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IRJET- A Review on Application of Artificial Intelligence to Predict Strength of Concrete
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1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 A Review on Application of Artificial Intelligence To Predict Strength of Concrete Mohini Undal1, Dr. P. O. Modani2, Prof. A. S. Gadewar3 1ME student of Structural Engineering, Department of Civil Engineering, PLIT, Maharashtra, India 2Assistant Professor of Structural Engineering, Department of Civil Engineering, PLIT, Maharashtra, India 3Assistant Professor of Structural Engineering, Department of Civil Engineering, PLIT, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract –Our thirst for progress as humans is reflected by our continuous research activities in different areasleadingto many useful emerging applications and technologies.Artificial intelligence and its applications are good examples of such explored fields with varying expectations and realistic results. Generally, artificially intelligent systems have shown their capability in solving real-life problems; particularly in non- linear tasks. Such tasks are often assigned to an artificial neural network (ANN) model to arbitrate as they mimic the structure and function of a biological brain; albeit at a basic level. In this paper, we investigate a newly emerging application area for ANNs; namely structural engineering.We design, implement and test an ANN model to predict the properties of different concrete mixes. Traditionally, the performance of concrete is affected by manynon-linearfactors and testing its strength comprises a destructive procedure of concrete samples. Key Words: Artificial Neural Network, Compressive strength, Durability, Ingredients of concrete. 1. INTRODUCTION Artificial Neural Networks are typical example of modern interdisciplinary subject that helps solving various engineering problem which couldn’t solved by traditional method. Neural network capable of collecting, memorizing, analyzing and processing large number of data gained from some experiment. They are an illustration of sophisticated modeling technique that can be used for solving many complex problems. The trained neural network serves as an analytical tool for qualified prognoses of the results, for any input data which were not included in the learning process of the network. Their operation is simple and easy. An artificial neural network isan emulation of biological neural system. It is developed systematically step by step procedure. Input/output training data is fundamental for this network asitconveys information whichis necessaryto discover the optimal operating point. The weight assigned with eacharrow which represent information arrow, to give more or less strength to the signal which they transmit. The input neuron have only one input, their output will input they received multiplied by weight. The neuron on output layer receives output of both input neuron, multiplied by their respective weight and sums them. Figure 1 Artificial Neural Network 2. REVIEW OF LITERATURE Mahmoud Abuy Yaman,Metwally Abd Elaty,Mohamed Taman (2017)represent self compacting concrete is a highly flow able type of concrete that spreads into form without the need of mechanical vibration. It represents a comparative study between two methodologies which have been applied on two different data sets of SCC mixtures, which were gathered from the literature, using artificial neural network (ANN). The two methodologies aim to get the best prediction accuracy for the SCC ingredients using the 28-day compressive strength and slump flow diameters as inputs of the ANN. In the first methodology, the ANN model is constructed as a multi input – multi output neural network with the six ingredients as outputs. In the second methodology, the ANN model is constructedas a multi input – single output neural net- work where the six ingredient outputs are predicted separately from six different neural networks of multi input – single output type. Also, the influence of the mixes homogeneity on the prediction accuracy is investigated through the second data set. The results demonstrate the superiority of the second methodology in terms of accuracy of the predicted outputs. [1] Volume: 06 Issue: 04 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 73
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 Behrooz Shirgir, Amir Reza Mamdoohi, Abolfazl Hassani(2015) states that pervious concrete is a concrete mixture prepared from cement, aggregates, water, little or no fines, and in some cases admixtures. The hydrological property of pervious concrete is the primary reason for its reappearance in construction. It gives two important aspects of pervious concrete due to permeability and compressive strength are investigated usingartificialneural networks (ANN) based on laboratory data. The proposed network is intended to represent a reliable functional relationship between the input independent variables accounting for the variability of permeability and compressive strength of a porous concrete. Results of the Back Propagation model indicate that the general fit and replication of the data regarding the data points are quite fine. The R-square goodness of fit of predicted versus observed values range between 0.879 and 0.918 for thefinal model; higher values were observed for the permeability as compared with compressive strength and for the train data set rather than the test data set. The findings can be employed to predict these two important characteristics of pervious concrete when there are no laboratorial data available. [2] Palika Chopra, Rajendra Kumar Sharma,Maneek Kumar(August 2015) gives effort hasbeenmadetodevelop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks and Genetic Programming . 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. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN modelwiththe training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool. [3] Neela Deshpande, Shreenivas Londhe, Sushma Kulkarni(2014) gives artificial neural networks have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC) along with two other data driven techniques namely Model Tree (MT) and Non-linear Regression (NLR). Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data). The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cementratio,Water– Cement ratio and Replacement ratio of naturalaggregatesby recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete. [4] Sakshi Gupta (2013) presents application of artificial neural network to develop model for predicting 28 days compressive strength of concrete withpartialreplacementof cement with nano-silica for which the data has been taken from various literatures. The use of nano-particle materials in concrete can add many benefits that are directlyrelatedto the durability of variouscementations materials, besidesthe fact that it is possible to reduce the quantities of cement in the composite. The performance of the model can be judged by the correlation coefficient, mean absolute error and root mean square error have been adopted as the comparative measures against the experimental resultsobtainedfromthe literature. [5] P. Muthupriya, K. Subramanian, B .G. Vishnuram (April 2011) represent artificial neural network for predicting compressive strength of cubes and durability of concrete containing metakaolin with fly ash and silica fume with fly ash are developed at the age of 3, 7, 28, 56 and 90 days. For building these models, training and testing using the available experimental results for 140 specimens produced with 7 different mixture proportions are used. Thedataused in the multi-layer feed forward neural networks models are designed in a format of eight input parameters covering the age of specimen, cement, metakaolin (MK), fly ash (FA), water, sand, aggregate and super plasticizer and in another set of specimen which contain SF instead of MK.Accordingto these input parameters, in the multi-layer feed forward neural networks models are used to predict thecompressive strength and durability values of concrete. It shown that neural networks have high potential for predicting the compressive strength and durability values of the concretes containing metakaolin, silica fume and fly ash. [6] Marai M Alshihri, Ahmed M Azmy, Mousa S E1-Bisy (2008) this investigation, the neural networks are used to predict the compressive strength of light weight concrete (LWC) mixtures after 3, 7, 14, and 28 days of curing. Two models namely, feed-forward back propagation (BP) and cascade correlation (CC), were used. The compressive strength was modeled as a function of eight variables: sand, water/cement ratio, lightweightfine aggregate, lightweight coarse aggregate, silica fume used in solution, silica fume used in addition to cement, super plasticizer, and curing period. It isconcluded that neural networkmodelpredicated © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 74
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 slightly accurate results and learned very quickly as compared to the BP procedure. This studyindicatedthatthe neural networks models are sufficient tools for estimating the compressive strength of LWC. This will reduce the cost and save time in this class of problems. [7] Jerzy Hola, Kezysztof Schabowcia (2005) deals with neural identification ofcompressive strength ofconcrete on basis on non-destructivity determined parameter. Basic information of neural network and types of artificial neural network most suitable for analysis of experimental results are given. A set of experimental data for training and testing covers compressive strength ranging from 24 to 105.The results predict that artificial neural network highly suitable for accessing compressive strength of concrete. [8] I. C. Yeh (1998) is demonstrating possibilities of adapting ANN to predict compressive strength of high performance concrete .A set of trial batches with HPC produced in laboratory & demonstrate satisfactory experimental results whichconclude that a strength based on ANN moreaccurate than model based on regression & it is easy, convenienttous ANN model.[9] 3. CONCLUSIONS Artificial Neural Network (ANN) model is a reliable computational model to solve different complex problems such as prediction problems. The neural network can be used for a particular problem when deviationintheavailable data is expected and accepted and also when a defined methodology is not available .Therefore, in order to predict the properties of concrete with high reliability, instead of using costly experimental investigation, Artificial Neural Network model can be replaced. The neural network model to predict compressive strength of concrete specimens is utilized in this study. The prediction from values of average percentage error Artificial Neural Network shows a high degree of consistency with experimentally evaluated compressive strength of concrete specimens used. Thus, the present study suggests an alternative approach of compressive strength assessment againstdestructivetesting methods. REFERENCES [1] Mahmoud Abu Yaman, Metwally Abd Elaty, Mohamed Taman, “Predicting the ingredients of self compacting concrete using artificial neural network,” Alexandria Engineering Journal, (2017)56,523-532. [2] Behrooz Shirgir, Amir Reza Mamdoohi,AbolfazlHassani, “Prediction of Pervious Concrete Permeability and Compressive Strength Using ArtificialNeuralNetworks,” International Journal of Transportation Engineering, Vol.2/ No.4/ spring 2015. [3] Palika Chopra, Rajendra Kumar Sharma and Maneek Kumar, “Prediction o Compressive Strength ofConcrete Using Artificial Neural Network and Genetic Programming,” Hindwai Publishing Corporation, Advances in Materials Science and Engineering Volume 2016, Article ID 467, August 2015. [4] Neela Deshpande, ShreenivasLondhe, SushmaKulkarni, “Modeling compressive strength of recycled aggregate concrete by artificial neural network, model tree and non-linear regression,” International Journal of Sustainable Built Environment (2014)3,187-198. [5] Sakshi Gupta, “Using Artificial Neural Network to Predict the Compressive StrengthofConcretecontaining Nano-silica,”Civil Engineering andArchitecture1(3):96- 102, 2013. [6] P. Muthupriya, K. Subramanian, B. G. Vishnuram, “PREDICTION OF COMPRESSIVE STRENGTH AND DURABILITY OF HIGH PERFORMANCE CONCRETE BY ARTIFICIAL NEURAL NETWORKS,”InternationalJournal of Optimization In Civil Engineering, April 2011; 1:189- 209. [7] Marai M. Alshihri, Ahmd M. Azmy, Mousa S. E1-Bisy, “Neural networks for predictingcompressivestrengthof structural light weight concrete”, Construction and Building Materials 23(2009) 2214-2219, 4 January 2009. [8] Jerzy Hola & Krzysztof Schabowica, “APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO DETERMINE CONCRETECOMPRESSIVESTRENGTHBASEDONNON- DESTRUCTIVE TESTS,” Journal of Civil Engineering and Management 1822-360, 21 Feb 2005. [9] I.C.Yeh, “MODELING OF STRENGTH OF HIGH- PERFORMANCECONCRETEUSINGARTIFICIALNEURAL NETWORKS,” Cement and Concrete Research, September 14, 1998. © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 75
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