Soft computing techniques: Systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times

et al. 2021

PLoS ONE

Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.

Section: Analysis and Outputs A) Lr Modelmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes

Ahmed

et al. 2021

PLoS ONE

“…Figure 29 is presented the relationship between the compression strength of geopolymer and l/b. In addition, the kurtosis finds the heaviness of the distribution tails, while skewness measures the symmetry of the distribution [66]. Fig.…”

Section: Alkaline Solution/binder (L/b)mentioning

confidence: 99%

“…Due to the good ability of machine learning regarding prioritization, optimization, forecasting and planning were widely used in the various engineering fields [57]. In the literature, machine learning systems were used to model the various characteristics of different types of concrete composites such as compression strength of green concrete [62], splitting tensile and flexural strength of recycled aggregate concrete [63], modulus of elasticity of recycled concrete aggregate [64,65], the fc′ of high volume fly ash concrete [66], the fc′ of eco-friendly GPC containing natural zeolite and silica fume [67], splitting tensile strength of fiber-reinforced concrete [68], the fc′ of self-compacting concrete modified with nanosilica [69], and so on.…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

Ahmed

2021

Preprint

The growing concern about global climate change and its adverse impacts on societies is putting severe pressure on the construction industry as one of the largest producers of greenhouse gases. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is cementless concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to being an eco-efficient and environmentally friendly construction material. Compressive strength is one of the most important mechanical property for all types of concrete composites including geopolymer concrete, and it is affected by several parameters like an alkaline solution to binder ratio (l/b), fly ash (FA) content, SiO2/Al2O3 (Si/Al) of the FA, fine aggregate (F) and coarse aggregate (C) content, sodium hydroxide (SH) and sodium silicate (SS) content, ratio of sodium silicate to sodium hydroxide (SS/SH), molarity (M), curing temperature (T), curing duration (CD) inside the oven and specimen ages (A). In this regard, a comprehensive systematic review was carried out to show the effect of these different parameters on the compressive strength of the fly ash-based geopolymer concrete (FA-GPC). In addition, multi-scale models such as Artificial Neural Network (ANN), M5P-tree (M5P), Linear Regression (LR), and Multi-logistic Regression (MLR) models were developed to predict the compressive strength of FA-GPC composites. For the first time, in the modeling process, twelve effective parameters including l/b, FA, Si/Al, F, C, SH, SS, SS/SH, M, T, CD, and A were considered the modeling input parameters. Then, the efficiency of the developed models was assessed by various statistical assessment tools like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2). Results show that the curing temperature, sodium silicate content, and ratio of the alkaline solution to the binder content are the most significant independent parameters that influence on the compressive strength of the FA-GPC, and the ANN model has better performance for predicting the compressive strength of FA-GPC in compared to the other developed models.

Multifunctional computational models to predict the long‐term compressive strength of concrete incorporated with waste steel slag

Piro

Hamad

et al. 2022

Structural Concrete

Self Cite

To preserve the environment and natural resources, steel slag recovery conserves natural resources and makes landfill space available. Steel slag as a waste material has been partially substituted for fine (sand) and coarse aggregate in concrete (gravel). Compressive strength (CS) is the most significant mechanical attribute for all forms of concrete composites. To save time, energy, and money, it is essential to create accurate models for forecasting the CS of normal concrete (NC). In addition, it offers essential information for organizing the building work and details the ideal time to remove the formwork. In total, 338 data points were gathered, processed, and modeled in total. During the modeling approach, the most influential elements impacting the compressive strength (CS) of concrete with steel slag replacement were addressed. According to the modeling method, the most effective parameter which affects the compressive strength of normal concrete is the curing time. This research employed a Multi Logistic Regression model (MLR), an Artificial Neural Network (ANN), a Full Quadratic model (FQ), an M5P‐tree model, and an Interaction model to predict the compressive strength of normal strength concrete (CS ranged from 10 to 55 MPa) with steel slag aggregate replacement. According to data from the literature, the steel slag concentration enhanced the compressive strength. Based on evaluations with statistical tools like the objective (OBJ) function, the scatter index, and the Taylar diagram, the ANN model with the lowest root mean square error did better at predicting compressive strength than the other models.