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Nazari A.,Swinburne University of Technology | Maghsoudpour A.,WorldTech Scientific Research Center | Sanjayan J.G.,Swinburne University of Technology
Construction and Building Materials | Year: 2014

In this work, a method for incorporating fly ash in constructional technology was developed by production of boroaluminosilicate geopolymers. These new class of geopolymers were appropriately synthesized from mixtures of fly ash and anhydrous borax and compressive strengths as high as 64 MPa were achieved by using suitable amount of borax. Different types of microstructures were observed by changing mixture proportion of the specimens. All of specimens revealed a brittle fracture with no crack branching in the mixture. Additionally, less unreacted fly ash particle was observed in the considered pastes indicating a totally different fracture mechanism in boroaluminosilicate geopolymers than aluminosilicate ones. FT-IR analyses of the mixtures revealed an additional B-O bond in comparison with aluminosilicate geopolymers. It was shown that this bond is a key factor to determine compressive strength and specimens with no B-O bond have lower strengths. © 2014 Elsevier Ltd. All rights reserved.


Sanjayan J.G.,Swinburne University of Technology | Nazari A.,Swinburne University of Technology | Pouraliakbar H.,WorldTech Scientific Research Center
Materials and Design | Year: 2015

In this study, fracture toughness of steel fibre-reinforced aluminosilicate geopolymers is investigated experimentally and is modelled by explicit finite element method. Nine geopolymeric pastes with various alkali activators to fly ash weight ratios, sodium hydroxide (NaOH) to sodium silicate weight ratios, NaOH concentrations and curing temperatures were prepared and their fracture toughness was measured by pre-notched three point bending specimens. All samples were then reinforced by substituting of 2, 3 and 5. vol.% of geopolymeric pastes by steel fibres with diameter and length of 0.5 and 30. mm respectively. The effects of four different parameters on experimental and predicted fracture toughness were probed. Results indicated that NaOH to sodium silicate and alkali activator to fly ash weight ratios do not cause any deviation between experimental and predicted results. On the other hand, NaOH concentration and curing temperature were the most significant parameters that caused deviation of predicted results from experimental ones. Generally, it is possible to use the proposed modelling procedure to predict fracture toughness of steel-fibre reinforced geopolymers with a reasonable approximation. © 2015 Elsevier Ltd.


Nazari A.,Swinburne University of Technology | Maghsoudpour A.,WorldTech Scientific Research Center | Sanjayan J.G.,Swinburne University of Technology
Construction and Building Materials | Year: 2015

In the present work, flexural strength of plain and fibre-reinforced boroaluminosilicate geopolymers is studied. Traditional aluminosilicate geopolymers are produced by alkali activation of an aluminosilicate source. Alkali activator is normally made by mixing a high alkali solution (such as sodium hydroxide) and a silica-rich source (such as sodium silicate). Alkali activation of fly ash in this study, to fabricate boroaluminosilicate binders, was performed by mixtures of anhydrous borax and sodium hydroxide. Flexural strength of the specimens in unreinforced and reinforced conditions was measured by three-point bending. Reinforced specimens were prepared by using 2, 3 and 5 wt.% of steel fibres, with length and diameter of 30 and 0.5 mm respectively. The highest flexural strength of unreinforced specimens was 9.5 ± 0.4 MPa, with borax to NaOH solution weight ratio of 0.912 and alkali activator to fly ash weight ratio of 0.9. Reinforcing of this mixture by 5 wt.% of steel fibres resulted in the highest flexural strength, 11.8 ± 0.9 MPa. Maximum and minimum average increase of flexural strength of about 47% and 5% were achieved by adding 5 and 2 wt.% of steel fibres to some mixtures respectively. Results indicated the ability of these new classes of construction materials for using in flexural load-bearing sections in both unreinforced and reinforced situations. © 2014 Elsevier Ltd. All rights reserved.


Nazari A.,WorldTech Scientific Research Center | Torgal F.P.,University of Minho
Expert Systems with Applications | Year: 2013

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. © 2013 Elsevier Ltd. All rights reserved.


