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. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R² of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model’s superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.

The present study explored the extraction of cellulose from forest residues of four timber species, namely Cedrela montana Moritz ex Turcz, Buddleja incana Ruiz & Pav, Vallea stipularis L. f. and Myrsine andina (Mez) Pipoly, in the high montane forest of Chimborazo province, Ecuador, for the sustainable utilization of leaves, branches, and flowers. An alkaline extraction method was used on the residues without the need for prior degreasing. An ANOVA analysis was applied to evaluate significant differences in cellulose extraction yields among the species’ residues. The characterization techniques used were Fourier transform infrared spectroscopy (FTIR) and polarized light optical microscopy, which confirmed the successful extraction of cellulose with characteristics comparable to standard cotton cellulose and other traditional species. The results showed significant variations in cellulose yield among the species, with Vallea stipularis L. f achieving the highest yield of 80.83%. The crystallinity of the samples was clearly evidenced by the polarity of the light in the samples during microscopy, demonstrating that the residues can be a viable and sustainable source of cellulose, contributing to a circular economy and reducing the environmental impact of forest waste.

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. These data entries represented ten (10) components of the steel fiber reinforced concrete such as C, W, FAg, CAg, PL, SF, FA, Vf, FbL, and FbD, which were applied as the input variables in the model and Cs, which was the target. Advanced machine learning techniques were applied to model the compressive strength (Cs) of the steel fiber reinforced concrete such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)”. All models were created using 2024 “Weka Data Mining” software version 3.8.6. Also, accuracies of developed models were evaluated by comparing sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), Error (%), Accuracy (%) and coefficient of determination (R²), correlation coefficient (R), willmott index (WI), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and symmetric mean absolute percentage error (SMAPE) between predicted and calculated values of the output. At the end, machine learning has been found to be a transformative approach that enhances the efficiency, cost-effectiveness, and sustainability of evaluating compressive strength in industrial wastes-based concrete reinforced with steel fiber. Among the models reviewed, Kstar and DT emerge as the most practical for achieving precise and sustainable results. Their adoption can significantly reduce environmental impacts and promote the sustainable use of industrial by-products in construction. The sensitivity of the input variables on the compressive strength of industrial wastes-based concrete reinforced with steel fiber produced 36% from C, 71% from W, 70% from FAg, 60% from CAg, 34% from PL, 5% from SF, 33% from FA, 67% from Vf, 5% from FbL, and 61% from 61%. Fiber Volume Fraction (Vf) (67%) high sensitivity suggests that steel fiber content greatly impacts crack resistance and tensile strength. Steel Fiber Orientation (61%) indicates the importance of fiber alignment in distributing stresses and enhancing structural integrity.

Cacao is one of the most important crops for Ecuador's economy, particularly in the Amazon region, where its production generates employment and economic development. However, this growth also presents significant environmental challenges, primarily related to the inadequate management of organic waste such as cacao husks and shells. This study analyzed the potential of these by-products within circular economy systems to promote sustainable waste management in cacao plantations, using Loreto Canton in Orellana Province as a case study. The primary objective was to optimize the management of organic waste through valorization strategies that reduce environmental impacts and enhance the social and economic sustainability of local producers. The methodological approach included a Life Cycle Assessment (LCA) supported by GaBi software and the TRACI 2.1 methodology, alongside the implementation of a Waste Management Plan with specific indicators for efficiency, waste minimization, recycling, social and economic sustainability, and community education.The results demonstrated a significant reduction in resource consumption, such as water, fertilizers, and chemical inputs (5.13%), and a 200% decrease in the generation of organic, plastic, and hazardous waste, which were valorized through composting, recycling, and the production of industrial goods. These activities generated a 200% increase in income from the sale of by-products, such as husks and shells, and facilitated the acquisition of national and international sustainability certifications.

This research aimed to elucidate the implications of particle size on cellulose extraction from the species Calamagrostis intermedia, endemic to Ecuador. Within the versatile cellulose extraction and trade industry, using timber sources results in a relentless environmental impact, mainly when no resource rationing occurs during the washing or bleaching phases. The investigation incorporated a sample pretreatment encompassing a drying, grinding, and sieving study, followed by an alkaline extraction. The study found that particle size significantly affected the quantity of fiber and cellulose pulp extracted from samples previously dried at 65°C with particle sizes of 300 and 106 μm. Characterization tests revealed variations in the color and texture of the extracted cellulose, attributable to an ineffective delignification stage within the fiber bleaching process. Variations in molecular weight corresponding to 126.32 x 103 g/mol were identified among samples, and FTIR analysis presented a syringyl ring and lignin stretching. Optical microscopy exposed the absence of well-defined regions in the fibers and a readily observable crystalline network. In conclusion, particle size critically influences cellulose extraction from Calamagrostis intermedia, impacting the yield and properties of the resultant product, indicating that further optimization of the extraction and delignification process is required to enhance the quality of the extracted cellulose. Keywords: Calamagrostis intermedia; Particle Size; Cellulose Extraction

El cacao es uno de los cultivos más importantes en Ecuador, especialmente en la Amazonía, sin embargo, su producción genera diversos impactos ambientales y sociales, principalmente relacionados con la gestión inadecuada de residuos. Este estudio analizó el impacto ambiental de las plantaciones de cacao mediante una evaluación del ciclo de vida (ACV) con un enfoque en la economía circular, tomando como caso de estudio el cantón Loreto, provincia de Orellana. El análisis se realizó utilizando la herramienta GaBi, aplicando el método TRACI 2.1 para identificar los principales impactos ambientales desde el transporte de plantas hasta la venta del producto final. Los resultados revelaron que las emisiones de CO₂ (228 kg), SO₂ (0.00462 kg) y N (0.00128 kg) están principalmente asociadas al uso de diesel en el transporte. Se identificaron impactos significativos sobre el agua, el aire y el suelo durante las etapas de riego, abono y fertilización, así como pérdida de biodiversidad causada por el uso intensivo de insumos químicos. En conclusión, la revalorización de subproductos del cacao bajo un enfoque de economía circular, junto con la implementación de estrategias sostenibles en la gestión de insumos, puede mitigar los impactos negativos y promover una producción más sostenible.