Medida del granizo mediante granizómetros = Hail measurements with hailpads

202 p.Los principales objetivos del trabajo que aquí se presenta son: 1) Realizar un estudio bibliométrico sobre las publicaciones dedicadas a la medida del granizo y el uso del granizómetro. 2) Analizar las posibles incertidumbres en la medida del granizo generadas en diferentes procesos de calibración del granizómetro. 3) Estudiar la incertidumbre generada en la medida del granizo por el solapamiento de las huellas en un granizómetro, y elaborar un modelo para corregir el efecto del solapamiento 4)Examinar cómo influye en la distribución de tamaños el hecho de agrupar los datos de los tamaños de granizo en clases discretas. 5) Predecir el tamaño máximo de granizo que alcanza el suelo a partir de variables meteorológica

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

[EN] The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.S

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

[EN] A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).S

To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models

[EN] This study aims to apply machine learning methods to predict the compression strength of self-compacting recycled aggregate concrete. To obtain this goal, the ensemble methods: Random Forest (RF), K-Nearest Neighbor (KNN), Extremely Randomized Trees (ERT), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), Category Boosting (CB) and the generalized additive models: Inverse Gaussian (GAM1) and Poisson (GAM2) were applied. For the development of the models, 515 research article samples were collected and divided into three subsets: training (360), validation (77), and testing (78). The SCC components: cement, water, mineral admixture, fine aggregates, coarse aggregates, and superplasticizers were taken as input variables and compression strength as output variables. To determine the ability of the models to project compressive strength, the following metrics were used: R2, RMSE, MAE, and MAPE. The results indicate that the RF (R2 = 0.7128, RMSE = 0.0807, MAE = 0.06) and GB (R2 = 0.6948, RMSE = 0.0832, MAE = 0.0569) models have a strong potential to predict the compressive strength of SCC with recycled aggregates. The sensitivity analysis of the RF model indicates that cement and water are the variables that have the highest impact in predicting the compressive strength, while coarse aggregate has the lowest impact.S

The Interactivity of a Virtual Museum at the Service of the Teaching of Applied Geology

[EN] In a framework in which teaching practice is a dynamic process, predisposed to continuous innovation, the Geological Collection of the University of León (CGULe), with 2000 copies of minerals, rocks and fossils, offers an opportunity for teaching innovation, in relationship with subjects of the geological disciplines that are taught in the Degrees of Mining Engineering and Energy Engineering. At http://laboratorio.wesped.es/, the first phase of development of the Virtual Museum of the CGULe is shown, where information and images of minerals and mineral deposits from León are offered. Likewise, videos of tests of characterization of minerals, made by students as a practice of the subject "Mineralogy and Petrography" (Degree in Mining Engineering), are offered as part of a teaching innovation. This teaching innovation was evaluated in two ways: a) comparing the academic results of students in this practice with equivalent results from previous courses and b) conducting a satisfaction survey. Given the small number of students who participated in this experience, the results of this evaluation are inconclusive. For this reason, teacher innovation will be extended in time and will be extended to other subjects of the above mentioned degrees.The work was partially funded by the project "Design of practical teaching-learning experiences in relation to the virtual musealization of the Geological Collection of the University of León", in the framework of the Support Plan for Teaching Innovation of the University of León (PAID & PAGID 2016). Likewise, the authors express their gratitude to D. Luis Armando Conejo Lombas, donor of copies to the Geological Collection of the University of Leon, as well as photographs of deposits to the Virtual Museum, and to the collaborators: Mr. Ángel Díez Bragado, Mr. Jesús García del Canto, Mr. Manuel Urcera Valladares and Mr. Guillermo Salazar Brugos.Gómez-Fernández, F.; Fernández-Raga, M.; Alaiz-Moretón, H.; Castañon-García, A.; Palencia, C. (2017). The Interactivity of a Virtual Museum at the Service of the Teaching of Applied Geology. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 712-719. https://doi.org/10.4995/HEAD17.2017.5366OCS71271

Impact of Design Parameters on the Ratio of Compressive to Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate

[EN] Most concrete studies are concentrated on mechanical properties especially strength properties either directly or indirectly (fresh and durability properties). Hence, the ratio of split tensile strength to compressive strength plays a vital role in defining the concrete properties. In this review, the impact of design parameters on the strength ratio of various grades of Self-Compacting Concrete (SCC) with recycled aggregate is assessed. The design parameters considered for the study are Water to Cement (W/C) ratio, Water to Binder (W/B) ratio, Total Aggregates to Cement (TA/C) ratio, Fine Aggregate to Coarse Aggregate (FA/CA) ratio, Water to Solid (W/S) ratio in percentage, superplasticizer (SP) content (kg/cu.m), replacement percentage of recycled coarse aggregates (RCA), replacement percentage of recycled fine aggregates (RFA), fresh density and loading area of the specimen. It is observed that the strength ratio of SCC with recycled aggregates is affected by design parameters.S

