Prediction of Mechanical Properties of Light Weight Brick Composition Using Artificial Neural Network on Autoclaved Aerated Concrete (original) (raw)
Related papers
Journal of Scientific Research, Education, and Technology (JSRET)
This research aims to find the optimal weight optimization algorithm and number of hidden nodes that can be used in Artificial Neural Network to predict mechanical properties (density and compressive strength) of Autoclaved Aerated Concrete (AAC) lightweight brick. The dataset is obtained from secondary source, with a total of 51 data points. From this dataset, the relationship between constituent elements of AAC with its density and compressive strength is modeled using ANN. It was found that the best weight optimization algorithm that can be used for this dataset is the LBFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) algorithm. The optimum hidden layer node is found to be 90 nodes. With this parameters, the ANN can predict density and compressive strength of AAC lightweight brick with accuracy of 93.51% and margin of error around 6.49%. The accuracy of the prediction can be improved by appending the dataset with data points from secondary sources or by doing more experimen...
Prediction of compressive strength of concrete using artificial neural network
The present-day structures are being made with the use of a number of different building materials with varying strength properties that govern their mechanical strength and correspondingly their durability and life. There are a number of techniques available to determine the compressive strength of these materials. However, use of an artificial neural network provides a non-destructive way to predict the compressive strength of the same. In the present study, the compressive strength of concretes prepared with two different cement types i.e. PPC and PSC with manufactured sand and natural sand as aggregate for four different water-cement (w/c) ratios have been undertaken using an artificial neural network (ANN). The predicted strength was compared with that obtained in the laboratory for the same.
This paper presents artificial neural network (ANN) based model to predict the compressive strength of concrete containing Industrial Byproducts at the age of 28, 56, 90 and 120 days. A total of 71 specimens were casted with twelve different concrete mix proportions. The experimental results are training data to construct the artificial neural network model. The data used in the multilayer feed forward neural network models are arranged in a format of ten input parameters that cover the age of specimen, cement, Fly ash, Silica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and Superplasticizer. According to these parameter in the neural network models are predicted the compressive strength values of concrete containing Industrial Byproducts. This study leads to the conclusion that the artificial neural network (ANN) performed well to predict the compressive strength of high performance concrete for various curing period.
Research Review and Modeling of Concrete Compressive Strength Using Artificial Neural Networks
2016
As the construction industry is flourishing day by day, modelling techniques are becoming more and more important in making predictions. Artificial neural network is one of the techniques through which these predictions can be made with limited errors. This research paper deals with the prediction of the compressive strength of concrete using artificial neural network (ANN). The parameters under consideration were different grades of concrete (M-20 and M-30), different curing techniques that are commonly used during the construction of a building (sprinkling, ponding etc.), duration of curing and ageing of the concrete block samples (cubes and cylinders). These parameters were given as input to train the network for the output compressive strength obtained experimentally. Different weights were obtained for the network layer which were used for getting the target value. The network formed was validated for the compressive strength of concrete for the required sample by giving inputs...
Materials
Lightweight concrete (LWC) is a group of cement composites of the defined physical, mechanical, and chemical performance. The methods of designing the composition of LWC with the assumed density and compressive strength are used most commonly. The purpose of using LWC is the reduction of the structure’s weight, as well as the reduction of thermal conductivity index. The highest possible strength, durability and low thermal conductivity of construction materials are important factors and reasons for this field’s development, which lies largely in modification of materials’ composition. Higher requirements for construction materials are related to activities aiming at environment protection. The purpose of the restrictions is the reduction of energy consumption and, as a result, the reduction of CO2 emission. To limit the scope of time-consuming and often high-cost laboratory works necessary to calibrate models used in the test methods, it is possible to apply Artificial Neural Networ...
This research work focuses on development of Artificial Neural Network (ANN) in prediction of compressive strength of lightweight foamed concrete (LFC) after 28 days. A total of 280 different data sets of LFC were collected from the technical literature. Training data set comprises 180 data entries, and the remaining data entries (100) are divided between the validation and testing sets. The ANN method can capture complex interactions among input/output variables in a system without any prior knowledge of the nature of these interactions and without having to explicitly assume a model form. Indeed, such a model form is generated by the data points themselves. This paper describes the database assembled, the selection and training process of the ANN model, and its validation. Results showed that production plastic density, sand and cement ratio, particle size distribution of sand can be predicted and compressive strength of foamed concrete mixtures much accurately, easy and fast. The...
Materials
In a fast-growing population of the world and regarding meeting consumer’s requirements, solid waste landfills will continue receiving a substantial amount of waste. The utilization of solid waste materials in concrete has gained the attention of the researchers. Ceramic waste powder (CWP) is considered to be one of the most harmful wastes for the environment, which may cause water, soil, and air pollution. The aim of this study was comprised of two phases. Phase one was based on the characterization of CWP with respect to its composition, material testing (coarse aggregate, fine aggregate, cement,) and evaluation of concrete properties both in fresh and hardened states (slump, 28 days compressive strength, and dry density). Concrete mixes were prepared in order to evaluate the compressive strength (CS) of the control mix, with partial replacement of the cement with CWP of 10 and 20% by mass of cement and 60 prepared mixes. However, phase two was based on the application of the arti...
In the present study the artificial neural network is used to predict the properties of fly ash and granulated blast furnace slag mixed compressed bricks. Bricks containing fly ash, different proportion of granulated blast furnace slag with constant quantity of cement are fabricated. The bricks are cured by three ways such as, by sprinkling water, by dipping into the alkaline water, and by immersing into the acidic water for 3, 7 and 28 days respectively. After curing, the bricks are tested to determine its compressive strength, water absorption and pH. The results from the experiments were used for the training of the artificial neural network neurons. Using the trained artificial neural network, the values for the composition 0% to 60% were obtained with 5% increment in every interval. It is observed that as the content of granulated blast furnace slag increases than the fly ash content, the compressive strength increases. With the increase in granulated blast furnace slag content the water absorption reduces and pH increases and hence increases the resistance against acid attack.
Acta Scientiarum. Technology, 2016
Currently, artificial neural networks are being widely used in various fields of science and engineering. Neural networks have the ability to learn through experience and existing examples, and then generate solutions and answers to new problems, involving even the effects of non-linearity in their variables. The aim of this study is to use a feed-forward neural network with back-propagation technique, to predict the values of compressive strength and modulus of elasticity, at 28 days, of different concrete mixtures prepared and tested in the laboratory. It demonstrates the ability of the neural networks to quantify the strength and the elastic modulus of concrete specimens prepared using different mix proportions.
USING THE ARTIFICIAL NEURAL NETWORKS FOR PREDICTING COMPRESSIVE STRENGTH OF NORMALLY CONCRETES
IAEME PUBLICATION, 2020
In this study Artificial Neural Networks (ANNs) models were developed for predicting the compressive strength, at the age of 28 days, of normally concretes. The experimental results used to construct the models were gathered from laboratory of Isra University - Amman in 2019. Total of 15 experimental design used for modeling ANN models. 80% in the training set, and 10% in the testing set, and 10% in the validation set. To construct the model, three input parameters were used to achieve one output parameter, referred to as the compressive strength of normally concrete. The results obtained in both, the training and testing phases strongly show the potential use of ANN to predict 28 days' compressive strength of normally concretes with average accuracy 90% and correlation coefficient 95%