Ali Nazari

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

Abstract 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 to 4 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.

Graphene oxide impact on hardened cement expressed in enhanced freeze-thaw resistance

AbstractGraphene oxide (GO) is a newly invented material with extraordinary properties. This paper presents the effect of graphene oxide addition on freeze–thaw resistance in hardened cement. GO is incorporated in the cementitious matrix in ratios of 0.01, 0.03, and 0.06% by weight of cement. Freeze–thaw cycle tests show a weight loss of approximately 0.8% after 540 cycles in the control mix compared to approximately 0.25% in 0.06% GO mix. Several tests were conducted to investigate the reasons behind this result. The tests included nitrogen and water adsorption, air content, and compressive strength. The results showed that GO mixes have finer pore structure than the control mix. Moreover, the results indicated that GO addition increases air content in the mix and shows high compressive strength compared to the control mix. The enhancement of freeze–thaw resistance due to GO addition can be because of the modification of pore structure where water hardly freezes in small pores. Also, the resistance of na...

Nanotechnology in eco-efficient construction

Description: As the environmental impact of existing construction and building materials comes under increasing scrutiny, the search for more eco-efficient solutions has intensified. Nanotechnology offers great potential in this area and is already being widely used to great success. Nanotechnology in eco-efficient construction is an authoritative guide to the role of nanotechnology in the development of eco-efficient construction materials and sustainable construction. Following an introduction to the use of nanotechnology in eco-efficient construction materials, part one considers such infrastructural applications as nanoengineered cement-based materials, nanoparticles for high-performance and self-sensing concrete, and the use of nanotechnology to improve the bulk and surface properties of steel for structural applications. Nanoclay-modified asphalt mixtures and safety issues relating to nanomaterials for construction applications are also reviewed before part two goes on to discuss applications for building energy efficiency. Topics explored include thin films and nanostructured coatings, switchable glazing technology and third generation photovoltaic (PV) cells, high-performance thermal insulation materials, and silica nanogel for energy-efficient windows. Finally, photocatalytic applications are the focus of part three, which investigates nanoparticles for pollution control, self-cleaning and photosterilisation, and the role of nanotechnology in manufacturing paints and purifying water for eco-efficient buildings. Nanotechnology in eco-efficient construction is a technical guide for all those involved in the design, production and application of eco-efficient construction materials, including civil engineers, materials scientists, researchers and architects within any field of nanotechnology, eco-efficient materials or the construction industry.-Provides an authoritative guide to the role of nanotechnology in the development of eco-efficient construction materials and sustainable construction-Examines the use of nanotechnology in eco-efficient construction materials-Considers a range of important infrastructural applications, before discussing applications for building energy efficiency Contents: Contributor contact details

Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming

In the present work, the effect of SiO2 and Al2O3 nanoparticles on compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nanoalumina and nanosilica has been predicted by gene expression programming. The models were constructed by 12 input parameters, namely the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO2 nanoparticle content, Al2O3 nanoparticle content, oven curing temperature, oven curing time and test trial number. The value for the output layer was the compressive strength. According to the input parameters in gene expression programming models, the data were trained and tested, and the effects of SiO2 and Al2O3 nanoparticles on compressive strength of the specimens were predicted with a tiny error. The results indicate that gene expression programming model is a powerful tool for predicting the effect of nanoparticles on compressive strength of the geopolymers in the considered range.

Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP

Abstract GEP has been employed in this work to model the compressive strength of different types of geopolymers through six different schemes. The differences between the models were in their linking functions, number of genes, chromosomes and head sizes. The curing time, Ca(OH)2 content, the amount of superplasticizer, NaOH concentration, mold type, aluminosilicate source and H2O/Na2O molar ratio were the seven input parameters considered in the construction of the models to evaluate the compressive strength of geopolymers. A total number of 399 input-target pairs were collected from the literature, randomly divided into 299 and 100 sets and were trained and tested, respectively. The best performance model had 6 genes, 14 head size, 40 chromosomes and multiplication as linking function. This was shown by the absolute fraction of variance, the absolute percentage error and the root mean square error. These were of 0.9556, 2.4601 and 3.4716 for training phase, respectively and 0.9483, 2.8456 and 3.7959 for testing phase, respectively. However, another model with 7 genes, 12 head size, 30 chromosomes and addition as linking function showed suitable results with the absolute fraction of variance, the absolute percentage error and the root mean square of 0.9547, 2.5665 and 3.4360 for training phase, respectively and 0.9466, 2.8020 and 3.8047 for testing phase, respectively. These models showed that gene expression programming has a strong potential for predicting the compressive strength of different types of geopolymers in the considered range.

