Prediction Of 28 Day Compressive Strength Of Concrete Using Relevance Vector Machines Rvm

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Prediction Of 28 Day Compressive Strength Of Concrete Using Relevance Vector Machines Rvm

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Author : Jones Owusu Twumasi
language : en
Publisher:
Release Date : 2013

Prediction Of 28 Day Compressive Strength Of Concrete Using Relevance Vector Machines Rvm written by Jones Owusu Twumasi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

Early and accurate prediction of the compressive strength of concrete is important in the construction industry. Modeling the compressive strength of concrete to obtain a balance and equality between prediction accuracy, time and uncertainty of the prediction is a very difficult task due to the highly nonlinear nature of concrete. For structural engineering purposes, the 28- day compressive strength is the most relevant parameter. In this study, an attempt has been made to predict the 28-day compressive strength of concrete using Relevance Vector Machine (RVM). An RVM belongs to the class of sparse kernel classifiers, which are powerful tools in classification and regression. It has a model of identical functional form to the popular and state-of-the-art Support Vector Machine (SVM). The benefits of using RVM include automatic estimation of nuisance parameters, probabilistic prediction and the ability to model complex data with little information. A total of 425 different data of high performance mix designs were collected from the University of California, Irvine repository. The data used to predict the compressive strength consisted of nine components. The RVM model was trained and tested using 395 and 30 data sets respectively. The model's performance was assessed at the end of the training and testing period using four performance measures; coefficient of determination, root-mean-square error, percentage of relevance vectors and residual plots. All the performance measures confirmed the accuracy of the model. The results of the study suggested that RVM is an effective tool for predicting the 28- day compressive strength of concrete from its mix ingredients.

Models For Prediction Of 28 Day Concrete Compressive Strength

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Author : Muhammad Masood Rafi
language : en
Publisher:
Release Date : 2015

Models For Prediction Of 28 Day Concrete Compressive Strength written by Muhammad Masood Rafi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Cement categories.

The design codes usually specify 28-day concrete compressive strength as design strength for reinforced concrete (RC) structures. Concrete specimens are cast and tested at 28 days to ensure compliance of concrete strength with the design requirements. Prediction of concrete strength can help in reducing waiting time and can result in speeding up construction activities. This paper presents prediction models for concrete compressive strength up to 28 days. The data of experimentally tested concrete cylinders were employed in the development of these models. The effects of cement chemical composition and fineness were included by defining two parameters in the models. The predictions are based on 7-day concrete strength. The proposed models provided good correlation with the observed concrete strength data. The models were also validated using the strength results of concrete mixes in the available literature. Generalized forms of the models have been suggested for cement brands available in Pakistan.

Prediction Of Uniaxial Compressive Strength Of Rock Using Support Vector Machine Algorithm

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Author : Hafedz Zakaria
language : en
Publisher:
Release Date : 2016

Prediction Of Uniaxial Compressive Strength Of Rock Using Support Vector Machine Algorithm written by Hafedz Zakaria and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Concrete Strength Prediction Modeling Based On Support Vector Machine Svm

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Author : Santosh Dhakal
language : en
Publisher:
Release Date : 2015

Concrete Strength Prediction Modeling Based On Support Vector Machine Svm written by Santosh Dhakal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Concrete categories.

Prediction Of 28 Day Strength Of Concrete By Using Early Age Methods

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Author : Babu R. Suresh
language : en
Publisher:
Release Date : 1989

Prediction Of 28 Day Strength Of Concrete By Using Early Age Methods written by Babu R. Suresh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with categories.

Predicting The 28 Day Compressive Strength Of Concrete At An Age Of 24 Hours

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Author : National Building Research Institute (South Africa)
language : en
Publisher:
Release Date : 1973

Predicting The 28 Day Compressive Strength Of Concrete At An Age Of 24 Hours written by National Building Research Institute (South Africa) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1973 with categories.

Relation Of 7 Day To 28 Day Compressive Strength Of Mortar And Concrete

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Author : John Whittemore Gowen
language : en
Publisher:
Release Date : 1926

Relation Of 7 Day To 28 Day Compressive Strength Of Mortar And Concrete written by John Whittemore Gowen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1926 with Concrete categories.

Predicting Potential Strength Of Portland Cement Concrete

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Author : West Virginia. Department of Highways
language : en
Publisher:
Release Date : 1978

Predicting Potential Strength Of Portland Cement Concrete written by West Virginia. Department of Highways and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Concrete categories.

Estimation Of 28 Day Compressive Strength Of High Strength Concrete Based On 7 Day Compressive Strength With Artificial Neural Network And Regression Methods And Results Comparison

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Author : Fathollah Sajedi
language : en
Publisher:
Release Date : 2009

Estimation Of 28 Day Compressive Strength Of High Strength Concrete Based On 7 Day Compressive Strength With Artificial Neural Network And Regression Methods And Results Comparison written by Fathollah Sajedi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with High strength concrete categories.

Artificial Neural Network To Predict The Compressive Strength Of Semilightweight Concrete Containing Ultrafine Ggbs

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Author : P. Parthiban
language : en
Publisher:
Release Date : 2020

Artificial Neural Network To Predict The Compressive Strength Of Semilightweight Concrete Containing Ultrafine Ggbs written by P. Parthiban and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Lightweight concrete categories.

Design strength is usually determined after a 28-day curing period as per codal provisions. The prediction of compressive strength before curing reduces waiting time and expedites regular construction activity. The aim of this study is to develop a neural network model to predict the 28-day compressive strength of semilightweight concrete (sLWC) containing ultrafine ground granulated blast-furnace slag (UFGGBS). In this investigation, a novel lightweight coarse aggregate that is made up of wood ash was used to prepare sLWC. Six input parameters, such as cement, UFGGBS as cement replacement, lightweight wood ash pellets as coarse aggregate, fine aggregate, water content, and superplasticizer, were used to train the model. The 28-day compressive strength was taken as an output parameter. A total of 384 data was collected from 24 sLWC mixes, each containing 16 specimens, and trained in an artificial neural network (ANN) using a feedforward-backpropagation model. Trained data were validated with a set of tested data. The correlation coefficient R 2 values for trained and tested data were 0.932 and 0.917, respectively, with least errors. The study concluded that ANN was a reliable and fast tool for predicting the compressive strength of sLWC. It also efficiently reduced cost and time.