• Advances in nano research
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• v.4 no.3
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• pp.181-195
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• 2016

Addition of nano-$SiO_2$ (NS) to geopolymer composites has been studied through measurement of compressive strengths, FTIR and XRD analysis. Alumino-silicate materials are coarse aggregate included waste concrete and demolished walls with its cementing binder, cement kiln dust (CKD) used and can possess a pronouncing activation for the geopolymer reaction resulting from the high alkali contents within. Materials prepared at water/binder ratios in a range of 0.30: 0.40 under curing of $40^{\circ}C$ and 100% Relative Humidity (R.H.), while the used activator is sodium hydroxide in the ratio of 2 wt. %. First, CKD is added in the ratio from 10 up to 50 wt., %, and the demolished walls was varied depending on the used CKD content, while using constant ratio of waste concrete (40 wt., %). Second step, depending on the optimum CKD ratio resulted from the first one (40 wt. %), so the control geopolymer mix composed of cement kiln dust, demolished walls and waste concrete in the ratio (40:20:40, wt %). Nano-silica partially replaced waste concrete by 1 up to 8%. Results indicated that, compressive strengths of geopolymer mixes incorporating nano-silica were obviously higher than those control one, especially at early ages and specially with 3%NS.

• International Journal of Concrete Structures and Materials
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• v.9 no.2
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• pp.145-158
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• 2015

The consequence of sulfate attack on geopolymer concrete, made from an alkali activated natural pozzolan (AANP) has been studied in this paper. Changes in the compressive strength, expansion and capillary water absorption of specimens have been investigated combined with phases determination by means of X-ray diffraction. At the end of present investigation which was to evaluate the performance of natural alumina silica based geopolymer concrete in sodium and magnesium sulfate solution, the loss of compressive strength and percentage of expansion of AANP concrete was recorded up to 19.4 % and 0.074, respectively.

• Advances in concrete construction
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• v.5 no.2
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• pp.145-154
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• 2017

In the present day scenario, concrete construction is rapidly becoming uneconomical and non sustainable practice, due to the scarcity of raw materials and environmental pollution caused by the manufacturing of cement. In this study an attempt has been made to propose recycled aggregates from demolition wastes as coarse aggregate in geopolymer concrete (GPC). Experimental investigations have been conducted to find optimum percentage of recycled aggregates (RA) in GPC by replacing 20%, 30%, 40%, 50% and 60% of coarse aggregates by RA to produce recycled aggregate geopolymer concrete (RGPC). From the study it has been found that the optimum replacement percentage of recycled aggregates was 40% based on mechanical properties and workability. In order to study and compare the flexural behaviour of RGPC and GPC four beams of size $175mm{\times}150mm{\times}1200mm$ were prepared and tested under two point loading. Test results were evaluated with respect to first crack load, ultimate load, load-deflection characteristics, ductility and energy absorption characteristics. Form the experimental study it can be concluded that the addition of recycled aggregate in GPC causes slight reduction in its strength and ductility. Since the percentage reduction in strength and behaviour of RGPC is meager compared to GPC it can be recommended as a sustainable and environment friendly construction material.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Computers and Concrete
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• v.20 no.4
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• pp.381-389
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• 2017

The various types of structural materials that are available in the construction industry nowadays make it necessary to predict their stress-strain behavior. Geopolymer are alternatives for ordinary Portland cement concrete that are made from pozzolans activation. Due to relatively new material, many mechanical specifications of geopolymer are still not yet discovered. In this study, stress-strain behavior has been provided from experiments for unconfined geopolymers. Modulus of Elasticity and stress-strain behavior are critical requirements at analysis process and knowing complete stress-strain curve facilitates structural behavior assessment at nonlinear analysis for structures that have built with geopolymers. This study intends to investigate stress-strain behavior and modulus of elasticity from experimental data that belongs for geopolymers varying in fineness and mix design and curing method. For the sake of behavior determination, 54 types of geopolymer are used. Similar mix proportions are used for samples productions that have different fineness and curing approach. The results indicated that the compressive strength ranges between 7.7 MPa and 43.9 MPa at the age of 28 days curing.

