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Prediction of Compressive Strength of Concrete Using

Prediction of Compressive Strength of Concrete Using

PREDICTION OF CONCRETE COMPRESSIVE STRENGTH USING ...

Abrams model; a widely accepted empirical equation relating water/cement ratio of concrete to its compressive strength results in only modest strength prediction (R2 =0.80). Using a multiple parameter regression model (augmented Abrams equation) approach, significantly improved strength prediction (R2 =0.98) was achieved.

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The prediction of compressive strength of concrete has great connotation, if it is brisk and consistent because it offers an option to do the essential modification on the mix proportion used to avoid circumstances where concrete does not attain the mandatory design strength or by avoiding concrete that is gratuitously sturdy and also for more economic use of raw material and fewer construction

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Abstract and Figures An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks...

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Mar 01, 2018  The ANN is modelled in MATLAB and applied to predict the compressive strength of RAC given the foregoing input features. The results indicate that the ANN is an efficient model to be used as a tool in order to predict the compressive strength of RAC which is comprised of different types and sources of recycled aggregates.

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Prediction of Compressive Strength of Concrete Using ...

Jan 01, 2021  Prediction of Compressive Strength of Concrete Using Double-Shear Testing Method ... Test results demonstrated that the DSTM is applicable to in situ tests of concrete compressive strength with much higher accuracy than the core-drilling method, and causes less damage to structures.

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PREDICTION OF CONCRETE COMPRESSIVE STRENGTH

equation for the prediction of concrete compressive strength at different ages (e.g. 7 and 28 days). The variables used in the prediction models were from the knowledge of the mix itself, i.e. mix proportion elements (cement, water, sand, aggregate and density). The proposed

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Prediction of Compressive Strength of Concrete Using ...

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions.

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Prediction of compressive strength of concrete containing ...

Jan 01, 2013  Highlights The concrete uses aggregates from construction and demolition waste. The ANN was used to construct an equation for predicting the compressive strength. The compressive strength is predicted at 3, 7, 28 and 91 days. The results show the potential of using ANN for predicting the compressive strength.

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Concrete compressive strength prediction using non ...

Dec 01, 2020  Results showed that the improvement to estimate the compressive strength of concretes using the sole RN is more efficient, while the combination of RN and UPV could not improve the accuracy in comparison with sole tests. Hereby, the results are summarized as follow: 1. RSM model proposed to predict the compressive strength using non-destructive ...

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Prediction of compressive strength of recycled aggregate ...

Apr 11, 2021  Compressive strength prediction of concrete recycled aggregates made from Ceramic Tiles using Feedforward Artificial Neural Network (FANN) Elsevier (2012) Google Scholar. S. Kim, H-B. Choi, Y. Shin, G-H. Kim, D-S. Seo. Optimizing the mixing proportion with neural networks based on genetic algorithms for recycled aggregate concrete.

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Prediction of Compressive Strength of General-Use Concrete ...

Aug 07, 2021  The model can be used to predict the compressive strength of general-use concrete mixes with recycled aggregate (20–40 MPa) considering both the recycled aggregate content and the curing age of concrete. A good correlation was found between the compressive strength and the two parameters investigated.

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Prediction of 28-day Compressive Strength of Concrete from ...

Prediction of Concrete Strength Using Microwave Based Accelerated Curing Parameters by Neural Network Prediction of compressive strength of concrete is very useful for economic constructions. The compressive strength can be estimated after 28 days of casting the specimen cubes or may be predicted based on the quantum and quality of ingredients ...

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Table 4 from Concrete compressive strength prediction ...

Table 4 The performance criteria (RMSE, MAE, and R2) values using LSTM, ANN, and SVM - "Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment"

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Prediction of concrete compressive strength due to long ...

Sep 01, 2014  As an example, estimation of the compressive strength loss for concrete with w/c = 0.5, and C 3 A = 5.0% subjected to 5.0% Mg sulfate attack can be determined using Figure 11, Figure 12, Figure 13. The compressive strength loss after 150 years is 16%, 31%, and 37% for cement content with 450, 350, and 300 kg/m 3 , respectively.

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Prediction Compressive Strength of Concrete Containing ...

Dec 28, 2020  Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random

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Prediction of compressive and splitting tensile strength ...

