Prediction of Compressive Strength of Geopolymer Concrete by Using Random Forest Algorithm

Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.

Comparative study of compressive strength of novel steel fiber reinforced geopolymer concrete and conventional concrete

Geopolymer concrete offers an eco-friendlier option compared to traditional cement, as it lowers greenhouse gas emissions during production. It consists of an alkaline solution with sodium or potassium silicate and sodium or potassium hydroxide, as well as a source material abundant in silica and alumina. For this study, thirty geopolymer concrete samples were produced in the lab using a mix design approach from Scheffe’s (5,2) model. The focus of the research was on maximizing the compressive strength of the concrete, especially when incorporating sawdust ash as the source material. The findings indicated that subjecting sawdust ash to pyrolysis in the absence of oxygen significantly impacts its pozzolanic properties and, consequently, the characteristics of the concrete. The research determined the optimal compressive strength of geopolymer concrete incorporating sawdust ash to be 21.673 MPa, along with specific concentration ratios of NaOH, Na2SiO4 to NaOH, sawdust ash in the binder, water to binder, and activator to sawdust ash at 10.5415, 2.0446, 38.6307, 0.0363, and 2.5882 respectively. Furthermore, MATLAB-based computer programs were utilized to optimize and forecast the ideal mixture proportions for sawdust ash-based geopolymer concrete.

This research is mainly concentrated on the comparison of compressive strength of geopolymer concrete and conventional concrete with different proportions. This research is progressed to find a suitable eco-friendly concrete as a replacement for conventional concrete. In conventional concrete, cement, sand and blue metal are used. Cement is used as a prime binding material, which is a key resource for carbon dioxide. Carbon dioxide, a major member of greenhouse gases is a serious threat to the environment. Availability of river sand is becoming obsolete due to its extensive use in the construction industry. This study is focused on the reduction of carbon dioxide and river sand by making a cementless concrete called geopolymer concrete with M-Sand as fine aggregate. In this research, concrete specimens with various mix proportions for conventional and geopolymer concrete have been cast. In geopolymer concrete, alkaline solution is added to activate the binding property of fly ash with aggregates. The geopolymer concrete is cured under ambient conditions. The geopolymer concrete exhibits comparatively better compressive strength and may be used as replacement for conventional concrete.

Geopolymer concrete is a type of amorphous alumino-silicate cementitious material. Geopolymer can be polymerized by polycondensation reaction of geopolymeric precursor and alkali polysilicates. Compared to conventional cement concrete, the production of geopolymer concrete has a relative higher strength, excellent volume stability and better durability. This paper presents the mix design and compressive strength of geopolymer concrete manufactured from the blend of palm oil fuel ash (POFA) and pulverized fuel ash (PFA) as full replacement of cement with a combination of sodium silicate and sodium hydroxide solution used as alkaline liquid. The density and strength of the geopolymer concrete with various PFA: POFA ratios of 0:100, 30:70, 50:50 and 70:30 together with sodium silicate to sodium hydroxide solution by mass at 2.5 and 1.0, are investigated. The concentrations of alkaline solution used are 14 Molar and 8 Molar. Tests were carried out on 100x100x100 mm cube geopolymer concrete specimens. Specimens were cured at room temperature and heat curing at 60°C and 90°C for 24 hours, respectively. The effects of mass ratios of PFA: POFA, the alkaline solution to PFA: POFA, ratio and concentration of alkaline solution on fresh and hardened properties of concrete are examined. The results revealed that as PFA: POFA mass ratio increased the workability and compressive strength of geopolymer concrete are increased, the ratio and concentration of alkaline solution increased, the compressive strength of geopolymer concrete increases with regards to curing condition.

A review of the influence of source material’s oxide composition on the compressive strength of geopolymer concrete

Geopolymer concrete is an eco-friendly alternative to conventional Portland cement concrete due to its reduced carbon emissions and utilization of industrial by-products. This study investigates the effect of heat curing on the compressive strength of geopolymer concrete using fly ash as the primary binder and Palm Oil Shell Ash (POSH) as a partial replacement material. Two alkaline activators to fly ash ratios were considered, namely 0.55 and 0.825. Specimens were cured under ambient conditions and heat curing at 60°C for 24 hours. Experimental results indicate that heat curing significantly enhances compressive strength compared to room temperature curing. The incorporation of POSH also contributed to strength improvement and sustainable waste utilization. The findings demonstrate that controlled heat curing is an effective technique for improving geopolymer concrete performance.

Conventional and Ensemble Machine Learning Models to Predict the Compressive Strength of Fly Ash Based Geopolymer Concrete

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteriaincorporated 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 (28oC) 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.

Fly ash is a hazardous and toxic waste material from burning coal. However, it can be used as a material of geopolymer concrete because it contains high amounts of silica and alumina. Recent research on geopolymer concrete is still being conducted because it is part of green building material. This review aims to determine and analyzed the durability of fly ash geopolymer hybrid concrete in aggressive environments. The environment studied included seawater, sulfuric acid, and elevated temperature. OPC concrete is a high calcium material; therefore it is susceptible to aggressive environments compared with OPC concrete, geopolymer concrete contains aluminosilicate and low calcium material, which is not reactive with aggressive environment. The method used in this study is a literature review of several relevant research sources, and data were collected and analyzed. The results showed that fly ash geopolymer hybrid concrete had increase compressive strength compared to OPC concrete. In the seawater environment, the compressive strength of geopolymer concrete increases from 10%-15%. The compressive strength of geopolymer concrete also increases in a sulfuric acid environment up to 360 days of test age, and it is resistant to fire up until 800 °C. Likewise, the porosity of the geopolymer fly ash concrete decreased while the OPC concrete increased. According to the analysis and discussion results, it was resumed that fly ash geopolymer concrete is more environmentally friendly and durable than OPC concrete, especially in seawater, sulfuric acid, and post-fire environments.

