Dr. Maureira-Carsalade, Nelson

This study evaluates bentonite clay (BNC) as a sustainable supplementary cementitious material for enhancing the structural performance of rigid pavement systems, with a dual focus on durability and eco-efficiency. Using Response Surface Methodology (RSM), the effects of varying BNC content on concrete properties were systematically analyzed. Results indicate that increasing BNC reduces workability, with slump values declining from 10.21 to 62.55 % due to its high-water absorption and decreases density (2355 kg/m³ for control vs. 2293 kg/m³ for 20 % BNC) owing to its lower specific gravity. While early-age strength diminishes at higher BNC levels, an optimal replacement of 12–16 % enhances long-term compressive strength via pozzolanic reactions, achieving 37.55 MPa at 91 days for the 16 % BNC mix. Flexural strength improvements are attributed to BNC’s crack and shrinkage mitigation. However, excessive BNC content (>16 %), compromises durability, evidenced by reduced ultrasonic pulse velocity (UPV) and increased porosity. BNC enhances sulfate resistance and thermal stability, demonstrating suitability for hot climates. Cement substitution with BNC reduces the carbon footprint by 31.91 %, aligning with sustainability goals. RSM-derived empirical models exhibit strong predictive accuracy (F-values: 67.07 for compressive strength, 36.92 for flexural strength; non-significant lack-of-fit, p > 0.04). The optimized mix (16 % BNC, 82-day curing) balances strength, durability, and environmental benefits. This work advances sustainable pavement design, addressing performance trade-offs and promoting low-carbon construction practices.

The objective of this study was based on the application of the response surface methodology (RSM) for yield optimization in maize (Zea mays L.). The hybrid INIA SQ-1 was used, and the Response Surface Methodology was used using the Box-Behnken design (DBB), with which the following factors were evaluated: plant density, nitrogen (N) dose and phosphorus (P) dose at three levels each; for the optimization of the response variables: “yield” (kg.ha-1) and the “number of grains per square meter” (g.m2). The response surface method provided a statistically validated predictive model, which through adjustments was adapted to an established optimization process. For the variable “yield”, a maximum response was found with the application of 150 Kg.ha-1 of N and 90 kg.ha-1 of P. In relation to the number of grains per square meter (g.m2), the optimum was obtained using 75,000 plants.ha-1 and an applied dose of 150 kg.ha-1.