• Computers and Concrete
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• v.31 no.4
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• pp.327-335
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• 2023

The most popular building material, concrete, is intrinsically linked to the advancement of humanity. Due to the ever-increasing complexity of cementitious systems, concrete formulation for desired qualities remains a difficult undertaking despite conceptual and methodological advancement in the field of concrete science. Recognising the significant pollution caused by the traditional cement industry, construction of civil engineering structures has been carried out successfully using Geopolymer Concrete (GPC), also known as High Performance Concrete (HPC). These are concretes formed by the reaction of inorganic materials with a high content of Silicon and Aluminium (Pozzolans) with alkalis to achieve cementitious properties. These supplementary cementitious materials include Ground Granulated Blast Furnace Slag (GGBFS), a waste material generated in the steel manufacturing industry; Fly Ash, which is a fine waste product produced by coal-fired power stations and Silica Fume, a by-product of producing silicon metal or ferrosilicon alloys. This result demonstrated that GPC/HPC can be utilised as a substitute for traditional Portland cement-based concrete, resulting in improvements in concrete properties in addition to environmental and economic benefits. This study explores utilising experimental data to train artificial neural networks, which are then used to determine the effect of supplementary cementitious material replacement, namely fly ash, Ground Granulated Blast Furnace Slag (GGBFS) and silica fume, on the compressive strength, tensile strength, and modulus of elasticity of concrete and to predict these values accordingly.

• Computers and Concrete
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• v.21 no.2
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• pp.157-166
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• 2018

This article presents the flexural behaviour of reinforced fly ash (FA)-based geopolymer concrete (GPC) beams with partial replacement of FA for about 10% by weight with paper sludge ash (PSA). The beams were made of M35 grade concrete and cured under three curing conditions for comparison viz., ambient curing, external exposure curing, and oven curing at $60^{\circ}C$. The beams were experimentally tested at the 28th day of casting after curing by conducting two-point loading flexural test. Performance aspects such as load carrying capacity, first crack load, load-deflection and moment-curvature behaviours of both types of beams were experimentally studied and their results were compared under different curing conditions. To verify the response of reinforced GPC beams numerically, an ANSYS 13.0 finite element program was also used. The result shows that there is a good agreement between computer model failure behaviour with the experimental failure behaviour.

• Advances in concrete construction
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• v.5 no.4
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• pp.377-390
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• 2017

This study proposes a mix design method for geopolymer concrete (GPC) using low calcium fly ash and alccofine, with the focus on achieving the required compressive strength and workability at heat and ambient curing. Key factors identified and nine mixes with varied fly ash content (350, 375 and $400kg/m^3$) and different molarity (8, 12 and 16M) of NaOH solutions were prepared. The cubes prepared were cured at different temperatures ($27^{\circ}C$, $60^{\circ}C$ and $90^{\circ}C$) and tested for its compressive strength after 3, 7 and 28 days of curing. Fly ash content has been considered as the direct measure of workability and strength. The suggested mix design approach has been verified with the help of the example and targets well the requirements of fresh and hardened concrete.

• Steel and Composite Structures
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• v.45 no.6
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• pp.877-894
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• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• Advances in concrete construction
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• v.13 no.1
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• pp.25-34
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• 2022

The production of geopolymer is considered as a cleaner process due to much lower CO₂ emission than that from the production of Portland cement. This paper presents a study of the potential use of recycled steel fibre (RSF) coming from the recycling process of the old tires in geopolymer mortars. Ground expanded perlite (EP) is used as a source of alumino-silicate and sodium hydroxide (NaOH=5, 10, 15, and 20M) is used as alkaline medium for geopolymer synthesis. RSFs were added to the mortar mixtures in four different volume fractions (0, 0.5, 1.0, and 1.5% of the total volume of mortar). The unit weight, ultrasound pulse velocity, flexural and compressive strength of expanded perlite based geopolymer mortar (EPGM) mixtures were determined. The microstructures of selected EPGMs were examined by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analyses. The optimum molarity of sodium hydroxide solution was found to be 15M for geopolymer synthesis by EP. The test results revealed that RSFs can be successfully used for fibre-reinforced geopolymer production.