Machine Learning Model to Predict Slump, VEBE and Compaction Factor of M Sand and Shredded Pet Bottles Concrete

Nadimalla, Altamashuddinkhan; Masjuki, Siti Aliyyah; Saad, Syed; Ali, Mohammed Hasan

doi:10.1088/1757-899x/1244/1/012023

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…By creating various mathematical models, the actual behavior of the material is captured, and accurate predictions of different properties, such as failure, strength, durability, deformation, etc., can be obtained [88][89][90][91]. Computational models also allow engineers to simulate different rheological behavior of concrete [92][93][94][95][96]. Engineers and researchers can develop cost-effective and suitable mix designs in this way.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

et al. 2023

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The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models’ cast and printed flexural strength values were also correlated. The model’s performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

et al. 2023

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Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms

Jha,

Parihar,

Dongre

et al. 2024

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This study has been undertaken to investigate the compressive strength, Flexural strength and split tensile strength of concrete of grade M30 and M40 in present investigation by laboratory and predicting the strength through Machine learning technique. Flexural strength and split tensile strength which establishes the concrete class, is one of the most crucial characteristics of concrete. The primary characteristic of concrete's durability and safety is its predictable compressive strength, Flexural strength and split tensile strength which is necessary for the use of concrete structures. To explore the time-dependent behavior of concrete strength, considering factors such as curing duration and age. Main aim is to compare the performance of different regression methods, such as linear regression, ridge regression, lasso regression, or machine learning approaches like Random Forest and evaluate their suitability for concrete strength prediction and to find the accuracy of algorithms and regression.

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Machine Learning Model to Predict Slump, VEBE and Compaction Factor of M Sand and Shredded Pet Bottles Concrete

Cited by 2 publications

References 6 publications

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms

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