Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network

Karthiyaini, S.; Senthamaraikannan, K.; Priyadarshini, J.; Gupta, Kamal; Shanmugasundaram, M.

doi:10.1155/2019/4654070

Cited by 23 publications

(15 citation statements)

References 53 publications

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“…In addition, two methods were applied to validate the model which are coefficient of determination (R 2 ) value and Average Absolute Relative Deviation (%AARD). R 2 value was obtained using Equation (2) [18]. Meanwhile, for calculating %AARD, the general equation is defined as Equation 3…”

Section: E Validation Of the Developed Modelmentioning

confidence: 99%

“…Table VII shows the experimental design for the validation where consist of the experimental result value, predicted value by the revised model and the residuals. There are two method used in this study to investigate the model validation which is by using R 2 value [18] and Average Absolute Relative Deviation (%AARD) [19]. R 2 was implemental to check the goodness of fit of the model between the experimental value and the predicted value by the revised model.…”

Section: Experimental and Validation Of Modelsmentioning

confidence: 99%

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Modelling of Average Pore Size and Porosity of Porous Polycaprolactone/Hydroxyap atite (PCL/HA) Composite Blends

Akhbar*,

Subuki,

Sharudin

et al. 2019

IJEAT

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The objective of this paper is to develop mathematical relationship between average pore size and porosity of porous polycaprolactone/hydroxyapatite (PCL/HA) composite and investigate the combined effect of temperature and pressure of foaming process and presence of HA. Porous PCL/HA composite was prepared using supercritical carbon dioxide (ScCO2) solid state foaming process. Three different temperatures and pressures of foaming process were varied at 35°C, 40°C, 45°C and 10MPa, 20MPa, 30MPa respectively. Meanwhile weight of HA was varied at 10, 20, 30 and 40 wt%. The result from analysis of variance (ANOVA) using Microsoft Excel found that average pore size is reduced with higher pressure and content of HA presence does not significantly affect the average pore size due to poor distribution of HA. Meanwhile for porosity, higher temperature is more dominant in increasing of porosity compared to the HA content. In addition, both designed models have low values of Average Absolute Relative Deviation (%AARD) and high value of coefficient of determination (R2 ) which reflects a good and satisfying result between the experimental values and model predicted values.

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Section: E Validation Of the Developed Modelmentioning

confidence: 99%

Section: Experimental and Validation Of Modelsmentioning

confidence: 99%

Modelling of Average Pore Size and Porosity of Porous Polycaprolactone/Hydroxyap atite (PCL/HA) Composite Blends

Akhbar*,

Subuki,

Sharudin

et al. 2019

IJEAT

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show abstract

“…For ft, Karthiyaini et al [105] developed an ANN model to predict ft of concrete and obtained an R 2 value of 0.94. Behnood et al [106] obtained an R 2 value of 0.89.…”

Section: Comparison Of the Results With Previous Studiesmentioning

confidence: 99%

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

et al. 2021

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Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

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“…Although they achieved a good prediction performance, their method did not include tuning for the hyperparameter of their model nor cross-validation procedure. The paper [12] compared the prediction performance of neural network and multiple regression to predict the compressive strength of fiber-reinforced concrete. They used different independent variables such as water/cement ratio, cross-sectional area of test specimen, Young's modulus of fiber in order to build their models.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation

Al-Abdaly

Al-Taai

Imran³

et al. 2021

EEJET

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Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRC

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Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network

Cited by 23 publications

References 53 publications

Modelling of Average Pore Size and Porosity of Porous Polycaprolactone/Hydroxyap atite (PCL/HA) Composite Blends

Modelling of Average Pore Size and Porosity of Porous Polycaprolactone/Hydroxyap atite (PCL/HA) Composite Blends

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation

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