Stress–strain modeling of high-strength concrete by the adaptive network-based fuzzy inference system (ANFIS) approach

AbstractIn this study, an adaptive network-based fuzzy inference system (ANFIS) approach is presented for modeling of high-strength concrete under uniaxial loading. The ANFIS approach applied to test the data of concrete cylinder test is available in the literature. In this paper, the stress–strain behavior of high-strength concrete subjected to axial load is obtained by using the ANFIS model. It is shown that the present model can predict the stress–strain behavior of concrete accurately by taking into account the effective parameters. The advantage of the proposed approach is that the stress–strain behavior of high-strength concrete can be predicted easily. The results of ANFIS approach are compared with the analytical models given in various studies concerning cylinder tests. The ANFIS approach results given show a good agreement with the experimental results.

[1]  Surendra P. Shah,et al.  Stress-Strain Results of Concrete from CircumferentialStrain Feedback Control Testing , 1995 .

[2]  Osman Gencel,et al.  Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete , 2011 .

[3]  W. Brostow,et al.  Mechanical properties of self-compacting concrete reinforced with polypropylene fibres , 2011 .

[4]  Murat Saatcioglu,et al.  Strength and Ductility of Confined Concrete , 1992 .

[5]  Surendra P. Shah,et al.  Effect of Length on Compressive Strain Softening of Concrete , 1997 .

[6]  Antoine E. Naaman,et al.  STRESS-STRAIN CURVES OF NORMAL AND LIGHTWEIGHT CONCRETE IN COMPRESSION , 1978 .

[7]  Robert Park,et al.  REINFORCED CONCRETE MEMBERS WITH CYCLIC LOADING , 1972 .

[8]  Alper Sezer,et al.  Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology , 2007 .

[9]  Fuat Demir,et al.  Stress–strain modelling of high strength concrete by fuzzy logic approach , 2012 .

[10]  Kuang-Han Chu,et al.  STRESS-STRAIN RELATIONSHIP FOR PLAIN CONCRETE IN COMPRESSION , 1985 .

[11]  R. Park,et al.  Stress-Strain Behavior of Concrete Confined by Overlapping Hoops at Low and High Strain Rates , 1982 .

[12]  Eivind Hognestad,et al.  A STUDY OF COMBINED BENDING AND AXIAL LOAD IN REINFORCED CONCRETE MEMBERS; A REPORT OF AN INVESTIGATION CONDUCTED BY THE ENGINEERING EXPERIMENT STATION, UNIVERSITY OF ILLINOIS, UNDER AUSPICES OF THE ENGINEERING FOUNDATION, THROUGH THE REINFORCED CONCRETE RESEARCH COUNCIL. , 1951 .

[13]  O. Gencel Physical and mechanical properties of concrete containing hematite as aggregates , 2011 .

[14]  J. Mander,et al.  Theoretical stress strain model for confined concrete , 1988 .

[15]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[16]  T. H. Wee,et al.  STRESS-STRAIN RELATIONSHIP OF HIGH-STRENGTH FIBER CONCRETE IN COMPRESSION , 1999 .

[17]  Min Liu,et al.  Using fuzzy neural network approach to estimate contractors’ markup , 2003 .

[18]  Jian-Da Wu,et al.  An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[19]  O. Gencel,et al.  Durability properties of concrete reinforced with steel-polypropylene hybrid fibers , 2012 .