Pinto Almeida, Carlos
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Item type:Publication, Lightweight deep learning for sustainable adobe construction: a block-level baseline aligned with circular economy principles and sustainable development goalsIntroduction – The construction sector accounts for nearly 37% of global energy-related CO2 emissions, motivating renewed interest in low-carbon vernacular materials such as stabilized adobe. The integration of recycled inputs into adobe formulations advances circular-economy principles in the built environment, yet large-scale adoption is constrained by the absence of objective, scalable quality-control methods. Methods – We present a methodologically rigorous baseline for AI-driven visual quality control in stabilized adobe construction. A dataset of 330 images was constructed from 55 physical adobe blocks fabricated under controlled laboratory conditions with three circular-economy stabilization mixtures: recycled polyethylene terephthalate (1.5%), sugarcane bagasse fiber (8%), and Portland cement (10%). Each block was photographed from six standardized angles. To eliminate data leakage, we implemented strict block-level data splitting. A MobileNetV2 transfer learning pipeline with frozen ImageNet weights, aggressive data augmentation, L2 regularization, and balanced class weights was developed. Performance was characterized through accuracy with bootstrap 95% confidence intervals, per-class metrics, ROC AUC and Average Precision, five-fold block-level cross-validation, ablation study, and Grad-CAM interpretability. Results – The model achieved 80.95% test accuracy (95% CI: 69.0%–92.9%) on a held-out set of seven physical blocks (42 images). AUC = 0.858 and Average Precision = 0.944. Per-class F1-scores were 0.692 (sound) and 0.862 (defective). Block-level five-fold cross-validation confirmed stability at 70.0% (95% CI: 60.7%–79.2%). Ablation showed: simple CNN 71.43%, MobileNetV2 without augmentation 71.43%, complete pipeline 80.95% (+9.5 pp). Grad-CAM confirmed attention to physically meaningful surface features. The 9.24 MB model footprint enables offline deployment on commodity smartphones. Conclusion – This work establishes a methodologically rigorous baseline for AI-driven quality control in stabilized adobe construction. By demonstrating meaningful performance with modest datasets and edge-deployable architectures, this study supports broader adoption of circular-economy adobe construction and contributes to Sustainable Development Goals 9, 11, 12, and 13. Future work should expand the dataset, evaluate hierarchical classification, and assess field deployment. Copyright © 2026 Pinto Almeida and Jordán Buenaño. Conclusion This work establishes a methodologically rigorous baseline for AI-driven quality control in stabilized adobe construction. By demonstrating meaningful performance with modest datasets and edge-deployable architectures, this study supports broader adoption of circular-economy adobe construction and contributes to Sustainable Development Goals 9, 11, 12, and 13. Future work should expand the dataset, evaluate hierarchical classification, and assess field deployment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effect of replacing mixing water with milk on the compressive strength of concrete at different curing ages(Frontiers Media SA, 2026-07-17); ; Zurita, MarcoIntroduction – The chemical composition of mixing water directly affects cement hydration and the strength development of concrete. Although alternative mixing liquids—such as treated wastewater, leachates, and wash water—have shown moderate strength reductions compatible with structural use, the behavior of concrete exposed to complex organic liquids remains largely uncharacterized. Methods – This study investigates how replacing mixing water with whole bovine milk, a medium rich in lactose, casein, and fat, affects cement hydration and compressive strength in conventional Portland concrete. Three mixtures with increasing replacement levels were produced: 0% (M0, control), 50% (M50), and 100% (M100). Twenty-seven cylindrical specimens (3 mixtures × 3 ages × 3 replicates) were tested under axial compression following ASTM C39/C39M at 15, 28, and 45 days. Peak strength, stress–strain curves, within-condition dispersion, and failure modes were recorded. Results – The control developed strengths of 14.39 ± 0.64, 16.47 ± 0.73, and 20.91 ± 0.81 MPa at 15, 28, and 45 days, whereas M50 and M100 remained below 1 MPa at all ages (M50: 0.61 ± 0.03, 0.67 ± 0.03, 0.90 ± 0.06 MPa; M100: 0.38 ± 0.02, 0.55 ± 0.02, 0.60 ± 0.03 MPa), a strength loss greater than 95% relative to the