Aug 07, 2021  Abstract The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. ... Prediction of compressive and splitting tensile strength of concrete with fly ash by using gene expression programming ...

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Prediction of Concrete Compressive Strength by ...

Mar 03, 2015  Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data

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Prediction of the Compressive Strength of Concrete Admixed ...

(ii) mathematical equation has been derived showing the relationship between concrete compressive strength and its constituents, using GEP. With an R 2 value of 0.95, from the model, the GEP algorithm has shown to be a good prediction program for modelling the compressive strength of concrete. The model derived can serve as the objective ...

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(PDF) Prediction of Compressive Strength of Concrete Using ...

Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming January 2016 Advances in Materials Science and Engineering 2016(2):1-10

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Prediction of concrete compressive strength using non ...

Prediction of concrete compressive strength using non-destructive test results Hamit Erdal 1 , Mürsel Erdal 2 , Osman Şim ş ek 2 and Halil İbrahim Erdal 3 1 Institute of Social Sciences ...

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Prediction of the Compressive Strength of Recycled ...

Recycled aggregate concrete (RAC), due to its high porosity and the residual cement and mortar on its surface, exhibits weaker strength than common concrete. To guarantee the safe use of RAC, a compressive strength prediction model based on artificial neural network (ANN) was built in this paper, which can be applied to predict the RAC ...

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Prediction Compressive Strength of Concrete Containing ...

Dec 28, 2020  Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random

get price

Concrete Compressive Strength Prediction Using Neural ...

Oct 07, 2020  Compressive Strength Prediction Using UPV, RN and SR. The compressive strength prediction of concrete specimens performed using neural network in different ages of concrete specimens. Figure 9 shows that the best accuracy for prediction is related to the age of 90 days with R 2 = 0.9803. The lowest accuracy prediction is related to the age of 7 ...

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Prediction of Compressive Strength of General-Use Concrete ...

Aug 07, 2021  The model can be used to predict the compressive strength of general-use concrete mixes with recycled aggregate (20–40 MPa) considering both the recycled aggregate content and the curing age of concrete. A good correlation was found between the compressive strength and the two parameters investigated.

get price

Prediction of the Compressive Strength of Concrete Admixed ...

(ii) mathematical equation has been derived showing the relationship between concrete compressive strength and its constituents, using GEP. With an R 2 value of 0.95, from the model, the GEP algorithm has shown to be a good prediction program for modelling the compressive strength of concrete. The model derived can serve as the objective ...

get price

Prediction of 28-day Compressive Strength of Concrete from ...

Prediction of Concrete Strength Using Microwave Based Accelerated Curing Parameters by Neural Network Prediction of compressive strength of concrete is very useful for economic constructions. The compressive strength can be estimated after 28 days of casting the specimen cubes or may be predicted based on the quantum and quality of ingredients ...

get price

Concrete Compressive Strength Prediction using Machine ...

Mar 05, 2020  Concrete Compressive Strength Prediction using Machine Learning. Pranay Modukuru. Mar 5, 2020 9 min read 0 Comments. Go to Project Site Project. Photo by Ricardo Gomez Angel on Unsplash. View post on Medium. Machine Learning Data Analysis Data Visualization Industry 4.0. Disqus Comments. We were unable to load Disqus.

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Table 4 from Concrete compressive strength prediction ...

Table 4 The performance criteria (RMSE, MAE, and R2) values using LSTM, ANN, and SVM - "Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment"

get price

Prediction of compressive and splitting tensile strength ...

Aug 07, 2021  Abstract The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. ... Prediction of compressive and splitting tensile strength of concrete with fly ash by using gene expression programming ...

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(PDF) Predicting 28 Days Compressive Strength of Concrete ...

BACKGROUND concrete strength at different age with high accuracy. This has been finally employed to predict 28 days strength of Early prediction of concrete compressive strength enables concrete made with brick aggregates from their 7 days test to know quickly about the concrete and its probable weakness results.

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Prediction of Later-Age Concrete Compressive Strength ...

Sep 08, 2020  Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications.

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Experimental Investigation and Prediction of Compressive ...

Dec 31, 2017  Instead of using the conventional variables in the prediction model, the inclusion of factors such as the amount of SF and FA, the water-to-cement equivalent ratio, and the difference between the minimum and maximum values of the aggregate enables the prediction of the compressive strength with reasonable accuracy.

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