Multivariable models including artificial neural network and M5P-tree to forecast the stress at the failure of alkali-activated concrete at ambient curing condition and various mixture proportions

Ahmed, Hemn Unis; Mohammed, Ahmed; Mohammed, Azad A.

doi:10.1007/s00521-022-07427-7

Cited by 42 publications

(9 citation statements)

References 86 publications

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“…This is mostly due to the wide range of the data variables. 60 Figure 18 demonstrates the effect of steel slag on compressive strength based on data collected from the literature. From Figure 18 it can be seen that the percentage of steel slag replacement with natural coarse aggregate higher than 50% enhanced the compressive strength of concrete.…”

Section: Full Quadratic Modelmentioning

confidence: 99%

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

Piro

Mohammed

Hamad

et al. 2022

Structural Concrete

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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.

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Section: Full Quadratic Modelmentioning

confidence: 99%

“…Correlation coefficient values imply that there are no significant correlations among the input parameters, as seen in Figure 2. This is mostly due to the wide range of the data variables 60 . Figure 18 demonstrates the effect of steel slag on compressive strength based on data collected from the literature.…”

Section: Models Comparisonmentioning

confidence: 99%

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

Piro

Mohammed

Hamad

et al. 2022

Structural Concrete

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“…Hence, creating greater strength in resisting the loads applied to the concrete. This study analyzes the content, and density, of the aggregates, FA and CA, along with their sizes, fine aggregate size (FAS), and coarse aggregate size (CAS) 9 . The summary of the used data points of the previously analyzed studies s recorded 94% of the data points clustering in the range of 0–4.75 mm of the FA diameter sizes.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the microstructure of SCMs has considerably improved due to the higher specific surface area of NPs and their reaction with undesirable C-H, which is present in the cement paste matrix to produce additional C-S-H gel. [8][9][10][11][12][13][14] The role of the ABC algorithm in this system is to find the optimum membership functions of the ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunneling database comprising more than 150 data samples with Brittleness Index, fracture spacing, α angle between the plane of weakness and the Technology business management (TBM) driven direction, and field single cutter load were assigned as model inputs to approximate Forest Productivity Index (FPI) values.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the analyzed data, it was illustrated that the addition of NPs into SCMs significantly enhances the fresh, mechanical, and durability performance of SCMs. Moreover, the microstructure of SCMs has considerably improved due to the higher specific surface area of NPs and their reaction with undesirable C–H, which is present in the cement paste matrix to produce additional C–S–H gel 8–14 . The role of the ABC algorithm in this system is to find the optimum membership functions of the ANFIS model to achieve a higher degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches

Ali

Muayad

Mohammed

2022

Structural Concrete

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Introducing nanotechnology in concrete is one of the most significant successes in enhancing the mechanical properties of concrete. It affects the quality of the microstructure of the concrete due to its extremely small nanoparticles leading to a faster reaction due to its large surface area. The nanoparticles fill the concrete's pores, resulting in considerably improved strengths in the early ages of the concrete. This research used six models; linear regression (LR), multilinear regression (MLR), nonlinear regression (NLR), pure quadratic (PQ), interaction (IA), and full quadratic (FQ), to predict the compressive strength of the concrete modified with different Nanosilica contents for various mix proportions. The models were applied to three datasets, with 420 collected from several research studies. The ranges of the concrete mix proportions used in this study were as follows, water/cement ratio (w/c) ranged between 0.1 and 1, cement content (C) ranged between 153.81 and 1200 kg/m3, fine aggregate content (FA) ranged between 492 and 2270 kg/m3, coarse aggregate content (CA) ranged between 617 and 2900 kg/m3, superplasticizer (SP) ranged between 0% and 6.7%, coarse aggregate size (CAS) ranged between 6 and 50.8 mm, fine aggregate size (FAS) ranged between 0.025 and 10 mm, Nanosilica content (NS%) ranged between 0% and 15%, w/c of 0.4–0.6 and the curing time (days) ranged between 3 and 180. The compressive strength of the collected datasets ranged from 3 to 120 MPa. The parameters that have improved the analyzed datasets of the compressive strength were the content of Nanosilica and the SP. The models were assessed for their accuracy using multiple assessment criteria; the most evident ones are; the objective function (OBJ), root mean square error (RMSE), Scatter Index (SI), and the maximum absolute error (MAE). The analytical models and the model comparison operations will be shown in detail in this research, indicating that the most effective and accurate model is the FQ model; where coefficient of determination (R2) of 0.96, RMSE of 3.49 MPa, MAE of 2.96 MPa, and SI of 0.08 were conducted from the analytical studies. From the developed models, the compressive strength of the concrete up to a high compressive strength level with high accurately can be predicted. Also, the compressive strength of the concrete can be accurately predicted for various sizes of aggregate and specimens.

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The Use of Artificial Neural Network Model to Predict the Compressive Strength of Sustainable Geopolymer Concrete: A Systematic Review

Philip,

Nidhi

2024

Lecture Notes in Civil Engineering

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Multivariable models including artificial neural network and M5P-tree to forecast the stress at the failure of alkali-activated concrete at ambient curing condition and various mixture proportions

Cited by 42 publications

References 86 publications

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

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

Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches

The Use of Artificial Neural Network Model to Predict the Compressive Strength of Sustainable Geopolymer Concrete: A Systematic Review

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