The Role of Weather Types in Assessing the Rainfall Key Factors for Erosion in Two Different Climatic Regions

P. 1-15This paper compares two different geographical sites, Aveiro and León, from different climatic regions, oceanic and continental, but which share the same type of weather (according to Lamb’s classification). The analysis was carried out over one year, and has revealed that rainfall in Aveiro is heavier and more abundant, with a higher number of raindrops and a longer duration of rain events (on average, 10 min longer than in Leon). Mean raindrop size is 0.45 mm in Aveiro and slightly smaller (0.37 mm) in Leon; in addition, the kinetic energy and linear momentum values in Aveiro are three times higher than those in Leon. A comparison of raindrop size distributions by weather type has shown that for both locations westerly weather presented a higher probability of rainfall, and the gamma distribution parameters for each weather type were independent of the study zone. When the analysis is done for the characteristics of rain related with erosion, the westerly cyclonic weather types (cyclonic west (CW) and cyclonic south-westerly (CSW)) are among the most energetic ones in both locations. However, comparing their five weather types with higher kinetic energy, in Aveiro a westerly component implies higher kinetic energy, while in Leon a southerly component involves more energy in the rain.S

Satisfaction Level of Engineering Students in Face-to-Face and Online Modalities under COVID-19—Case: School of Engineering of the University of León, Spain

[EN] University education in times of COVID-19 was forced to seek alternative teaching/learning methods to the traditional ones, having to abruptly migrate to the online modality, changes that have repercussions on student satisfaction. That is why this study aims to compare the level of student satisfaction in face-to-face and “forced” online modalities under COVID-19. A quantitative, cross-sectional methodology was applied to two groups of students: Under a face-to-face modality (n = 116) and under an online modality (n = 120), to which a questionnaire was applied under a Likert scale, with four dimensions: Course design structure, content, resources, and instructor. Non-parametric statistics, specifically the Mann–Whitney U-test, were used to compare the groups. The results showed that there are significant differences in the level of satisfaction of students in the face-to-face and online “forced” modalities (p = 0.01984 < 0.05), and the dimensions of the level of satisfaction that presented significant differences were course design structure (p = 0.04523 < 0.05) and content (p = 0.00841 < 0.05). The research shows that students in the face-to-face modality express a higher level of satisfaction, which is reflected in the dimension design structure of the course, specifically in its workload indicator, as well as in the dimension content, in its indicators, overlapping with other courses and materials.S

Póster científico sobre energía nuclear. Experiencias adquiridas y resultados obtenidos

P. 454-461La necesidad de innovar en educación se introduce en el ámbito universitario como algo prioritario. Atendiendo a esta inquietud, se lleva organizando durante cinco años un "Concurso de elaboración de un Póster científico", como una actividad formativa y evaluable más, dentro de la asignatura de Energía Nuclear. El objetivo de esta experiencia es motivar a los alumnos en la ampliación del conocimiento de una materia. Se define cada año un tema de trabajo diferente y se dan unas normas de presentación, semejantes a las de un Congreso. Se organizan grupos de trabajo con un máximo de cuatro estudiantes. La evaluación se realizará por un Comité Externo formado por expertos en la materia y por los propios alumnos. Durante la realización de una Jornada Técnica, relacionada con la temática propuesta, se falla el premio. Los ganadores exponen su trabajo en clase. Se realiza una encuesta de satisfacción, obteniendo muy buenos resultados.S

Estudio comparativo entre estudiantes de ingenierías de la Universidad de León mediante el test Force Concept Inventory

En este estudio se pretende detectar las carencias previas que tienen los estudiantes en la asignatura de Física, del primer curso y del primer semestre, cuando acceden a la Universidad. La investigación se ha realizado con alumnos de la rama de conocimiento de ingenierías, en la materia de Física. Se ha utilizado el test “Force Conccept Inventory”, desarrollado por Hestenes. Se ha realizado el test a los estudiantes al llegar a la Universidad (pre-test) y a los mismos alumnos al finalizar la materia. Los resultados de las pruebas se han comparado con las notas al finalizar la asignatura. Se ha podido observar que hay mas aprobados entre los alumnos que superaron el pre-test y el post-test. Esto quiere decir, que los estudiantes que han pasado el test presentan muchos menos preconceptos erróneos y por tanto consiguen sacar mejores notas