The purpose of this article is to evaluate regression’s potential and soft computing-based models such as linear regression, random forest, random tree, and artificial neural network for concrete mixed with marble powder waste for the accurate prediction of the 28-day compressive strength. A total of 49 experimental observations were collected from previously published research articles and randomly divided into two parts: the training dataset, which accounted for 70% of the total dataset, and the testing dataset, which accounted for 30% of the total dataset and was used to validate the model. To assess each model’s performance, evaluation has been done using five quantitative measures such as coefficient of correlation, root mean square error, mean absolute error, relative absolute error, root relative squared error, and Taylor diagram (graphical representation) was drawn for comparing the performance of developed models. Results conclude that M6-ANN had performed better than other applied models.

Geopolymer concrete offers a promising alternative to traditional Portland cement concrete, exhibiting comparable mechanical and durability performance while reducing environmental impacts. However, achieving desirable properties in geopolymer concretes through heat curing remains challenging. This study proposes the use of an unsupervised Artificial Neural Network (ANN): Self-Organizing Map (SOM) to determine the optimal curing temperature of geopolymer concrete based on experimental datasets. The novelty of this study lies in utilizing SOM for clustering and pattern recognition to establish the relationship between curing temperatures and compressive strength, providing a novel data-driven methodology for enhancing material performance. Data on compressive strength at different curing temperatures were collected and used to train and validate SOM models. Fly ash based geopolymer concretes of size 100mm3 cubes were prepared in two sets of activators; sodium hydroxide (NaOH) and a combination of sodium silicate (Na2SiO¬3) with NaOH. These samples underwent curing under three conditions: ambient, 60° and 80° for 28 days. Clustering analysis generated by the SOM model provides valuable insights into the relationship between curing conditions, activator dosages, and compressive strength. Consequently, a cluster of mix proportion was developed, enabling the selection of specific curing conditions that result in targeted compressive strength. The results show that curing temperatures of 80°C offers optimal compressive strength ranging from 27MPa to 34MPa. This method introduces a novel "cluster mix proportion" for selecting curing parameters and demonstrates the potential of machine learning in advancing sustainable construction materials. The approach provides a distinct advantage by reducing reliance on trial-and-error methods, saving time and resources, and establishing a foundation for further exploration of data-driven techniques in cement and concrete research.

In the recent year, extensive researches have been done on fly ash-based geopolymer concrete for its similar properties like Portland cement as well as its environmental sustainability. However, it is difficult to provide a consistent method for geopolymer mix design because of the complexity and uncertainty of its design parameters, such as the alkaline solution concentration, mole ratio, and liquid to fly ash mass ratio. These mix-design parameters, along with the curing time and temperature ominously affect the most significant properties of the geopolymer concrete, i.e., compressive strength. To overcome these difficulties, the paper aims to provide a simple mix-design tool using artificial intelligence (AI) models. Three well-established and efficient AI techniques namely, genetic programming, relevance vector machine, and Gaussian process regression are used. Based on the performance of the developed models, it is understood that all the models have the capability to deliver higher prediction accuracies in the range of 0.9362 to 0.9905 (based on R2 value). Among the employed models, RVM outperformed the other model with R2=0.9905 and RMSE=0.0218. Theodore, the developed RVM model is very potential to be a new alternative to assist engineers to save time and expenditure on account of the trial-and-error process in finding the correct design mix proportions.

Geopolymer concrete has better mechanical property and environmental advantages than ordinary Portland concrete. Different amount of fly ash, Ca, Mg and the proportions of mixing ingredients are researched in this paper. Contrast by mixing different ratio of calcium to magnesium (Ca-to-Mg) into the geopolymer concrete, the mechanical property was researched. The compress experiment was fulfilled, moisture reduction was measured. The experimental results show modified geopolymer concrete with Mg/Ca can enhance the ability of water retention. The compressive strength of geopolymer concrete enhanced when ratio of Mg-to-Ca was 1:1. The peak compressive strength of geopolymer concrete reached 43.7MPa when MgO and slag occupied 133% fly ash. Slag can obviously improve the mechanical properties of the geopolymers. Modified proper ratio of Ca-to-Mg can improve mechanical performance of the geopolymer gels by 52%-1461%.

This research discusses the compressive strength of geopolymers made from coffee grounds as an additional material in geo-polymer fly-ash based concrete. The research aims to design a geopolymer concrete mix based on coffee grounds and fly ash, determine the compressive strength of geopolymer concrete based on coffee grounds and fly ash, and identify the optimal percentage of coffee grounds in geopolymer concrete production. The study has limitations, such as a concrete strength of 35 MPa, testing at 7, 14, and 28 days, and the use of an alkali activator with a molarity of 8. The research method employed involved experiments conducted in the material and concrete laboratory with the variable percentage of coffee grounds ranging from 0% to 20%. The use of coffee powder in the geopolymer concrete mixture as a replacement for fly ash has an impact on reducing the slump value by up to 18.52 % and reducing the specific gravity of the concrete by up to 6.55%. However, the use of up to 10% coffee powder still produces compressive strength values above the minimum requirement for structural concrete compressive strength, namely 20 MPa. Thus, a mixture with 10% coffee grounds can still be used as an alternative construction material that is more economical and sustainable.