Jordan, Nancy

Preferred name
Jordan, Nancy
Main Affiliation
Ambato
Email
nancyjordan@uti.edu.ec
ORCID
0000-0002-1807-4839
Scopus Author ID
58019559000

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    Item type:Publication,
    Experimental analysis of ecuadorian adobe reinforced with natural fibers
    (2025)
    Carlos Pinto-Almeida
    ;
    Jordan, Nancy  
    ;
    Nahomy Anahí Armas-Robalino
    ;
    Bianca Christina Cáceres
    ;
    Juan Carlos Cajas-Corrales
    Introduction: In Latin America, the use of adobe as a building material remains highly relevant due to its low cost, local availability, and low environmental impact. Nevertheless, its limited mechanical strength and lack of standardization constrain broader implementation in contemporary architecture. Methods: This study addresses that gap by experimentally evaluating the mechanical performance of adobe blocks stabilized with fine sand and wild straw, produced with red clay from Puyo (Ecuador). The research followed an applied, descriptive–comparative design encompassing material characterization, a 30-day natural curing process, and compressive strength testing using a SHIMADZU Concreto 2000X machine. Results: The stabilized blocks achieved an average compressive strength of 9.63 kg/cm² (≈ 0.94 MPa), a mean displacement of 2.98 mm, and a maximum load of 14.16 kN, values that confirm their suitability for low-rise load-bearing structures. The inclusion of wild straw improved ductility, internal cohesion, and post-fracture integrity, while controlled shade drying minimized microcracking. Compared with traditional handmade bricks (31 kg/cm²), the material showed lower strength but significantly higher environmental and economic sustainability. Conclusion: The results provide empirical indicators to guide the standardization and scalability of stabilized adobe as a low-carbon, structurally viable alternative for rural and peri-urban housing in seismic regions.
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    Lightweight deep learning for sustainable adobe construction: a block-level baseline aligned with circular economy principles and sustainable development goals
    (Frontiers Media SA, 2026-07-08)
    Pinto Almeida, Carlos  
    ;
    Jordan, Nancy  
    Introduction – 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.