Khalaj G.,Islamic Azad University at Sāveh | Nazari A.,WorldTech Scientific Research Center | Khoie S.M.M.,Amirkabir University of Technology | Khalaj M.J.,Islamic Azad University at Sāveh | And 2 more authors.
Surface and Coatings Technology | Year: 2013

A duplex surface treatment on DIN 1.2210 steel has been developed involving nitriding and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700°C for 1-14h. The process formed a thickness up to 9.5μm of chromium carbonitride coatings on a hardened diffusion zone. Characterization of the coatings by means of scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) indicates that the compact and dense coatings mainly consist of Cr(C,N) and Cr2(C,N) phase. All the growth processes of the chromium carbonitride obtained by TRD technique followed a parabolic kinetics. Activation energy (Q) for the process was estimated to be 185.6kJ/mol of chromium carbonitride coating. A model based on genetic programming for predicting the layer thickness of duplex coating of the specimens has been presented. To construct the model, training and testing was conducted by using experimental results from 82 specimens. The data used as inputs in genetic programming models were five independent parameters consisting of the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. The training and testing results in genetic programming models illustrated a strong capability for predicting the layer thickness of duplex coating. © 2013 Elsevier B.V.


Narimani N.,University of Tehran | Zarei B.,University of Tehran | Pouraliakbar H.,University of Tehran | Pouraliakbar H.,WorldTech Scientific Research Center | Khalaj G.,Islamic Azad University at Sāveh
Measurement: Journal of the International Measurement Confederation | Year: 2015

Artificial neural networks with feed forward topology and back propagation algorithm were employed to predict the effects of chemical composition and corrosion cell characteristics on both corrosion current density and potential of microalloyed pipeline steels. Doing this, the chemical compositions comprising of "carbon", "magnesium", "niobium", "titanium", "nitrogen", "molybdenum", "nickel", "aluminum", "copper", "chromium", "vanadium" and "carbon equivalent" (all in weight percentage) along with corrosion cell characteristics of "reference electrode", "scan rate", "temperature", "relative pressure of oxygen", "pressure of purged CO2", "chloride ion", as well as, "bicarbonate concentration" were considered together as the input parameters of the network while the "corrosion current density" and "corrosion potential" were considered as the outputs. For purpose of constructing the models, 87 different data were gathered from literatures wherein different examinations were performed. Then data were randomly divided into training, testing and validating sets. Scatter plots and statistical criteria of "absolute fraction of variance (R2)", and "mean relative error (MRE)" were used to evaluate the prediction performance and universality of the developed models. Based on the analyses, the proposed models could be further used in practical applications and corrosion monitoring of the microalloyed steels. © 2014 Elsevier Ltd. All rights reserved.


Amirafshar A.,Tarbiat Modares University | Amirafshar A.,University of Applied Science and Technology of Iran | Pouraliakbar H.,University of Applied Science and Technology of Iran | Pouraliakbar H.,WorldTech Scientific Research Center
Measurement: Journal of the International Measurement Confederation | Year: 2015

In this research, friction stir processing (FSP) technique is applied for the surface modification of ST14 structural steel. Tungsten carbide tools with cylindrical, conical, square and triangular pin designs are used for surface modification at rotational speed of 400 rpm, normal force of 5 KN and traverse speed of 100 mm min-1. Mechanical and tribological properties of the processed surfaces including microhardness and wear characteristics are studied in detail. Furthermore, microstructural evolutions and worn surfaces are investigated by optical and scanning electron microscopes. Based on the achievements, all designed pins were successfully applicable for low carbon steel to produce defect-free processed material. By the microstructural changes within the stirred zone, the processed specimen is obtained higher mechanical properties. This is due to the formation of fine grains as the consequence of imposing intensive plastic deformation during FSP; however, this issue is highlighted by using square pin design. In this case, minimum grain size of 5 μm and maximum hardness of 320 VHN, as well as, maximum wear resistance are all examined for the specimen modified by square pin. © 2015 Elsevier Ltd. All rights reserved.


