Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials

Zhang, Jisong; Zhao, Yaohua; Li, Haijiang

doi:10.1155/2017/4563164

Cited by 49 publications

(24 citation statements)

References 27 publications

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“…In addition LibreOffice spreadsheet were used to calculate RMSE, R 2 , MAE and MAPE. Zhang et al [29] employed the Root Mean Square error (RMSE) -Equation (3)-and absolute factor of variance (R 2 )-Equation (4)-to examine deviations between the predicted and experimental values and Chandwani et al [30] used Mean absolute Error (MAE)-Equation 5…”

Section: 3mentioning

confidence: 99%

Prediction of Compressive Strengths for Rice Husks Ash incorporated concrete, Using Neural Network and Reviews

Ngandu¹

2021

ITECKNE

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Modelling of concrete that incorporates agricultural wastes such as rice husk ash (RHA) could potentially enhance utilization of green concrete and application of sustainable construction materials. This paper evaluations compressive strength prediction for rice husk ash (RHA) cementitious material incorporated concrete using artificial neural networks (ANNs) one of the various prediction methods. The research is based on various previous experimental studies.Literature reviews of 72 datasets for RHA incorporated concrete from 15 previous researches, were used and subjected to ANNs models, having learning rate of 0.06 with tanh activation functions. Four(4) input variables were considered, namely:- superplasticizer or water reducers variation from control (%), water to binder ratio, percentage of RHA and control compressive strengths. Output variable was compressive strength of RHA cementitious material incorporated concrete. The ANN with 15 neurons in the hidden layer was selected and indicated overall values of 5.10MPa, 0.99, 3.81MPa and 9.73% for the root mean square error (RMSE), absolute factor of variance (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) respectively and for individual training, validation/checking and testing datasets, the RMSE, R2, MAE and MAPE ranging between 3.98MPa-6.56MPa, 0.98-0.99, 3.44MPa-4.94MPa and 9.19%-12.41% respectively. Generally, both predicted and original dataset, indicated higher and lower strength values for 5-10% and 15-30% RHA incorporated cementitious material concrete respectively compared to the control strengths.Considering that the study utilized data from different sources and with a wide range of concrete strengths the selected ANN showed relatively good performance. The study provides an indicator that machine learning techniques could accurately predict green concrete strength. Based on model performance the percentage RHA cementitious materials in concrete and the other 3 input variable had a significant impact on concrete strengths. Future research should be conducted to predict green concrete focused on particular concrete class.

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Section: 3mentioning

confidence: 99%

Prediction of Compressive Strengths for Rice Husks Ash incorporated concrete, Using Neural Network and Reviews

Ngandu¹

2021

ITECKNE

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“…The RMSE, R 2 were also calculated by LibreOffice calc spreadsheet. Zhang et al [25] examined the deviation between predicted and experimental values using RMSE, mean absolute percentage error (MAPE) and absolute factor of variance (R 2 ), the equations (3) and (4). Chandwani et al [26] used six different statistical performance for evaluation that included mean absolute error (MAE) and mean absolute percentage error (MAPE) as shown in equations ( 5) and (6).…”

Section: Evaluation Of Network(s)mentioning

confidence: 99%

Prediction of Compressive Strengths of Concrete with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

Ngandu

2021

ITECKNE

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In recent past years, plastic waste has been a environmental menace. Utilization of plastic waste as fine aggregate substitution could reduce the demand and negative impacts of sand mining while addressing waste plastic challenges.This study aims at evaluating compressive strengths prediction models for concrete with plastic—mainly recycled plastic—as partial replacement or addition of fine aggregates, by use of artificial neural networks (ANNs), developed in OCTAVE 5.2.0 and datasets from reviews. 44 datasets from 8 different sources were used, that included four input variables namely:- water: binder ratio; control compressive strength (MPa); % plastic replacement or additive by weight and plastic type; and the output variable was the compressive strength of concrete with partial plastic aggregates.Various models were run and the selected model, with 14 nodes in hidden layer and 320,000 iterations, indicated overall root mean square error (RMSE) , absolute factor of variance (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 1.786 MPa, 0.997, 1.329 MPa and 4.44 %. Both experimental and predicted values showed a generally increasing % reduction of compressive strengths with increasing % plastic fine aggregate.The model showed reasonably low errors, reasonable accuracy and good generalization. ANN model could be used extensively in modeling of green concrete, with partial waste plastic fine aggregate. The study recommend ANNs models application as possible alternative for green concrete trial mix design. Sustainable techniques such as low-cost superplasticizers from recycled material and cost-effective technologies to adequately sizing and shaping plastic for fine aggregate application should be encouraged, so as to enhance strength of concrete with partial plastic aggregates.

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“…A total of a hundred and fourteen (114) training data set were presented to the network. Eighty (80) of these were used for training the network, seventeen (17) were used for validation, and another seventeen (17) were used for testing the network's performance. This division was achieved by the use of the 'dividerand' function and the network objects.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

“…Many researchers have successfully used artificial intelligence to model the relationship between parameters in concrete. ANN have been used in predicting the compressive strength of Ultra high performance concrete containing fly ash and silica fume [17]. [18], applied it in predicting the compressive strength of cement based materials exposed to sulphate attacks.…”

Section: Introductionmentioning

confidence: 99%

Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model

et al. 2018

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In this research work, the levernberg Marquardt back propagation neural network was adequately trained to understand the relationship between the 28th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Data used for the study were generated experimentally. A total of a hundred and fourteen (114) training data set were presented to the network. Eighty (80) of these were used for training the network, seventeen (17) were used for validation, and another seventeen (17) were used for testing the network's performance. Six (6) data set were left out and later used to test the adequacy of the network predictions. The outcome of results of the created network was close to that of the experimental efforts. The lowest and highest correlation coefficient recorded for all data samples used for developing the network were 0.901 and 0.984 for the test and training samples respectively. These values were close to 1. T-value obtained from the adequacy test carried out between experimental and model generated data was 1.437. This is less than 2.064, which is the T values from statistical table at 95% confidence limit. These results proved that the network made reliable predictions. Maximum compressive strength achieved from experimental works was 30.83N/mm2 at a water-cement ratio of 0.562 and a percentage replacement of ordinary portland cement with hydrated lime of 18.75%. Generally, for hydrated lime to be used in making structural concrete, ordinary portland cement percentage replacement with hydrated lime must not be up to 30%. With the use of the developed artificial neural network model, mix design procedure for hydrated lime cement concrete can be carried out with lesser time and energy requirements, when compared to the traditional method. This is because, the need to prepare trial mixes that will be cured, and tested in the laboratory, will no longer be required.

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Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials

Cited by 49 publications

References 27 publications

Prediction of Compressive Strengths for Rice Husks Ash incorporated concrete, Using Neural Network and Reviews

Prediction of Compressive Strengths for Rice Husks Ash incorporated concrete, Using Neural Network and Reviews

Prediction of Compressive Strengths of Concrete with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model

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