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prediction compressive strength of concrete containing

prediction compressive strength of concrete containing

Prediction of compressive strength of concrete containing ...

2013-1-1  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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Prediction of compressive strength of concrete containing ...

2008-1-1  In order to predict the 7, 28 and 90 days compressive strength values of concrete containing high-lime and low-lime FA without attempting any experiments were constructed models in artificial neural networks and fuzzy logic methods. The models were trained with input and output data.

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Prediction of Compressive Strength of Concrete

Prediction of compressive strength of concrete containing the industrial wastes using Artificial Neural Network (ANN). The results of the experimental study are used to develop the ANN model to predict the strength of concrete and it was observed that ANN has high potential for predicting the strength of concrete containing industrial wastes.

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Predicting the compressive strength of concrete

The results of sensitivity analysis indicated that the compressive strength of concrete containing MK is mostly influenced by its specific surface area, and SiO 2 /Al 2 O 3 ratio. The predicted results are in good agreement with the experimental ones.

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Prediction of compressive strength of concrete

2019-7-8  1 Prediction of compressive strength of concrete containing fly ash using data mining techniques Francisco F.Martins 1 and Aires Camões 2 1 Associate Professor, C-TAC/UM, Territory, Environment and Construction Research Centre, University of Minho, Guimarães, Portugal.

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Experimental Investigation and Prediction of

2019-7-30  evaluate the possibility of using the ANN to predict the compressive strength of UHPC containing SCMs. e prediction model used 11 input variables, which included themassofsand,cement,water,coarseaggregate,FA,SF, and superplasticiser, the water-to-cement equivalent ratio, theaggregate-to-cementequivalentratio,theneaggregate

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Prediction of optimum compressive strength of light

2020-7-1  Laboratory compressive strength at 28 days is slightly below the calculated target compressive strength by 24%. 4. This study has achieved light-weight palm kernel shell concrete and thereby recommends its use for structural applications having satisfied minimum compressive strength of 20 MPa and oven dry density of less than 2000 kg/m 3.

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Prediction of Compressive Strength of Concrete Using ...

2021-8-10  BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference: @Booklet{EasyChair:6272, author = {S Sushmitha and M Akash and S Jalok and S Ravikumar and V Arjun}, title = {Prediction of Compressive Strength of Concrete Using Artificial Intelligence}, howpublished = {EasyChair Preprint no. 6272}, year = {EasyChair, 2021}}

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Prediction of compressive strength of self-compacting ...

2011-10-1  The tests for compressive strength are generally carried out at about 7 or 28 days from the date of placing the concrete. The testing at 28-days is standard and therefore essential and at other ages can be carried out if necessary.

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Prediction of compressive strength of concrete containing ...

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.

get price

Prediction of compressive strength of concrete

2019-7-8  1 Prediction of compressive strength of concrete containing fly ash using data mining techniques Francisco F.Martins 1 and Aires Camões 2 1 Associate Professor, C-TAC/UM, Territory, Environment and Construction Research Centre, University of Minho, Guimarães, Portugal.

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Prediction Compressive Strength of Concrete Containing ...

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model Table 4 Comparison of different machine learning models for predicting compressive strength of concrete.

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Prediction of compressive strength of concrete containing ...

DOI: 10.1016/J.COMMATSCI.2007.04.009 Corpus ID: 136696934. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic @article{Topcu2008PredictionOC, title={Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic}, author={Ilker Topcu and Mustafa Saridemir},

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Prediction of Compressive Strengh of Concrete Containing ...

2021-8-11  The paper presents performance of the model developed to predict 28 days compressive strength using Fuzzy Logic technique for data taken from literature containing Nano-Silica as partial replacement of cement. The data used in the model is arranged in the format of seven input parameters that cover the contents of cement

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(PDF) Prediction of compressive strength of self ...

Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011. Paratibha Aggarwal. Rafat Siddique. Download PDF. Download Full PDF Package. This paper. A short summary of this paper.

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Compressive strength prediction of SCC containing fly ash ...

2021-7-15  Request PDF Compressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models Self-Compacting Concrete (SCC), is a

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Prediction of Compressive Strength of Concrete Using ...

2021-8-10  BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference: @Booklet{EasyChair:6272, author = {S Sushmitha and M Akash and S Jalok and S Ravikumar and V Arjun}, title = {Prediction of Compressive Strength of Concrete Using Artificial Intelligence}, howpublished = {EasyChair Preprint no. 6272}, year = {EasyChair, 2021}}

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Prediction of Compressive Strength of Rice Husk Ash ...

of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine

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Prediction of unconfined compressive strength of cement ...

Neural network analysis was used to construct models of unconfined compressive strength (UCS) as a function of mix composition using existing data from literature studies of Portland cement containing real industrial wastes. The models were able to represent the known non-linear dependency of UCS on

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Prediction of compressive strength of concrete containing ...

DOI: 10.1016/J.COMMATSCI.2007.04.009 Corpus ID: 136696934. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic @article{Topcu2008PredictionOC, title={Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic}, author={Ilker Topcu and Mustafa Saridemir},

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Prediction Compressive Strength of Concrete Containing ...

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model Table 4 Comparison of different machine learning models for predicting compressive strength of concrete.

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Prediction of Compressive Strengh of Concrete Containing ...

2021-8-11  The paper presents performance of the model developed to predict 28 days compressive strength using Fuzzy Logic technique for data taken from literature containing Nano-Silica as partial replacement of cement. The data used in the model is arranged in the format of seven input parameters that cover the contents of cement

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Prediction of compressive strength of concretes containing ...

Home Browse by Title Periodicals Advances in Engineering Software Vol. 40, No. 5 Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks

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Prediction Model of Compressive Strength Development

2019-3-13  Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method Guohua Liu * and Jian Zheng Institute of Hydraulic Structure and Water Environment, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; [email protected]

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Prediction of Compressive Strength of SCC-Containing ...

2021-8-14  Findings: By replacing cement with marble powder in a range between 5% to 10% by weight, it increases the compressive strength of concrete mix

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Prediction of Compressive Strength of Ultra-High ...

However, prediction of the 28-day compressive strength of concrete placed in the field on an actual construction site, termed field concrete herein, remains a challenge for the concrete industry ...

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Prediction of Compressive Strength of Concrete Using ...

2021-8-10  BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference: @Booklet{EasyChair:6272, author = {S Sushmitha and M Akash and S Jalok and S Ravikumar and V Arjun}, title = {Prediction of Compressive Strength of Concrete Using Artificial Intelligence}, howpublished = {EasyChair Preprint no. 6272}, year = {EasyChair, 2021}}

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Prediction of Compressive Strength of Concrete Using ...

2020-10-28  Vuong L.T., Le C., Nguyen D.S. (2021) Prediction of Compressive Strength of Concrete Using Recycled Materials from Fly Ash Based on Ultrasonic Pulse Velocity and Design of Experiment. In: Huang YP., Wang WJ., Quoc H.A., Giang L.H., Hung NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020.

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Prediction of Compressive Strength of Rice Husk Ash ...

of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine

get price