This study focuses on the evaluation of the effects of a natural treatment of limestone rock samples using microalgae known as diatoms. A total of 18 samples in the form of 50 mm cubes, carved from limestone rock from Boñar (Spain), were analyzed, divided into experimental and control groups with an equal number of samples. Through various tests evaluating porosity, water absorption, frost resistance, and salt crystallization, diatom-treated samples were found to show higher porosity and water absorption compared with the control samples, especially when the entire sample was analyzed as a whole. However, in tests focusing on the surface side most exposed to biodeposition, reduced water absorption was observed in the treated samples, suggesting an improvement in their antiabsorption properties. In addition, slightly higher frost resistance was detected in the treated samples. For this reason, this study provides valuable information on the potential of diatoms to influence the properties of limestone rocks, which can serve as a basis for future research in this field and for the development of more effective treatments to improve the characteristics of rocks used in various applications.

Nowadays, special concrete-like self-compacting concrete (SCC) requires sustainability by introducing recycled aggregates as a partial replacement for natural aggregate. Technological development initiatives in the construction sector estimate the 28 days' concrete compressive strength before casting due to faster requirement; one method selected is an artificial neural network. From works of literature, 515 mixed design are collected and utilized for training, validation, and testing data to prepare models. Different applications of SCC require different strengths of concrete. Based on control mix compressive strength, the mix designs are grouped into three families as low, medium, and high strength, apart from a common family. The correlation between input and output variables for three different families is analyzed. ANOVA analyses are done for input parameters. Coefficient of relation (R2) is used for sensitive assessment and results for family I (R2 = 0.9299), family II (R2 = 0.824), family III (R2 = 0.8775), and family IV (R2 = 0.7991). Two further sensitivity analyses indicate that input parameters' influence varies for different families.

The aim is to predict the compressive strength of self-compacting concrete with recycled aggregates by using machine learning methods. Various ensemble methods and generalized additive models were applied to a sample of 515 research articles divided into training, validation and testing. Compressive strength was considered as output variable and SCC components as input variables. In addition, several metrics were used to evaluate the models in terms of their prediction. The results indicate that the Random Forest and Gradient Boosting models are capable of accurately predicting compressive strength, and that cement and water are the variables with the greatest impact on the prediction.

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.

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.

One of the sustainable substitutes for traditional concrete is to incorporate recycled concrete aggregates (RCA) as a substitute for natural aggregates and rice husk ash (RHA) as a partial substitute for Portland cement. Worldwide request for high-strength concrete (HSC) is raising quickly. HSC is very brittle, as it needs some type of reinforcement. Lately, hooked steel fibers (HSF) are gaining momentum in the concrete industry due to their high tensile strength. The problem with RHA-RCA concrete is its low early strength due to which researchers have suggested utilizing chemical activators (sodium sulfate) to enhance the early age strength of RHA concrete. In the present research, compression, and split tensile strength were assessed. For durability properties, water absorption, chloride penetration, sorptivity, and acid resistance test was conducted. It was noted that recycled aggregate concrete with chemically activated RHA had considerably improved strength than samples with no sodium sulfate, particularly in early age strength. The mix (R35-45RHA–SF–A) showed increased compression and split tensile strength among all mixes at 90 days. At 70% recycled aggregate, activated concrete with 45% RHA and 2% HSF displayed enhanced strength to the reference mix. The positive impact of the activator was likewise observed in the durability characteristics of samples with recycled aggregates. X-ray diffraction (XRD) analysis confirmed the development of strength of RHA concrete at later ages. Chemically activation of RHA could be a beneficial answer to address the problem of low strength and durability of RHA modified high strength fiber reinforced recycled aggregate concrete.

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%).

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.

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.

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.

Water is one of the most important erosive agents in roadside hillslopes. When these are built with ineffective drainage systems, erosion occurs, reducing road’s service life. However, these systems are not receiving the appropriate importance, given their strategic value. Therefore, a new drainage system called ‘branched’ is proposed in this study. Its technical and economic feasibility is compared with those of the traditional system, which consists of drainages with lines that follow maximum hillslope, to assess differences in relation to erosion, construction and maintenance costs, and service life. Different parameters were analysed, such as the average velocity of water (mm −1 ) running through the channels, its average specific energy (kJ), and its drag force (N). A scale model was constructed and used to test these factors before implementing it in natural terrain for testing it under field conditions. According to the theoretical and measured results, these factors were lower in the branched drainage than in the traditional one (from 24% to 34% in speed, from 37% to 60% in energy, and from 51% to 73% in force). The service life of hillslopes with a branched system of up to 0.5 m high and 1:2 grade is significantly longer than in those with a traditional drainage. Although the initial economic expense for the construction of the branched system is higher (€3534/m 3 as opposed to €2930/m 3 for the traditional one), its maintenance cost will be lower than the traditional one (€1230/m 3 per year for the branched one as opposed to €1332/m 3 per year for the traditional one). Consequently, under our experimental conditions, the proposed drainage will be profitable from the eighth year of construction, saving on the road maintenance in the following 15 years of service life.

This 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.

The specificity of this Project, based on project-based learning (ABP) in the subjects of Fluid Mechanical Engineering in the Degree of Electrical Engineering and Degree of Measurement Engineering, is that it includes collaborative work in teams with foreign students. It incorporates virtual exchange activities carried in English, which allow to improve a large number of soft skills developed thanks to the ABP combination with intercultural interaction. This aims at increasing the motivation of the students, and improving their communicative capacity in English. This proposal will also involve strengthening their teamwork skills, and using new technologies that are basic to enable communication between team members in two different countries. Other skills will also be developed, such as better time management, individual responsibility in the tasks assigned and improved communication for non-experts, since the work has been developed with students of different degrees. Finally, this proposal will be completed by peer assessment making them more aware of their ability and their performance in the tasks.

La 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.

Soil erosion is a serious ecological and environmental problem, and the main cause of land degradation in many ecosystems at global scale. Detachment of soil particles by raindrop splash is the first stage in the soil erosion process. A review of the scientific literature published in peer-reviewed international journals (ISI) over the last decades on splash erosion research sheds light on the current scientific knowledge on this topic. In addition, it highlights the research gaps and unanswered questions in our understanding of soil erosion processes due to splash. In this literature review, a bibliographic search in Web of Science by the Institute for Scientific Information (ISI) database was carried out on August the 9th, 2016, that returned 669 papers containing the words “splash erosion”. The research found was categorised according to a number of criteria: i) devices used to measure splash erosion, ii) advantages and disadvantages of these devices, iii) splash erosion studies by country, iv) date of publication of the first article, v) evolution of the number of articles published in each ten-year period, vi) concepts studied, vii) keywords, viii) authors, ix) number of citations, and x) most cited articles. After this review a synthesis of the information that the science has published about splash erosion was made in order to improve our understanding about splash erosion, by identifying the research questions that still remain unanswered today about the first detachment mechanism. From this review several issues were found important for the advancement of this research topic: a) further study of the known basic factors influencing splash erosion; b) description and quantification of sources of uncertainty about the measurement of different variables; c) to understand the influences that the chosen research approach by individual researchers will have in the final result; and, d) to study the impact of drivers or mitigation techniques that may affect splash erosion.

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.