Decreasing the hazardous effect of waste quartz powder and the toxicity of epoxy resin by its synergistic application in industrial coatings.

Quartz powder sourced from industrial wastes is very hazardous. It is because it contains large amounts of fine particles. Thus, it has the potential to cause cancer and nervous system impact on humans and animals. Furthermore, its disposal leads to water pollution and plant pollination (negative for the environment). It will not be dangerous if incorporated into a hardened epoxy resin coating. In turn, epoxy resin is very harmful to the environment, in particular to aquatic organisms; therefore, it is necessary to reduce its mass in coatings by using additives. The article describes the systematic investigation of the adhesion of an epoxy resin coating and an economic and toxicity analysis showing the cost and toxicity reduction of the epoxy resin coating by replacing a part of the epoxy resin mass with waste quartz powder. The key novelty of the following article is to highlight a new way to decrease the hazardous effect of waste quartz powder, thanks to its utilization in epoxy resin coatings. Furthermore, the novelty is to decrease the toxicity of epoxy resin by reducing its mass necessary to make the industrial coating.

Stabilization of Chromium Waste by Solidification into Cement Composites.

This article deals with the study of hazardous chromium leaching, stabilized/solidified by cement CEM II after 28 days of curing, in an acidic environment. The mortars subjected to this study were investigated by X-ray diffraction (XRD) characterization to evaluate the influence of chromium waste on their mineralogical structure. In the study range (0.6-1.2%), increasing the mass percentage of Cr2O3 in the mortars indicates that chromium accelerates the hydration process and setting of the mortar and increases the mechanical strength of the mortars compared to the control sample. It was observed that the release of chromium during the Toxicity Characteristic Leaching Procedure (TCLP) test and the efficiency of the stabilization/solidification process depended on the initial Cr concentration and the leaching time. The use of XRD allowed the identification of new crystallized phases in the cement matrices, namely, CaCrO4·2H2O and chromium-ettringite Ca6Cr2(SO4)3(OH)12·26H2O, which confirms the immobilization of chromium and the efficiency of the stabilization/solidification process. In this research, the release mechanism was found to be primarily a surface phenomenon by modeling the experimental data (dissolution or precipitation).

Nano-Clay Platelet Integration for Enhanced Bending Performance of Concrete Beams Resting on Elastic Foundation: An Analytical Investigation.

Acknowledging the growing impact of nanotechnologies across various fields, this engaging research paper focuses on harnessing the potential of nano-sized materials as enhancers for concretes. The paper emphasizes the strategic integration of these entities to comprehensively improve the strength and performance of concrete matrixes. To achieve this, an analytical study is conducted to investigate the static behavior of concrete beams infused with different types of clay nano-platelets (NC's), employing quasi-3D beam theory. The study leverages the effective Eshelby's homogenization model to determine the equivalent elastic characteristics of the nanocomposite. The intricate interactions of the soil medium are captured through the use of a Winkler-Pasternak elastic foundation. By employing virtual work principles, the study derives equations of motion and proposes analytical solutions based on Navier's theory to unravel the equilibrium equations of simply supported concrete beams. The results shed light on influential factors, such as the clay nano-platelet type, volume percentage, geometric parameters, and soil medium, providing insights into the static behavior of the beams. Moreover, this research presents previously unreported referential results, highlighting the potential of clay nano-platelets as reinforcements for enhancing structural mechanical resistance.

Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder.

In this study, a machine learning model for the precise manufacturing of green cementitious composites modified with granite powder sourced from quarry waste was designed. For this purpose, decision tree, random forest and AdaBoost ensemble models were used and compared. A database was created containing 216 sets of data based on an experimental study. The database consists of parameters such as the percentage of cement substituted with granite powder, time of testing and curing conditions. It was shown that this method for designing green cementitious composite mixes, in terms of predicting compressive strength using ensemble models and only three input parameters, can be more accurate and much more precise than the conventional approach. Moreover, to the best of the authors' knowledge, artificial intelligence has been one of the most effective and precise methods used in the design and manufacturing industry in recent decades. The simplicity of this method makes it more suitable for construction practice due to the ease of evaluating the input variables. As the push towards decreasing carbon emissions increases, a method for designing green cementitious composites without producing waste that is more precise than traditional tests performed in a laboratory is essential.

External Confined Concrete Cylinders Behavior under Axial Compression Using CFRP Wrapping.

Carbon-fiber-reinforced polymer (CFRP) is a composite material used to mend and strengthen concrete structural elements in civil engineering. The prime aim of this experimental study is to investigate the comportment of confined concrete cylinders (CCC) under uniaxial loads by altering the concrete strength, the CFRP angle orientation, and the volumetric ratio, following the externally bonded reinforcement technique (EBR). We present the results of the confinement effect and failure mechanisms issue of more than 150 specimens of CFRP confined concrete cylinders that have been undertaken and in which several parameters were altered. Totally and partially confined concrete cylinders were tested for failure under axial compressive loads and indirect tensile tests. Four different ratios of water/cement (0.33, 0.36, 0.401, and 0.522) were investigated. In addition, three sand-resin ratios were prepared to improve the mechanical properties and the adhesion of the CFRP and the concrete. The obtained results revealed a clear improvement in the compressive strength of the specimens made with low strength concrete (from 38% to 66%) compared to those made of high strength concrete (from 11% to 31%), where the improvements are relatively low. Furthermore, the transversally confined concrete cylinders presented significant gains in strength over those confined longitudinally. Lastly, adding sand to the resin increases the compressive strength of confined concrete cylinders (1.19% to 54.62%) and reduces the cost of the resin used for fixing CFRP materials.

