Application of Genetic Algorithms Coupled with Neural Networks to Optimization of Reinforced Concrete Footings

  • Jiin-Po Yeh I-Shou University
  • Shu-Yu Yeh I-Shou University
Keywords: Reinforced Concrete Isolated Footings, Genetic Algorithms, Feedforward Backpropagation Networks, Radial Basis Networks

Abstract

This paper first applies genetic algorithms to optimally design reinforced concrete isolated footings subjected to concentric loading. Based on the ACI Building Code, constraints are built by considering wide-beam and punching shears, bending moment, allowable soil pressure, the development length for deformed bars and clear distance between deformed bars. Design variables consist of the width, length and thickness of the footing and the number of bars in the long and short directions, all of which are discrete. The objective function is to minimize the cost of steel and concrete in the footing. There are totally 144 cases of reinforced concrete isolated footings considered. The optimal results are randomly divided into three groups for the use of neural networks: training data, validation data and testing data. Two kinds of artificial neural networks are employed in this paper: two-layer feedforward backpropagation networks and radial basis networks. Linear regression analysis between the network outputs and targets of the testing data is performed to judge the accuracy of the neural networks. Numerical results show that the feedforward backpropagation network is very effective and has high accuracy with the correlation coefficients and the slope of the regression line being close to one and the y-intercept close to zero. Besides, it is better than the radial basis networks and needs much fewer neurons in the hidden layer.

Author Biographies

Jiin-Po Yeh, I-Shou University

Professor

Department of Civil and Ecological Engineering

Shu-Yu Yeh, I-Shou University

Graduate Student

Department of Civil and Ecological Engineering

References

(1) Holland, J. H. (1975), Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI, USA.

(2) Goldberg, D. E. (1989), Genetic algorithms in search, optimization and machine learning, Addison Wesley, Reading, MA, USA.

-3750.

(12) Rumelhart, D. E., McClelland, J. L. and the PDP Research Group (1986), Parallel distributed processing: explorations in the microstructure of cognition. volume 1: foundations, MIT Press, Cambridge, MA, USA.

(15) Gholizadeh, S. and Salajegheh, E. (2010), "Optimal design of structures for earthquake loading by self organizing radial basis function neural networks," Advances in Structural Engineering, Vol. 13, No. 2, pp. 339-356.

(17) Meon, M. S., Anuar, M. A., Ramli, M. H. M., Kuntjoro, W., and Muhammad, Z. (2012), "Frame optimization using neural network", International Journal Advanced Science Engineering Information Technology, Vol. 2, No. 1, pp. 28-33.

(20) ACI (2008), Building code requirements for structural concrete (ACI 318-08) and commentary (ACI 318R-08), American Concrete Institute, Farminton Hills, MI, USA.

(21) The MathWorks (2015), Global optimization toolbox: user's guide, The MathWorks, Inc., Natick, MA, USA.

Computation, Vol. 66, No. 217, pp. 261-288.

(25) Hagan, M. T., Demuth, H. B. and Beale, M. H. (1996), Neural network design, PWS Publishing, Boston, MA, USA.

Published
2016-09-14