ÁVILA VEGA, CARLOS FABIÁN
Preferred name
ÁVILA VEGA, CARLOS FABIÁN
Main Affiliation
MCIAA - Mecánica Computacional e Inteligencia Artificial Aplicada
Web Site
ORCID
0000-0002-6979-1571
Scopus Author ID
57204362296
11 results
Now showing 1 - 10 of 11
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Item type:Publication, Beyond Prescriptive Codes: A Validated Linear–Static Methodology for Seismic Design of Soft-Storey RC Structures(MDPI AG, 2025-12-23); ; Reinforced concrete buildings with masonry-induced soft-storey irregularities exhibit extreme seismic vulnerability, a critical risk often underestimated by conventional code-based design. Standard equivalent static methods typically fail to capture the intense concentration of seismic demand at the flexible ground level, leading to unconservative designs that do not meet performance objectives. This research proposes a corrective linear–static methodology to address this deficiency. A new Equivalent Lateral Force profile (ELFi1) was developed, derived from modal analyses of 235 representative soft-storey archetypes to accurately account for stiffness heterogeneity. This profile was integrated with a realistic response modification coefficient (Ri1 = 5.04), determined to be 37% lower than the normative R-factor (R = 8) prescribed by code. Nonlinear static analyses confirmed that conventional design resulted in “irreparable” damage (mean Global Damage Index = 0.82). In contrast, redesigning the structure using the proposed ELFi1 and Ri1 methodology successfully mitigated damage concentration, upgrading structural performance to a “repairable” state (mean Global Damage Index = 0.52). Finally, Incremental Dynamic Analysis validated the approach; the redesigned structure satisfied FEMA P695 collapse prevention criteria, achieving an Adjusted Collapse Margin Ratio (ACMR) of 2.10. This study confirms the proposed method is a robust and practical design alternative for soft-storey mechanisms within a simplified linear framework. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Morphological characterization of the hippocampus: a first database in Ecuador(Frontiers Media SA, 2024-10-18) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Toward Responsible AI in High-Stakes Domains: A Dataset for Building Static Analysis with LLMs in Structural Engineering(MDPI AG, 2025-10-24); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cellular concrete: A viable low‐carbon alternative for developing countries in seismic regions?(Wiley, 2025-02-05); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Human–AI Teaming in Structural Analysis: A Model Context Protocol Approach for Explainable and Accurate Generative AI(MDPI AG, 2025-09-04); ; The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Thermodynamics-Informed Neural Networks for the Design of Solar Collectors: An Application on Water Heating in the Highland Areas of the AndesThis study addresses the challenge of optimizing flat-plate solar collector design, traditionally reliant on trial-and-error and simplified engineering design methods. We propose using physics-informed neural networks (PINNs) to predict optimal design conditions in a range of data that not only characterized the highlands of Ecuador but also similar geographical locations. The model integrates three interconnected neural networks to predict global collector efficiency by considering atmospheric, geometric, and physical variables, including overall loss coefficient, efficiency factors, outlet fluid temperature, and useful heat gain. The PINNs model surpasses traditional simplified thermodynamic equations employed in engineering design by effectively integrating thermodynamic principles with data-driven insights, offering more accurate modeling of nonlinear phenomena. This approach enhances the precision of solar collector performance predictions, making it particularly valuable for optimizing designs in Ecuador’s highlands and similar regions with unique climatic conditions. The ANN predicted a collector overall loss coefficient of 5.199 W/(m2·K), closely matching the thermodynamic model’s 5.189 W/(m2·K), with similar accuracy in collector useful energy gain (722.85 W) and global collector efficiency (33.68%). Although the PINNs model showed minor discrepancies in certain parameters, it outperformed traditional methods in capturing the complex, nonlinear behavior of the data set, especially in predicting outlet fluid temperature (55.05 °C vs. 67.22 °C). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Environmental Impact of Earthquake-Resistant Design: A Sustainable Approach to Structural Response in High Seismic Risk Regions(MDPI AG, 2024-11-28); This study evaluates the environmental impact of earthquake-resistant structural design choices in high-risk seismic regions through life cycle assessment. As climate change concerns intensify, understanding the environmental implications of structural design decisions becomes crucial for sustainable construction. Examining a building in Quito, Ecuador, the research compares three structural systems: Optimized Framed System (OFS), Optimized Dual System (ODS), and Equivalent Framed System (EFS). The assessment quantifies emissions through a ‘cradle to gate’ approach, encompassing materials fabrication, transportation, and construction processes. The results demonstrate that the ODS achieves optimal seismic performance equal to the EFS while reducing emissions by 38%, with only 5% higher emissions than the OFS. The findings establish that effective earthquake-resistant design can simultaneously achieve structural resilience and environmental sustainability, providing valuable insights for sustainable structural engineering practices in seismic regions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, In Pursuit of Healthier Learning Environments: High‐Altitude Classroom Ventilation(Wiley, 2024-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Influence of Abaca Fiber Treated with Sodium Hydroxide on the Deformation Coefficients Cc, Cs, and Cv of Organic Soils(MDPI AG, 2024-10-15); Andreína Damián ChalánThis study shows the influence of the inclusion of abaca fiber (Musa Textilis) on the coefficients of consolidation, expansion, and compression for normally consolidated clayey silt organic soil specimens using reconstituted samples. For this purpose, abaca fiber was added according to the dry mass of the soil, in lengths (5, 10, and 15 mm) and concentrations (0.5, 1.0, and 1.5%) subjected to a curing process with sodium hydroxide (NaOH). The virgin and fiber-added soil samples were reconstituted as slurry, and one-dimensional consolidation tests were performed in accordance with ASTM D2435. The results showed a reduction in void ratio (compared to the soil without fiber) and an increase in the coefficient of consolidation (Cv) as a function of fiber concentration and length, with values corresponding to 1.5% and 15 mm increasing from 75.16 to 144.51 cm2/s. Although no significant values were obtained for the compression and expansion coefficients, it was assumed that the soil maintained its compressibility. The statistical analysis employed hierarchical linear models to assess the significance of the effects of incorporating fibers of varying lengths and percentages on the coefficients, comparing them with the control samples. Concurrently, mixed linear models were utilized to evaluate the influence of the methods for obtaining the Cv, revealing that Taylor’s method yielded more conservative values, whereas the Casagrande method produced higher values. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Parametric Research of Granular Flow in Silos: A Micro- Mechanical Approach(Escuela Politecnica Nacional, 2023-11-14);