Bagheri A.,WorldTech Scientific Research Center | Nazari A.,WorldTech Scientific Research Center
Materials and Design | Year: 2014

Compressive strength of geopolymeric specimens produced by class C fly ash and granulated blast furnace slag aggregates has been studied. Four different independent factors comprising of aggregate content, sodium hydroxide concentration, curing time and curing temperature were considered as the variables. To attain the maximum possible accurate responses by means of the smallest amount of examinations, Taguchi design of experiment method was followed. By taking into account three levels for each factor, 9 series of experiments were conducted on the specimens at 2 and 7. days of water curing regime. For both considered regimes, a specimen with 30 weight percent of aggregate and sodium hydroxide concentration of 12. M cured at 90. °C for 16. h had the highest compressive strength. On account of reactivity between aggregates and the fly ash, the compressive strength was reached to 69.3. ±. 5.3. MPa and 76.2. ±. 3.6. MPa at 2 and 7. days of water curing, respectively. Fracture surface of specimens with the highest and the lowest strengths as well as effect of each considered factor on the compressive strength of the specimens were studied. © 2013 Elsevier Ltd.


Khalaj G.,Islamic Azad University at Sāveh | Pouraliakbar H.,Islamic Azad University at Sāveh | Pouraliakbar H.,WorldTech Scientific Research Center
Ceramics International | Year: 2014

Five different tool steels (DIN 1.2080, 1.2210, 1.2344, 1.2510 and 1.3343) have been targeted for a duplex surface treatment consisted of nitriding followed by vanadium thermo-reactive diffusion (TRD). TRD process was performed in molten salt bath at 575, 650 and 725 C for 1 to 15 h. A duplex ceramic coating of vanadium carbonitride (VCN) with a thickness up to 10.2 μm was formed on tool steel substrates. Characterization of the ceramic coating by means of scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) indicated that the diffused compact and dense layers mainly consisted of V(C,N) and V2(C,N) phases. Layer thickness of duplex coating has been modeled by gene expression programming (GEP). Recently, application of GEP as a computer-aided technique has got appreciable attraction especially for modeling and to formulate engineering demands. For GEP approaches, chemical composition of steel substrates along with different bath and processing parameters totally composed of 17 different parameters were considered as inputs to establish mathematical correlations. Finally, the training and testing results in models have shown strong potential for predicting the layer thickness of duplex treated ceramic coating on tool steels. © 2013 Elsevier Ltd and Techna Group S.r.l.


Khalaj G.,Islamic Azad University at Sāveh | Pouraliakbar H.,WorldTech Scientific Research Center | Arab N.,Islamic Azad University at Sāveh | Nazerfakhari M.,Islamic Azad University at Sāveh
Measurement: Journal of the International Measurement Confederation | Year: 2015

In corrosion monitoring, the prediction of material constitutive and environment relationship can improve the optimization design process of pipeline. Recently, the artificial neural network (ANN) models are considered as a powerful tool to describe the electrochemical corrosion behavior of materials. Based on the experimental data from the potentiodynamic polarization of high-strength low-alloy (HSLA) steels, an ANN was trained with standard back-propagation learning algorithm to predict the passivation current density and potential of microalloyed steels. The inputs of the model were chemical compositions comprising of "carbon", "magnesium", "niobium", "titanium", "nitrogen", "molybdenum", "nickel", "aluminum", "copper", "chromium", "vanadium" and "carbon equivalent" (weight percent) and microstructure consist of diffusion (ferrite/pearlite) and shear (bainite/martensite) transformations, corrosion cell characteristics such as "reference electrode", "scan rate", "temperature", relative pressure of oxygen", "pressure of purged CO2", "chloride ion" and "bicarbonate concentration" whereas "passivation current density" and "passivation potential" were the outputs. According to the predicted and experimental results, it was indicated that the developed model showed a good capacity of modeling complex corrosion behavior and could accurately tracks the experimental data in a wide steel chemical compositions, microstructures, temperature ranges and corrosion cell characteristics. Scatter plots and statistical criteria of "absolute fraction of variance (R2)", and "mean relative error (MRE)" were used to evaluate the prediction performance and universality of the developed models. Based on analyses, the proposed models could be further used in practical applications and corrosion monitoring of microalloyed steels. © 2015 Elsevier Ltd. All rights reserved.

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