• Advances in concrete construction
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• 제14권5호
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• pp.331-339
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• 2022

Geopolymer concrete (GPC) is one of the best substitute materials for conventional concrete in construction. The conventional concrete provided by Portland cement has a detrimental influence on the environment during its production. In this study, the bond strength, which is an important structural property, of deformed steel bars with slag-based GPC was measured. In accordance with the ASTM C234 procedure, bond strength was measured on 18 specimens of slag-based GPC with three sizes of steel bars and different embedded lengths. Two groups of GPC specimens with different compressive strengths, which were cured under ambient conditions, were tested. The results indicated that the bar diameter has a great effect on the bond strength, and the bond strength behavior of the slag-based GPC is comparable with that of conventional concrete. The ACI-318 Code for the bond strength of ordinary Portland cement concrete can be used conservatively to determine the bond strength of the GPC reinforced with deformed steel bars.

• Advances in concrete construction
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• 제7권4호
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• pp.231-239
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• 2019

The present study deals with the development of metakaolin-based geopolymer concrete (GPC) and thereafter studying the effects of adding ultra-fine slag on its mechanical and permeability characteristics. The mechanical characteristics including compressive, split tensile, flexural strengths and elastic modulus were studied. In addition, permeability characteristics including water absorption, porosity, sorptivity and chloride permeability were studied up to 90 days. The results showed the effective utilization of metakaolin for the development of elevated temperature cured geopolymer concrete having high 3-day compressive strength of 42.6 MPa. The addition of ultra-fine slag up to 15%, as partial replacement of metakaolin resulted in an increase in strength characteristics. Similar improvement in durability properties was also observed with the inclusion of ultra-fine slag up to 15%. Beyond this optimum content of 15%, further increase in ultra-fine slag content affected the mechanical as well as permeability parameters in a negative way. In addition, the relationship between various properties of GPC was also derived.

• Advances in concrete construction
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• 제11권3호
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• pp.219-229
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• 2021

In recent years, geopolymer cements, have gained significant attention as an environmental-friendly type of cement. In this experimental research, effects of different alkaline activator solutions and variations of associated parameters, including time of addition, concentration, and weight ratio, on the mechanical strengths of Granulated Ground Blast Furnace Slag (GGBFS)-based Geopolymer Concrete (GPC) were investigated. Investigation of the effects of simultaneous usage of KOH and NaOH solutions on the tensile and flexural strengths of GGBFS-based GPC, and the influence of NaOH solution addition time delay on the mechanical strengths is among the novel aspects investigated in this research. four series of mix designs and corresponding specimen testing is conducted to study different parameters of the active alkali solutions on GPC mechanical strengths. The results showed that addition of NaOH to the mix after 3 min of mixing KOH and Na2SiO3 with dry components (1/3 of the total mixing duration) resulted in the highest compressive, tensile and flexural strengths amongst other cases. Moreover, increasing the KOH concentration up to 12 M resulted in the highest compressive strength, while weight ratio of 1.5 for Na2SiO3/KOH was the optimum value to achieve highest compressive strengths.

• Computers and Concrete
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• 제31권4호
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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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• 제27권4호
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• Structural Engineering and Mechanics
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• 제70권6호
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Computers and Concrete
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• 제33권2호
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.