control. Coefficients of variation ranged from 3.9% to 6.1%, below the 10% threshold of ACI 214R. The modified mixtures also lost the ascending branch of the stress–strain curves and exhibited diffuse failure modes, without the longitudinal cracking typical of sound concrete. Conclusion – The magnitude of the strength loss (>95%) places milk in a category distinct from previously reported alternative mixing liquids. Rather than producing a degraded variant of conventional concrete, it generates a system in which the cementitious reaction is practically inhibited. This behavior, attributable to the combined action of lactose, casein, and fats on C3S hydration, redefines the operational limits of chemical compatibility between organic fluids and cementitious systems and provides a reference calibration point for predictive models of organic inhibition. Copyright © 2026 Pinto, Zambrano, Apraez, Gómez, Zurita and Buele. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mechanical Performance of Volcanic Ash Concrete Showing Modulus Reduction with Strength Retention(2025); ; Daniel Abril-CaminoThis study aims to evaluate the mechanical behavior of concrete that incorporates 51.3% raw volcanic ash into its structure, focusing on its static elasticity modulus and compressive strength. Cylindrical concrete samples were prepared via the mix design commonly used in practice in Baños, Tungurahua, Ecuador. Three curing methods were applied: immersion, water spraying, and no curing. Compressive strength tests were conducted at 3, 7, 14, 21, and 28 days, whereas the static modulus was measured at 28 days following ASTM C469. Despite the high use of ash in the mixture, the mixtures achieved adequate compressive strengths for structural applications, reaching 28.05 MPa. However, a significant reduction in the static modulus was observed, with experimental values of approximately 7.06 GPa, whereas the value of 24.89 GPa was predicted by the equations given in ACI318. The use of raw volcanic ash in structural mixes requires modifications to deformation and stiffness calculations to ensure seismic performance, suggesting the need to review local regulations on traditional mixes. Based on the experimental data, an alternative empirical model, the VAM model, was proposed to better predict the elastic modulus of concrete with high volcanic ash content. The findings reveal a dual function of ash, acting as a pozzolanic material and as a low-density aggregate, highlighting the need to adjust the design equations when raw volcanic ash is used. This work contributes to the sustainable design of concrete mixtures in seismic regions.10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Twins in Higher Education: Applications, Challenges, and Future Perspectives(2026)The Fourth Industrial Revolution has introduced emerging technologies that are reshaping educational models, among them digital twins, originally developed in the aerospace industry as virtual representations of real systems. Their integration into higher education now enables the simulation of complex learning environments, monitoring of student performance, and personalization of learning processes. However, their adoption within university settings remains incipient, fragmented, and lacking in standardization, which limits their transformative potential. This situation underscores the need to systematize dispersed knowledge on their educational use. This study presents a review with a descriptive and exploratory approach, based on peer-reviewed scientific articles selected from databases such as Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science, published between 2018 and 2024. The findings indicate that digital twins are primarily applied for simulations (68%), learning monitoring (52%), and educational personalization (44%). Reported outcomes highlight improvements not only in learner autonomy, motivation, and acquisition of practical skills, but also in conceptual understanding, problem-solving abilities, and collaboration among students in project-based environments. At the institutional level, benefits include greater efficiency in resource management, reduction of physical laboratory costs, and continuity of teaching in hybrid or remote learning contexts. Nevertheless, challenges remain, including limited connectivity, high implementation costs, insufficient teacher training, and ethical risks. Digital twins represent a strategic technology for transforming higher education into more flexible, personalized, and sustainable models, provided they are supported by inclusive policies and adequate investment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.3