Retraction Note: Effects of waste glass and waste marble on mechanical and durability performance of concrete

[EN] After publication concerns were raised that the XRD spectra in Figure 8 are identical and that the data points in Figure 16 and Figure 18 are the same although they report the results of different experiments. The authors are unable to provide the original data for examination. In addition, an investigation by the Editors has shown inappropriate changes in authorship during the review process. The Editors no longer have confidence in the results and conclusions presented. Jawad Ahmad disagrees with this retraction. Fahid Aslam, Jesús de-Prado-Gil and Shaker M.A.Qaidi did not respond to the correspondence from the Editors about this retraction. The Editors were not able to obtain current contact details for Rebeca Martinez-Garcia and Ameni Brahmia

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

Characteristics of Sustainable Concrete with Partial Substitutions of Glass Waste as a Binder Material

[EN] Manufacturing waste has been quickly increasing over time as a result of the fast‑rising population as well as the consumption of foods that are thrown away dishonestly, resulting in environmental contamination. As a result, it has been suggested that industrial waste disposal may be considerably reduced if it could be integrated into cement concrete manufacturing. The aim of this study is to analyze the properties of concrete employing waste glass (WG) as a binding material in proportions of 5%, 10%, 15%, 20%, 25%, and 30% by weight of cement. The fresh property was assessed using a slump cone test, while mechanical performance was assessed using flexural, compressive, splitting tensile, and pull‑out strength after 7, 28, and 56 days. Furthermore, microstructure analysis was studied by scan elec‑ tronic microscopic (SEM), Fourier‑transform infrared spectroscopy (FTIR) and thermo‑gravimetric analysis (TGA) test. The results reveal that the addition of discarded glass reduces the workability of concrete. Furthermore, mechanical performance was increased up to a 20% substitution of waste glass and then gradually declined. Waste glass can be employed as a micro filler or pozzolanic material without affecting the mechanical performance of concrete, accord‑ ing to microstructure research.S

Mechanical performance of concrete reinforced with polypropylene fibers (PPFs)

[EN] Fibers are one of the most prevalent methods to enhance the tensile capacity of concrete. Most researchers focus on steel fiber reinforced concrete which is costly and easily corroded. This study aims to examine the performance of polypropylene fiber reinforced concrete through different tests. PPFs were added into concrete blends in a percentage of 1.0%, 2.0%, 3.0%, and 4.0% by weight of cement to offset its objectionable brittle nature and improve its tensile capacity. The fresh property was evaluated through slump cone test and while mechanical strength was evaluated through compressive strength, split tensile strength flexure strength, and flexure cracking behaviors after 7-, 14-, and 28-days curing. Results indicate that slump decrease with the addition of PPFs while fresh density increase up to 2.0% in addition to PPFs and then decreases. Similarly, strength (compressive strength; split tensile strength, and flexure strength) was increased up to 2.0% addition of PPFs and then decrease gradually. It also suggests that Ductility; first crack load, maximum crack width, and load-deflection inter-relations were considerably improved due to incorporations of PPFs.SIThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Deanship of Scientific Research at King Khalid University for funding this work through group research program under grant number RGP. 2 /71/42

Waste Foundry Sand in Concrete Production Instead of Natural River Sand: A Review

[EN] The by-product of the foundry industry is waste foundry sand (WFS). The use of WFS in building materials will safeguard the ecosystem and environmental assets while also durable construction. The use of industrial waste in concrete offsets a shortage of environmental sources, solves the waste dumping trouble and provides another method of protecting the environment. Several researchers have investigated the suitability of WFS in concrete production instead of natural river sand in the last few decades to discover a way out of the trouble of WFS in the foundry region and accomplish its recycling in concrete production. However, a lack of knowledge about the progress of WFS in concrete production is observed and compressive review is required. The current paper examines several properties, such as the physical and chemical composition of WFS, fresh properties, mechanical and durability performance of concrete with partially substituting WFS. The findings from various studies show that replacing WFS up to 30% enhanced the durability and mechanical strength of concrete to some extent, but at the same time reduced the workability of fresh concrete as the replacement level of WFS increased. In addition, this review recommended pozzolanic material or fibre reinforcement in combination with WFS for future research.SIThe research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ‘Priority 2030′ (Agreement 075-15-2021-1333 dated 30 September 2021)

Effects of Steel Fibers (SF) and Ground Granulated Blast Furnace Slag (GGBS) on Recycled Aggregate Concrete

