Estimating Strength of Concrete Using a Genetic Algorithm Combining Operation Tree (GAOT)

Kuan Ting Chen; Li Chen; You Rong Fu; Sing Han Chen

doi:10.4028/www.scientific.net/AMM.325-326.1293

Paper Titles

Automated Visual Inspection for Medicines in Aluminum-Plastic Blister Packaging
p.1267

A Vision Detection Algorithm for LIGA Part Assembly
p.1271

LS_SVM Based on DTW and Sliding Window Pruning Algorithm and its Application
p.1276

Analysis of NMPC Algorithm Based on Reduced Precision Solution Criteria
p.1282

Estimating Strength of Concrete Using a Genetic Algorithm Combining Operation Tree (GAOT)
p.1293

Comparative Safety Analysis of Different Bollard Supporting Structures
p.1297

Acoustic Emission Activity Detector for Crack Location in Steel Structures
p.1301

Increasing the Capacity of Concrete Column with Integrated Permanent Formwork Using Wood-Wool Cement Board
p.1305

Dynamic Analysis of Dam-Break Loss Based on Unascertainty Theory
p.1310

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 325-326Estimating Strength of Concrete Using a Genetic...

Estimating Strength of Concrete Using a Genetic Algorithm Combining Operation Tree (GAOT)

Abstract:

The main purpose of this paper is to propose an incorporating a genetic algorithm (GA) into the Operation Tree (OT), called GAOT, and apply it to estimate the compressive strength of high-performance concrete (HPC). A large number of experimental data were used to compare accuracies of the model building technique. The results show that this novel model, GAOT, can obtain highly nonlinear mathematical equations with low estimating errors for predicting the compressive strength of HPC.

You might also be interested in these eBooks

Manufacturing Engineering and Process II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 325-326)

Pages:

1293-1296

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.325-326.1293

Citation:

Cite this paper

Online since:

June 2013

Authors:

Kuan Ting Chen, Li Chen, You Rong Fu, Sing Han Chen

Keywords:

Back-Propagation Networks, Genetic Algorithm Operation Trees, High-Performance Concrete (HPC), Nonlinear Regression Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.I. Kim, D.K. Kim, M.Q. Feng, and F. Yazdani, "Application of neural networks for estimation of concrete strength." J Mater Cvi Eng., Vol.16, No.3 (2004), pp.257-264.

DOI: 10.1061/(asce)0899-1561(2004)16:3(257)

Google Scholar

[2] I.C. Yeh, "Modeling of strength of high-performance concrete using artificial neural networks." Cement and Concrete Research, Vol. 28, No. 12 (1998a), pp.1797-1808.

DOI: 10.1016/s0008-8846(98)00165-3

Google Scholar

[3] T. Ji, T. Lin, and X. Lin, "A concrete mix proportion design algorithm based on artificial neural networks." cement and concrete research, Vol. 36(2006), pp.1399-1408.

DOI: 10.1016/j.cemconres.2006.01.009

Google Scholar

[4] I.C. Yeh, "Modeling concrete strength with augment-neuron networks." Journal of Materials in Civil Engineering, ASCE, Vol. 10, No. 4 (1998b), pp.263-268.

DOI: 10.1061/(asce)0899-1561(1998)10:4(263)

Google Scholar

[5] I.C. Yeh, "Analysis of strength of concrete using design of experiments and neural networks." Journal of Materials in Civil Engineering, ASCE, Vol. 18, No. 4 (2006), pp.597-604.

DOI: 10.1061/(asce)0899-1561(2006)18:4(597)

Google Scholar

[6] Lien LC, Yeh IC, Cheng MY, Modeling strength of highperformance concrete using genetic algorithms and operation tree. J Technol 21(1):41–54, (2006).

Google Scholar

[7] Davis L., Handbook of genetic algorithms. Van Nostrand Reinhold, New York. (1991)

Google Scholar

[8] Goldberg DE, Genetic algorithms in search optimization and machine learning. Addison–Wesley, Massachusetts. (1989)

Google Scholar