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

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

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

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

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

Iniciación a la investigación de los estudiantes mediante la creación de un poster científico

FECIES. Foro sobre la Evaluación de la Calidad de la Educación Superior y de la Investigación (12. 2015. Sevilla)[ES] Tanto la formación como la innovación educativa son campos que se están desarrollando continuamente en distintos ámbitos: universitario, empresarial e incluso no universitario (Sein-Echaluce, Fidalgo y García, 2014). El objetivo de este trabajo es motivar a los alumnos en la ampliación del conocimiento de una materia. Dentro de la propia asignatura, como una actividad más, se organiza un "Concurso de elaboración de un Póster científico". Se define un tema de trabajo y se dan unas normas de presentación, semejantes a las de un Congreso. Durante la realización de una Jornada técnica u otro evento similar, relacionado con la temática propuesta, se falla el premio. El grupo autor del póster ganador, expone su trabajo ante los presentes y recibe un pequeño obsequio. Durante ese día todos los pósteres están expuestos en el lugar donde se celebra el acto. Al finalizar este trabajo, se realiza una encuesta de opinión entre los alumnos. Se han obtenido siempre unos resultados muy positivos y se ha conseguido aumentar la motivación de los estudiantes hacia la asignatura (Barnett et al., 2003). El 94% opina que les ha resultado de ayuda para conocer mejor la materia.[EN] Training and educational innovation are fields that are continuously developing in different areas: academic, business and even non-academic disciplines (Sein-Echaluce, Fidalgo and García, 2014). The aim of this paper is to motivate students to expand their knowledge on a specific subject. Our proposal is the organization during the course of a complementary activity called "Student contest of scientific posters". A specific subject is defined and the presentation standards are given to the students following patterns similar to the instructions given in a conference. A technical conference or a similar event related to the topic is organized and the prize is awarded during that event. The group who has won the prize presents their poster to the audience and receives a small gift. On that day, all the posters are displayed during the workshops at the conference venue. A satisfaction survey is conducted at the end of each course. This survey has always revealed very positive opinions, as well as an increase in student motivation towards the subject (Barnett et al., 2003). According to the survey, over 94% of students feel that this experience has been helpful to improve their understanding of the subject

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

[EN] Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.S