Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning.

The traditional evaluation of compressive strength through repeated experimental works can be resource-intensive, time-consuming, and environmentally taxing. Leveraging advanced machine learning (ML) offers a faster, cheaper, and more sustainable alternative for evaluating and optimizing concrete properties, particularly for materials incorporating industrial wastes and steel fibers. In this research work, a total of 166 records were collected and partitioned into training set (130 records = 80%) and validation set (36 records = 20%) in line with the requirements of data partitioning and sorting for optimal model performance.

Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning.

Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide.

Utilization of Forest Residues for Cellulose Extraction from Timber Species in the High Montane Forest of Chimborazo, Ecuador.

The present study explored the extraction of cellulose from forest residues of four timber species, namely Moritz ex Turcz, Ruiz & Pav, L. f. and (Mez) Pipoly, in the high montane forest of Chimborazo province, Ecuador, for the sustainable utilization of leaves, branches, and flowers.