Examinando por Autor "Ruiz, Gonzalo"

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Comparative analysis of flexural strength prediction in SFRC using frequentist, Bayesian, and Machine Learning approaches
(Elsevier, 2024-10-11) Rosa, Ángel de la; Sáinz-Aja, José; Rivas, Isaac; Ruiz, Gonzalo; Ferreño, Diego
Steel fiber reinforcement significantly enhances the flexural strength of concrete, which is vital for structural integrity. Annex L of the new Eurocode 2 classifies steel fiber-reinforced concrete by its flexural performance, aiding engineers in designing resilient structures. This study investigates the flexural behavior of steel fiber-reinforced concrete (SFRC) using three data-driven methodologies: Frequentist Inference (FI), Bayesian Inference (BI), and Machine Learning (ML). A comprehensive database was constructed from three-point bending tests on SFRC specimens, encompassing various compressive strengths, fiber quantities, and geometric parameters, to identify key factors influencing material properties. The findings indicate that all three methodologies yield comparable predictive capabilities for flexural responses in SFRC. Notably, FI models emphasize the importance of compressive strength and fiber volume fraction, along with fiber properties such as non-dimensional length and tensile strength. BI models enhance predictive stability by integrating prior knowledge and quantifying uncertainty, demonstrating their advantage, particularly in data-scarce situations. Additionally, ML analysis reveals that linear regression (LR) models can achieve accuracy similar to or greater than that of more complex models. This research provides novel insights into the application of BI and ML in concrete technology, emphasizing their potential to enhance predictive modeling. Additionally, it offers practical guidelines for optimizing SFRC design through a case study that compares residual flexural strengths obtained via Bayesian analysis, classifying the material in accordance with Annex L of the new Eurocode 2
Effect of cellulose nanofibers on the rheological and mechanical properties of cement pastes with metakaolin and natural volcanic pozzolan
(Elsevier, 2024-11-22) Rosa, Ángel de la; Ruiz, Gonzalo; Husillos-Rodríguez, Nuria; Moreno, Rodrigo
This study investigates the influence of cellulose nanofibers (CNF) on the rheological and mechanical properties of Portland cement pastes with pozzolanic additions, focusing on compressive ductility. Unlike prior research that examines individual factors, this comprehensive analysis evaluates various parameters affecting CNF-enhanced pastes. Using commercially available CNF and two pozzolanic additions—metakaolin (MK) and natural volcanic pozzolan (NVP)—the study assesses mechanical and ultrasonic methods for CNF dispersion. CNF is added at 0%, 0.05%, and 0.5% by weight of cement (CEM). Results show distinct differences in flow curves and thixotropy, both with and without ultrasound. Compressive strength tests at 28 days reveal that 0.05% CNF is optimal for CEM ＋ MK pastes without ultrasound, outperforming Portland cement pastes with NVP. However, 0.5% CNF significantly improves compressive ductility in CEM and CEM + NVP pastes, despite reduced compressive strength. This study offers valuable insights into optimizing mechanical properties of CNF-modified pastes, enhancing the efficacy and sustainability of construction materials