[EN] Recycled aggregate is a good option to be used in concrete production as a coarse aggregate that results in environmental benefits as well as sustainable development. However, recycled aggregate causes a reduction in the mechanical and durability performance of concrete. On the other hand, the removal of industrial waste would be considerably decreased if it could be incorporated into concrete production. One of these possibilities is the substitution of the cement by slag, which enhances the concrete poor properties of recycled aggregate concrete as well as provides a decrease in cement consumption, reducing carbon dioxide production, while resolving a waste management challenge. Furthermore, steel fiber was also added to enhance the tensile capacity of recycled aggregate concrete. The main goal of this study was to investigate the characteristics of concrete using ground granulated blast-furnace slag (GGBS) as a binding material on recycled aggregate fibers reinforced concrete (RAFRC). Mechanical performance was assessed through compressive strength and split tensile strength, while durability aspects were studied through water absorption, acid resistance, and dry shrinkage. The results detected from the different experiments depict that, at an optimum dose (40% RCA, 20%GGBS, and 2.0%), compressive and split tensile strength were 39% and 120% more than the reference concrete, respectively. Furthermore, acid resistance at the optimum dose was 36% more than the reference concrete. Furthermore, decreased water absorption and dry shrinkage cracks were observed with the substitution of GGBS into RAFRC.SIThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a group research program under grant number RGP. 2/129/42 and Taif University Researchers Supporting Project grant number [TURSP-2020/324]

Concrete with Partial Substitution of Waste Glass and Recycled Concrete Aggregate

[EN] The current practice of concrete is thought to be unsuitable because it consumes large amounts of cement, sand, and aggregate, which causes depletion of natural resources. In this study, a step towards sustainable concrete was made by utilizing recycled concrete aggregate (RCA) as a coarse aggregate. However, researchers show that RCA causes a decrease in the performance of concrete due to porous nature. In this study, waste glass (WG) was used as a filler material that filled the voids between RCA to offset its negative impact on concrete performance. The substitution ratio of WG was 10, 20, or 30% by weight of cement, and RCA was 20, 40, and 60% by weight of coarse aggregate. The slump cone test was used to assess the fresh property, while compressive, split tensile, and punching strength were used to assess the mechanical performance. Test results indicated that the workability of concrete decreased with substitution of WG and RCA while mechanical performance improved up to a certain limit and then decreased due to lack of workability. Furthermore, a statical tool response surface methodology was used to predict various strength properties and optimization of RCA and WG.SIThis research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program “Priority 2030” (Agreement 075-15-2021-1333 dated 30 September 2021)

Utilización de Motores Gráficos de Videojuegos en Entornos de Aprendizaje Basado en Proyectos (ABP) / Utilização de motores gráficos de videojogos em ambientes de aprendizagem baseados em projectos (PBL)

El ABP es un método pedagógico que favorece el aprendizaje significativo, y en el que los alumnos trabajan autónomamente en la realización de un proyecto que plantea la resolución de problemas, reales y motivadores, abordando temáticas del currículo educativo. Una de sus características es la ausencia del marco tradicional de aprendizaje en el aula. El proyecto debe suponer un reto que conduzca al estudiante a involucrarse activamente en la construcción de su propio conocimiento, con una actividad lo suficientemente atractiva para captar su atención y esfuerzo, sin dejar de lado el aspecto colaborativo. Los Motores Gráficos para la realización de videojuegos (Unity3D) proporcionan la posibilidad de crear juegos de ordenador, paseos virtuales o aplicaciones 3D, sin coste, con una curva de aprendizaje simple. La capacidad de aplicar acciones mediante programación orientada a objetos, unido a la posibilidad de simular comportamientos siguiendo las leyes de la física (motores de físicas), proporcionan características idóneas a este software para ser usado en el ABP. En el presente artículo se describen, analizan y, en su caso, se evalúan, tres entornos donde profesores del Área de Expresión Gráfica de la ULE, ya están aplicando el ABP, usando como herramienta de trabajo un motor gráfico (Unity3D). Cronológicamente, y debido también a las características de la propia tarea, se inició este proceso con la tutorización de trabajos fin de grado o máster (TFG y TFM). El profesor actúa como orientador y es el estudiante el que elige la temática dentro de sus preferencias o expectativas profesionales. Posteriormente, se ha aplicado la metodología en el ámbito de la asignatura de Expresión Gráfica II. El alumno propone un proyecto de su interés, relacionado con la especialidad de sus estudios y, en el que, además de trabajar competencias propias de la asignatura, comienza a adquirir otras transversales de otras materias. Por último, la tercera experiencia, que está en fase de diseño y autorización, corresponde al nivel de Educación Secundaria en los denominados estudios de Bachiller de Investigación/Excelencia. Los estudiantes adquirirán habilidades en ingeniería, colaborando en un proyecto conjunto con sus pares y realizando otro trabajo individual de su preferencia