Artificial Neural Network To Predict The Compressive Strength Of Semilightweight Concrete Containing Ultrafine Ggbs
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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.
Artificial Intelligence Neural Network Compressive Strength Prediction Of Recycled Aggregate Concrete Samples
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Author : Abdelaziz Nijem
language : en
Publisher:
Release Date : 2020
Artificial Intelligence Neural Network Compressive Strength Prediction Of Recycled Aggregate Concrete Samples written by Abdelaziz Nijem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Old and demolished structures profusely exist in landfills because they are not being recycled frequently nor being employed correctly. This leads to an increase of construction and demolished wastes (C&D). These demolished structures and blocks can be broken down into smaller components to serve as aggregates (which are called recycled aggregates). Recycled aggregates are not being used regularly because they sometimes have detrimental influence on the compressive strength of concrete. Recycled concrete aggregate (RCA) reduces compressive strength of the concrete samples due to absorption issues related to the type, and age of the old concrete. Increase in water absorption levels leads to reduction in the compressive strength. If this issue is resolved, consumption of natural resources would decrease, and the use of recycled aggregate would increase which has beneficial reflection on the economy and the environment. The objective of this research was to develop a model to predict the compressive strength of concrete containing different percentages of RCA. This research studied the physical properties that reduce compressive strength, and even included the parameters that are aligned to the concrete mixture and treated them as input parameters in a prediction model. The model was created using artificial intelligence neural network. The built model included a specific prediction algorithm which was Bayesian Regularization Backpropagation which can deal with many types of data, even those of the random type. Although, the data was considered as non-linear, the Bayesian probability algorithm was able to determine the pattern between the data and reduce the error by using the error function which was Mean Squared Error. The experimental data was collected from previous published research works in literature. The collection of the data and the evaluation of the model were both built upon specific criteria. The training results showed the success of the model. The model can be used as a tool by engineers to calculate compressive strength when recycled aggregates are added by entering the physical properties of the mixture. The work done here can be extended in the future to cover optimization of mechanical properties of concrete containing RCA.
Concrete Strength Prediction Using Artificial Neural Networks
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Author : Dino Scorziello
language : en
Publisher:
Release Date : 1995
Concrete Strength Prediction Using Artificial Neural Networks written by Dino Scorziello and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Development And Applications Of Artificial Neural Network For Prediction Of Ultimate Bearing Capacity Of Soil And Compressive Strength Of Concrete
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Author : Seyed Jamalaldin Seyed Hakim
language : en
Publisher:
Release Date : 2006
Development And Applications Of Artificial Neural Network For Prediction Of Ultimate Bearing Capacity Of Soil And Compressive Strength Of Concrete written by Seyed Jamalaldin Seyed Hakim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Concrete categories.
Artificial Neutral Networks (ANNs) have recently been widely used to model some of the human activities in many areas of science and engineering. One of the distinct characteristics of the ANNs is its ability to learn from experience and examples and then to adapt with changing situation. ANNs does not need a specific equation from the differs from traditional prediction models. Instead of that, its need enough input-output data. Also, it can continously re-train the new data, so that it can conveniently adapt to new data. The research work focuses on development and application of artificial neural networks in some specific civil engineering problems such as prediction of ultimate bearing capacity of soil and compressive strength of concrete after 28 days. One of the main objectives of this study was the development and application of an ANN for predicting of the ultimate bearing capacity of soil. Hnece, a large training set of actual ultimate bearing capacity of soil cases was used to train the network. A neural network model was developed using 1600 data set of nine inputs including the width foundation, friction angle in three layer, cohession of three layers and depth of first and second layer are selected as input of predicting of ultimate bearing capacity in soil. The model contained a training data set of 1180 cases, a verification data set of 240 cases and a testing data set of 240 cases. The training was terminated when the average training error reached 0.002. Many combinations of layers, number of neurons, activation function, different values for learning rate and momentum were considered and the results were validated using an independent validation data set. Finally 9-15-1 is chosen as the architecture of neural network in study. That means 9 inputs with a set of 15 neurons in hidden layer has the most reaonable agreement architecture. This architecture gave high accuracy and reasonable Mean Square Error (MSE). The network computes the mean squared erroe between the actual and predicted values for output over all patterns. Calculation of mean percentage relative error for training set data, show that artifial neural network predicted ultimate bearing capacity with error of 14.83%. The results prove that the artificial neural network can work sufficiency for predicting of ultimate bearing capacity as an expert system. It was observed that overall construction-related parameters played a role in affecting ultimate bearing capacity, buts especially the parameter "friction angle" play most important role. An important observation is that influencing of the parameters "cohesion" is too less than another parameters for calculating of ultimate bearing capacity of soil.
