Application of ANN and ANFIS Models in Determining Compressive Strength of Concrete

Concrete compressive strength is recognized as one of the most important mechanical properties of concrete and one of the most significant mechanical properties in determining the quality of the produced concrete. Since the traditional procedures of determining the compressive strength of concrete require time and cost, scholars have always been looking for new methods to replace them with existing traditional methods. In this paper, soft computing methods are investigated for determining the compressive strength of concrete. To be specific, 150 different concrete specimens with various mix design parameters have been built in the laboratory, and the compressive strength of them have been measured after 28 days of curing in the water. Five different concrete mix parameters, (i.e. cement, water to cement ratio, gravel, sand, and microsilica) were considered as input variables. In addition, two soft computing techniques have been used in this study which are Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference (ANFIS) System. Results have shown that both of ANN and ANFIS models are successful models for predicting the compressive strength of concrete. Also, results have shown that ANFIS is more capable than ANN in predicting the compressive strength of concrete.

[1]  Sanjay Sharma,et al.  Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using A , 2012 .

[2]  Mansour Talebizadeh,et al.  Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models , 2011, Expert Syst. Appl..

[3]  Hosein Naderpour,et al.  Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .

[4]  Amir Nasrollahi,et al.  Optimum shape of large-span trusses according to AISC-LRFD using Ranked Particles Optimization , 2017 .

[5]  Jianhua Yan,et al.  Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. , 2017, Waste management.

[6]  Mohammad Mohammadhassani,et al.  Application of ANFIS and LR in prediction of scour depth in bridges , 2014 .

[7]  A. K. Gupta,et al.  Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA , 2017, J. Comput. Des. Eng..

[8]  Mehdi Nikoo,et al.  Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete , 2017, Frontiers of Structural and Civil Engineering.

[9]  Paresh Chandra Deka,et al.  Prediction of Air Temperature by Hybridized Model (Wavelet-ANFIS) Using Wavelet Decomposed Data☆ , 2015 .

[10]  Faezehossadat Khademi,et al.  Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression , 2016 .

[11]  Zahra Keshavarz.  Predicting the Civil Engineering Characteristics through Soft Computing Models , 2017 .

[12]  Faezehossadat Khademi,et al.  Prediction of Compressive Strength of Concrete by Data-Driven Models , 2015 .

[13]  Mehdi Nikoo,et al.  Displacement determination of concrete reinforcement building using data-driven models , 2017 .

[14]  Mahmoud Abu Yaman,et al.  Predicting the ingredients of self compacting concrete using artificial neural network , 2017 .

[15]  Raffaele Landolfo,et al.  Prediction of the flexural overstrength factor for steel beams using artificial neural network , 2014 .

[16]  Jase D. Sitton,et al.  Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks , 2017 .

[17]  T. Jayakumar,et al.  Intelligent Modeling Using Adaptive Neuro Fuzzy Inference System (ANFIS) for Predicting Weld Bead Shape Parameters During A-TIG Welding of Reduced Activation Ferritic-Martensitic (RAFM) Steel , 2013, Transactions of the Indian Institute of Metals.

[18]  N. Anantharaman,et al.  Hydrodynamic studies in fluidized bed with internals and modeling using ANN and ANFIS , 2017 .

[19]  Khader M. Hamdia,et al.  Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS , 2015 .