Vicente Mercado

Improving educational quality requires a deeper understanding of teaching and learning processes: which practices produce positive outcomes and which aspects need to be reconsidered. In this context, evidence-based education and predictive analytics offer complementary perspectives that can guide educational decisions, and help institutions respond more effectively to the educational needs of students. Evidence-based education seeks to ensure that decisions do not depend exclusively on intuition, tradition, or experience. At the same time, it recognizes that the professional judgment of educators remains essential. Evidence can help assess the effectiveness of a strategy, identify its strengths and limitations, and support continuous improvement

This research presents the calibration of a constitutive model to replicate the cyclic performance of soils using a Bayesian framework. This study uses data from laboratory-conducted consolidated undrained isotropic cyclic triaxial tests and numerical tools to estimate optimal parameters by the application of Slice Sampling in a Bayesian analysis and to determinate the uncertainty of the model. For each calibrated parameter in the model, a probability distribution was obtained from the Markov chain. The means and the standard deviations from the distributions are compared with the laboratory results by the simulation of a series of consolidated undrained isotropic cyclic triaxial tests and a numerical model for a deposit that replicates the Wildlife’s stratigraphic characteristics. The calibrated model response offers a good approximation of the recorded data and the uncertainty due to the model is evaluated. The results of this study demonstrate that Bayesian calibration can reliably quantify parameter uncertainty, reveal parameter correlations that deterministic methods overlook, and improve confidence in liquefaction assessments. This probabilistic framework provides a robust basis for extending calibration to other soil types and site conditions.

In this research, two components were developed jointly: On the one hand, an experimental plan was created to obtain specific variables of the concrete and serve as a reference for the second model, a numerical and computational type created to address the variability in parameters, such as the elasticity and flow of coarse aggregate and mortar. The experimental work reproduced a specific gradation with a 1” nominal maximum size TMN on spheroidal-shaped particles. To address the diversity of limestone in the national territory, rocks were extracted from five calcareous areas of the country, which were transformed through different activities from lamination and turning with different bits to being carved into spheres until the reference gradation formed. The next stage consisted of determining the mechanical and stress–strain properties of both the mortar and coarse aggregate. In the case of mortar, the compressive stress was obtained from 50 mm × 50 mm × 50 mm cubes and the modulus of elasticity from 100 mm × 200 mm cylinders; for the coarse aggregate, the compressive stress was obtained through tests on cylinders of the calcareous rock used to form the spherical aggregate particles. The materials were mixed according to a previous proportioning for f′_c = 28 MPa and cast in cylinders of 100 mm × 200 mm. Finally, the compressive stress and the modulus of elasticity in these specimens were determined. Separately, a computational model was created to reproduce the experimental model with the same type of materials and load conditions and thereby estimate the compressive stress and modulus of elasticity of the tested material. This model was developed based on finite elements simulating concrete under a two-phase model, in which the coarse aggregate phase was arranged with spheroidal particles that were assembled with the mortar paste, thus finally reproducing a concrete cylinder of 100 mm × 200 mm. Material properties, taken from experimental work, were assigned to these materials. Initially, work was carried out in the elastic range, obtaining, as a result, the modulus of elasticity E_c, and then the specimen was brought to failure, obtaining, as a result, the maximum compressive stress f′_c. To attend to the influence of the effect of the mesh size for modeling on the numerical results of both parameters, several simulations were carried out in which mesh sizes of 4.0, 3.5, 3.0, and 2.5 mm were established for the mortar and the coarse aggregate, respectively, for carrying out the modeling. The results in the computational model showed that the compressive stress turned out to be more sensitive than the modulus of elasticity to the variation in the size of the mesh. For the first, the differences between the 4 mm mesh and the 2.5 mm mesh reached 3%, but for the second, the difference only reached 1% between the results for the same meshes. When the results between the experimental and computational models were compared, we found that the experimental values had the best closeness with results in the 2.5 mm mesh.