In foamed concrete, porosity is essential because it is strongly related to other properties such as density, permeability, and strength. Porosity measurement (usually expressed as a percentage of total volume) is obtained in a laboratory using experimental water vacuum saturation and MIP methods. However, pore structure -including size, distribution, shape, and connection- is also needed to understand foamed concrete performance. Pore structure characterization is estimated through specialized digital image analysis. Micro CT, scanning electron microscopy or X-ray tomography images are frequently used to obtain pore structure on cellular concrete. However, these images are highly specialized and require equipment that is not easy to find and very expensive. Also, image processing is complex, and it includes some specialized software. This paper presents a pore structure characterization and porosity estimation using non-specialized digital images on foamed cement paste made with alternative agents. The procedure for acquiring images uses only a camera without any specialized equipment. The proposed methodology isolates the pores in the image and measures shape features such as pore diameter, eccentricity, and solidity. Acquiring and processing the images is simpler, faster, and cheaper than other specialized analyses. Results show that the volumetric porosity estimation was entirely accurate, with an estimation deviation of less than 10%. Also, the pore structure parameters such as pore size and distribution of foamed pastes can be quantified accurately.

Los residuos de demolición y construcción (RCD), se han convertido en una problemática constante en el sector, debido a su volumen de producción y la falta de espacio para su disposición final. Para solucionar esta problemática se han propuesto diversas estrategias de gestión que consideran desde la disminución en la fuente hasta la reutilización como materia prima en la fabricación de diversos elementos. En este último aspecto, a nivel mundial, se han llevado a cabo esfuerzos por determinar el posible reúso de dichos residuos. Sin embargo, en muchos casos ese proceso parece ser inconsistente pues no se considera la naturaleza y propiedades del residuo como un condicional en la identificación de su posible reutilización. En este artículo, se presenta un ejemplo de aplicación de la identificación del potencial de reúso de residuos de demolición y construcción de la ciudad de Medellín, a partir de la determinación de las propiedades físicas, mineralógicas y energéticas, y del inventario específico de producción. Los resultados mostraron que, para el caso de estudio, los residuos con mayor potencial de uso para la reincorporación en la cadena productiva fueron los residuos de excavación y de mampostería de bloque, con los cuales, además, se probó un modelo de reutilización consistente en una placa suelo-cemento conformada por 95% de RCD.

The largest proportion of solid waste comes from the construction industry. Construction and demolition waste (CDW) include among others, petrous waste like those derived from concretes and masonry, ceramic rips, and excavation products, being the latter, the ones with the largest generation percentage. Petrous waste reuse is well studied in literature, however, the reincorporation of excavated soils with poorly geotechnical features represents a major challenge than traditional type of reuse. In this paper, experimental results are shown for the design of compacted blends composed of excavated soils, CDW and cement, which are selected as the main supply of prefabricated hydraulic elements. Technical feasibility was established from Proctor test and unconfined compressive strength results, both as a criterion for workability and maximum densification and use of waste. A life cycle assessment (LCA) was performed for the compacted blends with the best technical feasibility results to evaluate its environmental impact. The results showed that compacted blends with at least 90% of waste meet the technical criteria for being reused.

Modeling for design and simulation has gained significance in both academic and industrial domains, primarily due to the availability of specialized software. In the domain of modeling mineral beneficiation crushers, the focus is on controlling variables related to equipment mechanics and material characteristics. In this study, the comminution process of a conical crusher in a stone aggregates production plant was specifically modeled using artificial neural networks. A Feedforward artificial neural network topology was employed to simulate the comminution process. Different network configurations were tested through 50 laboratory experiments, where size data were collected at the crusher’s entrance and exit. The aim was to select the configuration that best matched the actual data by optimizing mean square error, error dispersion, and linear fit. After thorough evaluation, the network configuration with hidden layers of 70 and 40 neurons emerged as the most effective. This configuration yielded a mean square error of 6.24, an error dispersion of 35.05, and a robust linear fit of 0.99. Subsequently, this model was applied to new size distribution data from the crusher’s entrance and exit, resulting in an R parameter of 0.999. The study highlights the effectiveness of the trained artificial neural network model in accurately predicting particle size distribution at the exit of a conical crusher. This has significant implications for optimizing the beneficiation process in stone aggregate production.