Effects of Al_2O_3 nanoparticles on properties of self compacting concrete with ground granulated blast furnace slag (GGBFS) as binder

In this work,strength assessments and percentage of water absorption of self compacting concrete containing ground granu-lated blast furnace slag (GGBFS) and Al2O3 nanoparticles as binder have been investigated.Portland cement was replaced by different amounts of GGBFS and the properties of concrete specimens were investigated.Although it negatively impacts the physical and mechanical properties of concrete at early ages of curing,GGBFS was found to improve the physical and me-chanical properties of concrete up to 45 wt% at later ages.Al2O3 nanoparticles with the average particle size of 15 nm were added partially to concrete with the optimum content of GGBFS and physical and mechanical properties of the specimens were measured.Al2O3 nanoparticle as a partial replacement of cement up to 3.0 wt% could accelerate C-S-H gel formation as a re-sult of increased crystalline Ca(OH)2 amount at the early ages and hence increase strength and improve the resistance to water permeability of concrete specimens.The increase of the Al2O3 nanoparticles’ content by more than 3.0 wt% would cause the reduction of the strength because of the decreased crystalline Ca(OH)2 content required for C-S-H gel formation.Several em-pirical relationships have been presented to predict flexural and split tensile strength of the specimens by means of the corre-sponding compressive strength at a certain age of curing.Accelerated peak appearance in conduction calorimetry tests,more weight loss in thermogravimetric analysis and more rapid appearance of the peaks related to hydrated products in X-ray dif-fraction results,all indicate that Al2O3 nanoparticles could improve mechanical and physical properties of the concrete speci-mens.

Computer-aided design of the effects of Cr_2O_3 nanoparticles on split tensile strength and water permeability of high strength concrete

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of 8 input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network was gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network has predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

Compressive strength and abrasion resistance of concrete containing SiO_2 and CuO nanoparticles in different curing media

In the present study,abrasion resistance and compressive strength of concrete specimens containing SiO2 and CuO nanoparticles in different curing media have been investigated.Portland cement was partially replaced by up to 2.0 wt%of SiO2 and CuO nanoparticles and the mechanical properties of the produced specimens were measured.Increasing the nanoparticles content was found to increase the abrasion resistance of the specimens cured in water and saturated limewater,while this condition was not observed for compressive strength in the both curing media.The enhancement of abrasion resistance was higher for the specimens containing SiO2 nanoparticles in both curing media.Since abrasion resistance and compressive strength of the specimens followed a similar regime as the nanoparticles increased for the specimens cured in saturated limewater,some experimental relationships has been presented to correlate these two properties of concrete for this curing medium.On the whole,it has been concluded that the abrasion resistance of concrete does not only depend on the corresponding compressive strength.

Effects of CuO Nanoparticles on Microstructure,Physical,Mechanical and Thermal Properties of Self-Compacting Cementitious Composites

In the present study,split tensile strength of self-compacting concrete with different amount of CuO nanoparticles has been investigated.CuO nanoparticles with the average particle size of 15 nm were added partially to self compacting concrete and split tensile strength of the specimens has been measured.The results indicate that CuO nanoparticles are able to improve the split tensile strength of self compacting concrete and recover the negative effects of polycarboxylate superplasticizer on split tensile strength.CuO nanoparticle as a partial replacement of cement up to 4 wt% could accelerate C-S-H gel formation as a result of increased crystalline Ca（OH）2 amount at the early ages of hydration.The increase of the CuO nanoparticles more than 4 wt% causes the decrease of the split tensile strength because of unsuitable dispersion of nanoparticles in the concrete matrix.Accelerated peak appearance in conduction calorimetry tests,more weight loss in thermogravimetric analysis and more rapid appearance of related peaks to hydrated products in X-ray diffraction（XRD） results all also indicate that CuO nanoparticles up to4 wt% could improve the mechanical and physical properties of the specimens.Finally,CuO nanoparticles could improve the pore structure of concrete and shift the distributed pores to harmless and few-harm pores.

Computer-aided Prediction of the ZrO_2 Nanoparticles' Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have been developed at different ages of curing. For building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models were arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles. It has been found that neural network （NN） and gene expression programming （GEP） models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

Effect of Layers Position on Fracture Toughness of Functionally Graded Steels in Crack Divider Configuration

In the present study, fracture toughness of functionally graded steels in crack divider configuration has been modeled. By utilizing plain carbon and austenitic stainless steels slices with various thicknesses and arrangements as electroslag remelting electrodes, functionally graded steels were produced. The fracture toughness of the functionally graded steels in crack divider configuration has been found to depend on the composites＇ type together with the volume fraction and the position of the containing phases. According to the area under stress-strain curve of each layer in the functionally graded steels, a mathematical model has been presented for predicting fracture toughness of composites by using the rule of mixtures. The fracture toughness of each layer has been modified according to the position of that layer where for the edge layers, net plane stress condition was supposed and for the central layers, net plane strain condition was presumed. There is a good agreement between experimental results and those acquired from the analytical model.