Maestría Análisis de Datos y Sistemas Inteligentes
URI permanente para esta colecciónhttp://hdl.handle.net/11634/72664
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Tipo de ítem: Ítem , Identificación paramétrica de rigidez y amortiguamiento estructural mediante una Red Neuronal Evolutiva con optimización multiobjetivo(Universidad Santo Tomás, 2026-06-26) Niño Sandoval, Kelly Johanna; Valencia Niño, Cesar Hernando; Rodríguez Torres, Andrés José; Universidad Santo TomásProblem: the identification of structural parameters in multiple-degree-of-freedom systems using only acceleration data constitutes a coupled inverse problem, sensitive to noise and characterized by limited damping observability, in this context, objective: this research developed a parametric identification framework to estimate the stiffness 𝑘𝑖 and damping 𝑐𝑖 of the five stories of a reducedscale prototype building, relying exclusively on accelerometric signals, to this end, method: a physics-informed Evolutionary Artificial Neural Network was integrated with a two-phase NSGAII Multiobjective Genetic Algorithm, formulating the identification task as the simultaneous minimization of temporal fidelity, modal consistency, and physical regularization; the network mapped a feature vector of dimension D=660 to admissible structural parameters through bounded decoding. As results: using the 1985 Mexico City earthquake record, nine configurations were evaluated with 30 independent runs, and the best architecture (H1=32, H2=16, PS1=220) achieved a median RMSE of 0.1895 m/s², a median 𝑅 2 of 0.860, and coefficients of variation below 0.15 for both stiffness and damping; additionally, nonparametric validation showed significant differences compared with GA and PSO (p<0.001), and the Pareto front reached 98 solutions with HV=1.000±pm0.000; finally, discussion: the results indicate that the proposed framework reduces repeated identification to a single learning task, with millisecond-level inference and potential application in post-earthquake structural health monitoring.Tipo de ítem: Ítem , Clasificación del Desempeño Académico en la Prueba Saber 11 Mediante Variables Socioeconómicas: Estudio Comparativo Entre Redes Neuronales, Convolucionales y Sistemas Expertos(Universidad Santo Tomás, 2026-10-25) Sanabria Casanova, Cesar Augusto; Acosta Velásquez, Elkin Vladimir; Valencia Niño, Cesar Hernando; Universidad Santo TomasThe research work was developed as a comparative study of different artificial intelligence models ("MLP – CNN 1D - LightGBM") for the classification of academic performance based on the results of the Saber 11° State examination in Colombia, through socioeconomic, family, and institutional variables. It addresses the problem of the limitations in traditional statistical approaches when capturing the complex, non-linear, and high-dimensional structures of the variables that explain educational gaps in Colombia. To this end, multiclass predictive models were formulated to utilize a historical dataset of approximately 7.2 million anonymized records provided by the ICFES, corresponding to the periods from 2014-2 to 2024-2. The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was employed, implementing a hybrid processing pipeline and comparative analyses of the three classes of artificial intelligence models. The experimental results demonstrated that deep neural network architectures ("MLP") achieved the best predictive performance and highest statistical stability, outperforming the one-dimensional convolutional neural network ("1D CNN") and the ensemble algorithm (LightGBM) by capturing the non-linear dependencies of the dataset for each individual. In conclusion, socioeconomic variables possess a significant predictive capacity regarding academic performance classification, and deep learning models serve as a technological and methodological tool to validate the design of an early warning system and support evidence-based decision-making in the Colombian educational sector.