalaín, Juan Manuel
K1 Self-compacting concrete
K1 Recycled aggregate
K1 Response surface methodology
K1 Mechanical and durability performance
K1 Life cycle assessment
K1 Cost
K1 Hormigón-Ensayos
K1 Concrete-Testing
K1 Materiales de construcción
K1 Building materials
AB Recycled Aggregate (RA) incorporation to Self-Compacting Concrete (SCC) typically reduces mechanical performance and durability. The aim of this research is to model these variations as a function of RA content using advanced statistical tools such as Central Composite Design (CCD, α = 1) and Response Surface Methodology (RSM) to facilitate the optimization of RA additions. This study examines the behavior of SCC produced with 0 %–100 % recycled aggregate (RA), both coarse and fine, while maintaining 300 kg/m 3 of Portland cement. Five performance dimensions were assessed: fresh properties, compression-related mechanical properties, bending- tensile mechanical properties, durability (effective porosity and water absorption), and eco-environmental indicators (global warming potential and cost). Most resulting models with R 2 values above 0.95 were dependent on the square of the RA contents, and showed minimal interaction between coarse and fine RA. Therefore, their direction of maximum variation was approximately the vector i + j in a Cartesian coordinate system. According to models’ slopes, the greatest property variations occurred above 50 % replacement, but a higher rate for fresh and durability properties. Simultaneous optimization of all the models recommended using 35 %–45 % coarse RA and 30 %–45 % fine RA. Additionally, range optimization yielded specific RA amounts for high-performance SCC, comprising 0 %–20 % coarse RA and 20 %–40 % fine RA, and for conventional-performance SCC, which would admit 60 %–100 % coarse RA and 0 %–70 % fine RA.
PB Elsevier
SN 0301-4797
YR 2026
FD 2026-01
LK https://hdl.handle.net/10259/11160
UL https://hdl.handle.net/10259/11160
LA eng
NO This research work was supported by grant PID2023-146642OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU; grants UIC-231 and BU033P23 funded by the Junta de Castilla y León (Regional Government) and ERDF/EU; and grant SUCONS, Y135.GI funded by the University of Burgos.
DS Repositorio Institucional de la Universidad de Burgos
RD 05-may-2026