Effects of graphene oxide in enhancing the performance of concrete exposed to high-temperature

Abstract Effects of incorporation of graphene oxide (GO) in normal and high strength concrete at high temperatures were experimentally investigated. The behaviour of concrete specimens was examined by comprehensive thermal analyses of heat transfer, residual mechanical strength, pore structure, mass loss and dilatometery. The results showed significant improvement of mechanical strength of the specimens with GO; the residual compressive strength was about 70% compared to 35% for the reference specimens. This can be attributed to the modification of pore structure of GO specimens, which increased gel porosity and reduced capillary porosity. Hence, thermal deformation of specimens with GO was compatible and shows no early negative expansion. As a result, better resistance for cracks was observed in GO mixes and led to maintaining the mechanical strengths. Effective anti-spalling behaviour was observed for GO high strength specimens, whereas the reference specimens exhibited high spalling. This could be due to the reinforcing effect of GO and the influence of GO on creating networks of micro channels that assisted in releasing the vapour pressure.

Computer-aided Prediction of the ZrO2 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.

Boroaluminosilicate geopolymers: role of NaOH concentration and curing temperature

In the present paper, effects of concentration of sodium hydroxide solution (NaOH) and curing temperature on properties of boroaluminosilicate fly ash-based geopolymers are studied. Geopolymers, cement-free eco-friendly construction materials, are formed by alkali activation of an aluminosilicate source. By changing the alkali activator from a silica-rich to boron-rich one, it may be possible to have boroaluminosilicate binders. Results obtained indicated formation of B–O bonds in these types of geopolymers. Increasing NaOH concentration was observed to reduce compressive strength due to changes occurring in the nature of reactions between the alkali activator and fly ash particles. Establishment of unwanted complex compounds as well as formation of non-stoichiometric aluminosilicate binders instead of boroaluminosilicate ones were supposed to be the main reasons for this strength reduction. Additionally, curing temperature had a strong effect on the formation of new phases. Various microstructures were observed in boroaluminosilicate binders, where the presence of needle-like crystals was the main difference between these types of geopolymers and the aluminosilicate one.

Inhibition of carbonation attack in cement-based matrix due to adding graphene oxide

Abstract Transport properties of cementitious materials are very important as they effectively influence the durability of these materials. Carbonation rate depends on the ease of carbon dioxide (CO2) movement from outer to internal surfaces. In this study, graphene oxide (GO) was incorporated in cement matrix to improve resistance of cementitious mix against carbon dioxide attack. The results showed very low carbonation depth of cementitious mix with GO (GM), only 8% of carbonation depth of the control cement mixture without GO (CM) after 6 months’ carbonation period. The most notable finding was the limited carbonation depth of GM despite the increasing of exposure to CO2. As carbonation depth increased for CM with time, full carbonation occurred after 24 months. However, GM showed no progress in carbonation front. This significant result can be attributed to interlocking influence of the CO2 molecules. Moisture loss monitoring confirms that GO sheets can act as reservoirs for water (H2O) molecules which limit transport of CO2 towards calcium phases of cement hydration. Physical interlocking was examined through molecule dynamic modelling, Monte Carlo atomistic simulation was conducted to evaluate the effects of GO sheets on carbon dioxide adsorption by cement matrix. The results showed that GO sheets reduce van der Waals adsorption energy of CO2 molecules. In addition, concentration of CO2 molecules around GO sheets was higher than the other porous areas which indicates the capability of these sheets for adsorbing of the gaseous molecule because of their high surface area.

Modeling of compressive strength of geopolymers by a hybrid ANFIS-ICA approach

AbstractA hybrid adaptive neuro-fuzzy interfacial systems–imperialist competitive algorithm (ANFIS-ICA) was presented to determine the effect of concentration of alkali solution, alkali binder to alkali solution weight ratio, alkali activator to ordinary portland cement (OPC) weight ratio, oven curing temperature, and age of curing on the compressive strength of OPC-based geopolymers. Optimization of the type and number of membership functions was carried out by ICA while the training, testin,g and validating of the collected data sets was conducted by ANFIS. The obtained results indicated that the proposed ANFIS-ICA model is capable to predict the compressive strength of geopolymeric specimens well and suitably determine the effect of each parameter on this property. A parametric study is presented to show the effect of each parameter predicted by the model on compressive strength of the specimens.