Modeling the compressive strength of green mortar modified with waste glass granules and fly ash using soft computing techniques

Ahmad, Soran Abdrahman; Rafiq, Serwan; Faraj, Rabar H.

doi:10.1007/s41062-023-01041-9

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…Each independent variable has been individually correlated with the dependent variable (compressive strength) to determine the specific relationship between each independent variable and the dependent variable. The collected data is separated into two groups: the training data, consisting of 107 data points, and the testing data, consisting of 35 data points [64,65].…”

Section: Methodsmentioning

confidence: 99%

“…It is a commonly used model for predicting a dependent variable based on independent factors that have a linear relationship with it [62,64]. In engineering, "L.R" may be used to forecast the compressive strength, as seen in the following format:…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

“…Artificial Neural Networks (ANN) is regarded as dependable models that mimic the functioning of the human brain. They have input, hidden, and output components [68]. The structure of ANN is not standardised, and the analysis process is contingent upon the input parameters and the specific form of the given issues, as seen in Figure 12.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…A model with a SI (statistical index) that greater than 0.3 is considered to have poor performance, while a SI between 0.2 and 0.3 is considered acceptable. An SI between 0.1 and 0.2 is considered good performance, and a SI between 0 and 0.1 is considered excellent performance [66,67].…”

Section: Models Assessmentmentioning

confidence: 99%

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Soft Computing Modeling Including Artificial Neural Network, Non-linear, and Linear Regression Models to Predict the Compressive Strength of Sustainable Mortar Modified with Palm Oil Fuel Ash

Karim,

Rafiq,

Ahmad

et al. 2024

Constr.

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Producing sustainable concrete and mortar is the idea that have been investigated by many researchers in the world through using waste materials in the mortar or concrete compositions to reduce the thread on the environment. In order to predict the compressive strength of mortar, this article proposes statistical models utilising linear regression (LR), nonlinear regression (NLR), and artificial neural network (ANN) based on experimental data collected from prior research in the field. The pozzolanic material used in mortar is agricultural waste, specifically Palm Oil Fuel Ash (POFA). In order to choose the most efficient model, the proposed models were evaluated using several statistical parameters. When compared to alternative models (Linear regression, nonlinear regression, and ANN), the one developed using ANN proved to be the most efficient in terms of approach, giving lower values for root mean square error (RMSE) and mean absolute error (MAE) which were 5.11, and 4.175 respectively. The suggested ANN model performed well according to the scatter index (SI), and the coefficient of determination value (R2) value was 34% more than the R2 in the LR model and 23% greater in the NLR model.

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Section: Methodsmentioning

confidence: 99%

Section: Linear Regression (Lr)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Models Assessmentmentioning

confidence: 99%

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Soft Computing Modeling Including Artificial Neural Network, Non-linear, and Linear Regression Models to Predict the Compressive Strength of Sustainable Mortar Modified with Palm Oil Fuel Ash

Karim,

Rafiq,

Ahmad

et al. 2024

Constr.

View full text Add to dashboard Cite

show abstract

“…SI greater than 0.3 indicates poor performance, while values between 0.2 and 0.3 are acceptable. SI values between 0.1 and 0.2 indicate good performance, whereas an SI less than 0.1 signifies excellent performance, as detailed in reference [44].…”

Section: Models Assessmentmentioning

confidence: 99%

Different Statistical Modeling to Predict Compressive Strength of High-Strength Concrete Modified with Palm Oil Fuel Ash

Ahmad,

Mohammed,

Rafiq

et al. 2024

Emerg. Technol. Eng. J.

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The present study focuses on proposing various statistical models, such as linear regression (LR), nonlinear regression (NLR), and artificial neural network (ANN), to forecast the compressive strength of environmentally friendly high-strength concrete, incorporating waste agricultural material like palm oil fuel ash (POFA). A dataset of 105 experimental observations was compiled from existing literature to achieve this goal, which was subsequently partitioned into training and testing subsets. Each model was developed based on the training data and evaluated using the testing data. The performance of each proposed model was gauged using diverse statistical metrics like the coefficient of determination, mean absolute error, root mean square error, and scatter index to identify the most effective model. The findings indicate that using POFA with a finer particle size exerts a greater influence on the concrete's properties. The replacement was done using the weight method, and the predicted equation worked with the variation of the used rate of POFA from 0 to 60% of total binder weight. Substituting a portion of cement with POFA leads to a reduction in the heat of hydration and an extension of the setting time. The optimal percentage of POFA is 30%, yielding mechanical properties superior to those of the control mixture, particularly in the later stages of development. Among the models considered, the ANN demonstrates superior efficiency and accuracy in predicting the compressive strength of conventional concrete modified with POFA compared to LR and NLR models. This is evident in the ANN's higher R2 values of 52% and 16%, respectively, and a lower scatter index below 0.1%.

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A Comprehensive Exploration on the Effect of Waste Glass Powder as a Partial Replacement of Cement in Mortar: A Review, Analysis, and Modeling Investigation

Ahmad,

Ahmed,

Rafiq

et al. 2023

Arab J Sci Eng

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Modeling the compressive strength of green mortar modified with waste glass granules and fly ash using soft computing techniques

Cited by 13 publications

References 29 publications

Soft Computing Modeling Including Artificial Neural Network, Non-linear, and Linear Regression Models to Predict the Compressive Strength of Sustainable Mortar Modified with Palm Oil Fuel Ash

Soft Computing Modeling Including Artificial Neural Network, Non-linear, and Linear Regression Models to Predict the Compressive Strength of Sustainable Mortar Modified with Palm Oil Fuel Ash

Different Statistical Modeling to Predict Compressive Strength of High-Strength Concrete Modified with Palm Oil Fuel Ash

A Comprehensive Exploration on the Effect of Waste Glass Powder as a Partial Replacement of Cement in Mortar: A Review, Analysis, and Modeling Investigation

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