Browsing by Author "Carrasco-Astudillo, Nicolas"

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    Electrochemical analysis of carbon steel embedded in mortars with pretreated copper tailings as supplementary cementitious material
    (2024) Sepulveda-Vasquez, Carlos; Carrasco-Astudillo, Nicolas; Munoz, Lisa; Molina, Paulo; Ringuede, Armelle; Guerra, Carolina; Sancy, Mamie
    The cement industry, responsible for 8% of global greenhouse gas emissions, necessitates developing sustainable materials to replace cement partially. This investigation examined the feasibility of using copper tailings, a byproduct of mining, as alternative materials for cement within mortars and reinforced mortars (0-15 wt%). The microstructural composition of the tailings was analyzed using scanning electron microscopy and X-ray diffraction. The corrosion resistance of mortars reinforced with copper tailings was elucidated through opencircuit potential measurements and electrochemical impedance spectroscopy. The results showed that incorporating 5 and 10 wt% of sieved copper tailings improved the mechanical strength and significantly enhanced the electrochemical stability, as indicated by more noble open-circuit potential values. Specifically, the sieved tailings played a crucial role in forming a more stable oxide film, which was confirmed by higher impedance values, suggesting a reduced corrosion rate. In contrast, mortars with 5 wt% of milled tailings exhibited properties like those of the control group. This electrochemical understanding highlights the potential of processed copper tailings in mitigating the environmental impact of cement production and enhancing the durability of cementitious composites.
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    Optimizing Retaining Walls through Reinforcement Learning Approaches and Metaheuristic Techniques
    (2023) Lemus-Romani, Jose; Ossandon, Diego; Sepulveda, Rocio; Carrasco-Astudillo, Nicolas; Yepes, Victor; Garcia, Jose
    The structural design of civil works is closely tied to empirical knowledge and the design professional's experience. Based on this, adequate designs are generated in terms of strength, operability, and durability. However, such designs can be optimized to reduce conditions associated with the structure's design and execution, such as costs, CO2 emissions, and related earthworks. In this study, a new discretization technique based on reinforcement learning and transfer functions is developed. The application of metaheuristic techniques to the retaining wall problem is examined, defining two objective functions: cost and CO2 emissions. An extensive comparison is made with various metaheuristics and brute force methods, where the results show that the S-shaped transfer functions consistently yield more robust outcomes.