The Effect of Basalt Aggregates and Mineral Admixtures on the Mechanical Properties of Concrete Exposed to Sulphate Attacks.

In this study, basalt, which is common around Diyarbakir province (Turkey), is used as concrete aggregate, waste materials as mineral additives and Portland cement as binding material to prepare concrete mixes. This paper aims to determine the proper admixture levels and usability of Diyarbakir basalt in concrete mixtures based on mechanical, physical and chemical tests. Thus, in order to determine the strength and durability performance of concrete mixtures with Diyarbakir basalt as aggregate, 72 sample cubes of 150 mm were prepared in three groups: mineral-free admixture (MFA), 10% of cement amount substituted for silica fume (SFS) and 20% for fly ash (FAS) as waste material. The samples were exposed to water curing and 100g/L sulphate solution to determine the loss in weight of the concrete cubes and compressive strength was examined at the end of 7, 28 and 360 days of the specimens. Analysis of the microstructure and cracks that influence durability, were also performed to determine effects of sulphate attacks alkali-silica reactions on the specimens using scanning electron microscopy (SEM). A loss in weight of the concrete cubes and compressive strength was distinctly evident at the end of 56 and 90 days in both acids.

The Isotropic Material Design of In-Plane Loaded Elasto-Plastic Plates.

This paper puts forward a new version of the Isotropic Material Design method for the optimum design of structures made of an elasto-plastic material within the Hencky-Nadai-Ilyushin theory. This method provides the optimal layouts of the moduli of isotropy to make the overall compliance minimal. Thus, the bulk and shear moduli are the only design variables, both assumed as non-negative fields. The trace of the Hooke tensor represents the unit cost of the design. The yield condition is assumed to be independent of the design variables, to make the design process as simple as possible. By eliminating the design variables, the optimum design problem is reduced to the pair of the two mutually dual Linear Constrained Problems (LCP). The solution to the LCP stress-based problem directly determines the layout of the optimal moduli. A numerical method has been developed to construct approximate solutions, which paves the way for constructing the final layouts of the elastic moduli. Selected illustrative solutions are reported, corresponding to various data concerning the yield limit and the cost of the design. The yield condition introduced in this paper results in bounding the values of the optimal moduli in the places of possible stress concentration, such as reentrant corners.

Mechanical and Microstructural Properties of Ordinary Concrete with High Additions of Crushed Glass.

This study investigates the use of crushed glass waste as partial cement replacement in ordinary concretes. Six concrete mixes were designed and prepared: a reference without substitution and five substitution percentages of crushed glass waste ranging from 5% to 25%. The made concrete mix design underwent different tests, namely: slump test, mechanical strength, thermogravimetric analysis (TGA), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) determination and finally, water porosimetry, in order to evaluate the influence of the use of crushed glass waste on the properties of fresh and hardened concrete. Mechanical strengths results show that the use of 15% of the crushed glass waste improves the mechanical strength. TGA analysis confirms this result by highlighting a higher hydration degree. The latter contributes to the reduction of the porosity and, consequently, the mechanical strength increases. Also, it can be caused by the increasing amount of chromium which, if added a little, accelerates the hydration of C3S and leads to an increase of the mechanical strength. The BET technique and porosimetry tests showed that the use of crushed glass waste reduces the global porosity of concrete. This is due to the filling effect of the glass powder.

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material.

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash.

Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water-binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.

The Nature-Inspired Metaheuristic Method for Predicting the Creep Strain of Green Concrete Containing Ground Granulated Blast Furnace Slag.

The aim of this study was to develop a nature-inspired metaheuristic method to predict the creep strain of green concrete containing ground granulated blast furnace slag (GGBFS) using an artificial neural network (ANN)model. The firefly algorithm (FA) was used to optimize the weights in the ANN. For this purpose, the cement content, GGBFS content, water-to-binder ratio, fine aggregate content, coarse aggregate content, slump, the compaction factor of concrete and the age after loading were used as the input parameters, and in turn, the creep strain (εcr) of the GGBFS concrete was considered as the output parameters. To evaluate the accuracy of the FA-ANN model, it was compared with the well-known genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). Results indicated that the ANNs model, in which the weights were optimized by the FA, were more capable, flexible and precise than other optimization algorithms in predicting the εcr of GGBFS concrete.

The Isotropic and Cubic Material Designs. Recovery of the Underlying Microstructures Appearing in the Least Compliant Continuum Bodies.

The paper discusses the problem of manufacturability of the minimum compliance designs of the structural elements made of two kinds of inhomogeneous materials: the isotropic and cubic. In both the cases the unit cost of the design is assumed as equal to the trace of the Hooke tensor. The Isotropic Material Design (IMD) delivers the optimal distribution of the bulk and shear moduli within the design domain. The Cubic Material Design (CMD) leads to the optimal material orientation and optimal distribution of the invariant moduli in the body made of the material of cubic symmetry. The present paper proves that the varying underlying microstructures (i.e., the representative volume elements (RVE) constructed of one or two isotropic materials) corresponding to the optimal designs constructed by IMD and CMD methods can be recovered by matching the values of the optimal moduli with the values of the effective moduli of the RVE computed by the theory of homogenization. The CMD method leads to a larger set of results, i.e., the set of pairs of optimal moduli. Moreover, special attention is focused on proper recovery of the microstructures in the auxetic sub-domains of the optimal designs.