Estimating The Compressive Strength Of Portland Cement Using Artificial Neural Network
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Author : Henok Hunduma
language : en
Publisher:
Release Date : 2013
Estimating The Compressive Strength Of Portland Cement Using Artificial Neural Network written by Henok Hunduma 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.
The purpose of this thesis is to develop Artificial Intelligence Models to predict the 28-days compressive strength of Portland cement (CCS). Two models, Artificial Neural Network and Fuzzy Logic were created using 4 input parameters of Portland cement that comprise both the physical and chemical characteristics. C3S, C2S, Alkali, and Cement fineness, were used as input variables to predict one outcome of compressive strength. Early strength prediction in the production process instead of waiting 28 days for the test to be completed could significantly improve the quality of the cement and reduce the cost associated with the waiting period. Data collected from literature was applied to predict the compressive strength of Portland cement. A rectangular mold of cement and water was created and kept in a temperature of 20° with 90% relative humidity for 24 hours. The cured sample was then stored in a water bath for 27 days and 6 identical bars were tested. The original data had twenty input parameters of cement with one output of compressive strength. The four most significant input parameters were selected for this particular revision. Out of the 150 generated points 100 were used to train the models while 50 data points were applied in the testing of the system. The average percentage errors achieved were 4.2% and 5.8 % for the fuzzy logic model and ANN model respectively. The results indicated that Artificial Intelligence (AI) could be a useful tool for the prediction of cement strength, and through the application of fuzzy logic algorithms, a more user friendly and more explicit model than the ANN could be produced within successful low error margins.
Predicting Compressive Strength Of Mortar By Using Neural Network
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Author : Thomas Tang Loong Ling
language : en
Publisher:
Release Date : 2008
Predicting Compressive Strength Of Mortar By Using Neural Network written by Thomas Tang Loong Ling and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Concrete categories.
Artificial Neural Networks And Fuzzy Logic Applications In Modeling The Compressive Strength Of Portland Cement
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Author : Sever Can
language : en
Publisher:
Release Date : 2004
Artificial Neural Networks And Fuzzy Logic Applications In Modeling The Compressive Strength Of Portland Cement written by Sever Can and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Fuzzy logic categories.
Portland cement production is a complex process that involves the effect of several processing parameters on the quality control of 28-day cement compressive strength (CCS). There are some chemical parameters like the C3S, C2S, C3A, C4AF, and SO3 contents in addition to the physical parameters like Blaine (surface area) and particle size distribution. These factors are all effective in producing a single quantity of 28-day CCS. The long duration of 28 day CCS test provided the motivation for research on predictive models. The purpose for these studies was to be able to predict the strength instead of waiting for 28 days for the test to be complete. In this thesis, artificial intelligence (AI) methods like artificial neural networks (ANNs) and fuzzy logic were used in the modeling of the 28-day CCS. The two models were compared for their quality of fit and for the ease of application.Quality control data from a local cement plant were used in the modeling studies. The data were separated randomly into two parts: the first one contained 100 data points to be used in training and the second part had 50 data points to be used in testing stages of the models. In this study, four different AI models were created and tested (3 ANN, 1 fuzzy logic). One of the ANN models (Model A) had 20 input parameters in 20x20x1 architecture with testing average absolute percentage error (AAPE) of 2.24%. The other ANN model (Model B) had four input parameters (SO3, C3S, Blaine and total alkali amount) in 4x4x1 architecture with AAPE of 2.41%. Both of the Model A and the Model B were created in the MatLABʼ environment by writinga custom computer code. The last ANN model (Model C) actually refers to 72 differentANN models created in the MatLABʼ neural networks toolbox. In order to obtain a model with the lowest error, different learning algorithms, training functions and architectures in combinations were tested. The lowest AAPE among these models appeared to be 2.31%. The fuzzy logic model (Model D) which had four input parameters (SO3, C3S, Blaine and total alkali amount) was created in the MatLAB fuzzy logic toolbox. In order to write the fuzzy rules, the sensitivity analysis of the Model B was utilized. The AAPE of the Model D was 2.69%. The model was compared with the ANN models for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly andmore explicit model than the ANNs could be produced within successfully low error margins.
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 Networks In Prediction Of Concrete Strength Reduction Due To High Temperature
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Author : Chih-Hung Chiang
language : en
Publisher:
Release Date : 2005
Artificial Neural Networks In Prediction Of Concrete Strength Reduction Due To High Temperature written by Chih-Hung Chiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Experimental Verification Of Slag Cement Mortar Compressive Strength Predicted Using Neural Network Analysis
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Author : Marylynn Li Li Chung
language : en
Publisher:
Release Date : 2008
Experimental Verification Of Slag Cement Mortar Compressive Strength Predicted Using Neural Network Analysis written by Marylynn Li Li Chung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Concrete categories.