Predicting the flexural strength of recycled aggregate concrete (RAC) is challenging because recycled constituents introduce variability and nonlinear interactions. This study develops a K-nearest neighbors (KNN) model to predict 90-day flexural strength using 366 mix designs collected from peer-reviewed studies published between 2010 and 2024. Eighteen mixture, supplementary cementitious material, and chemical-modulus variables were considered. After preprocessing and hyperparameter optimization, the model achieved R2 = 0.812 on the independent test set. SHAP analysis identified the water-to-binder ratio, natural coarse aggregate content, and maximum aggregate size as the most influential predictors. Partial dependence plots further revealed nonlinear effects and interactions among key variables. Most predictions were within ±10% of experimental values. The results show that KNN can support preliminary RAC mixture screening and provide interpretable guidance for optimizing flexural performance while incorporating recycled aggregates.
Improper management of agro-industrial by-products such as whey and coffee pulp leads to pollution and nutrient loss. Their biotransformation using Hermetia illucens larvae was evaluated under two configurations: simple bioconversion without recirculation and biofiltration with recirculation. Three diets (100% grass; 50% pulp – 50% grass; and 100% pulp) were tested with whey. Under simple bioconversion without recirculation, the 100% pulp diet achieved the highest final weight (0.19 ± 0.02 g/larvae) and bioconversion (16.69 ± 1.62%), whereas the 50% pulp – 50% grass diet reached the highest efficiency (34.20 ± 0.93%). Using the selected diet, biofiltration with recirculation increased survival (89.25 ± 2.38% vs. 78.75 ± 3.78%) without changing bioconversion (≈13%). It is concluded that balancing the fibrous fraction with readily degradable substrates improves performance and process stability, confirming the potential of Hermetia illucens to valorize these residues within a circular economy framework.
The main objective of this article is to present the minimum area (Amin) in contact with the ground for elliptical combined footings (ECFs), assuming the partially compressed area (PCA), i.e., a zone of the surface under the base in contact with the soil is compressed, and the other is not (the soil pressure is linear). Several authors present the Amin for ECFs, but the fully compressed area (FCA). The methodological development is presented through integration to determine the equations of the resultant force and the resultant moments on the X and Y axes for case I (FCA) and for case II (PCA). Six numerical studies are shown to demonstrate the advantages of using the model proposed in this article (PCA) with respect to the model proposed by other authors (FCA). The results show significant savings with case II, up to 53.11% compared to case I. Therefore, this work will be very useful for foundation designers.
The Chilean large-scale mining operations are undergoing technological and organizational transformations that require updating human capital competencies to optimize performance; in this context, the lack of clear definitions and prioritization of emerging competencies hinders strategic planning and the development of educational programs. This study aimed to identify and prioritize these competencies using the Delphi methodology within a mixed-methods approach, combining qualitative and quantitative analyses; two rounds of consultation were conducted with a panel of twelve experts through structured surveys. A total of 25 key competencies were identified and grouped into five dimensions: technological, digital, socio-emotional, environmental, and ethical; among these, integrity, responsibility, and honesty stand out, while cybersecurity emerges as the leading technological competency. Experts consistently emphasized that technological advances alone are insufficient. Their responses suggest that future mining performance will depend largely on how effectively organizations develop and integrate human competencies.
This study presents a digital twin of 5-axis milling processes for thin-walled parts to predict and prevent the occurrence of static deflection issues, surface location error due to forced vibration, and regenerative chatter. Driven by real-time machine signals or machining programs, the twin continuously simulates the milling process given an initial workpiece and any 5- axis toolpath. Thanks to the implementation of an automatic mesher and a finite element model generator, the stiffness and dynamics of the part are determined based on its current geometry. This enables the digital twin to predict and visualize in 3D the static deflection, forced vibration, and process stability, allowing optimization of the process and avoidance of these issues. Finally, the twin is validated on a 5-axis machining case of a steel blade, in which a surface location error caused by a resonance is diagnosed and eliminated.
