Arch Dam Deformation Prediction Model Based on PSO-SVM

Jing Yang Liu; He Zhi Liu

doi:10.4028/www.scientific.net/AMM.351-352.1306

Paper Titles

Analysis and Experimental Study of Concrete Strength Detection
p.1289

Study on Seismic Response Control of Self-Anchored Suspension Bridge with TMD
p.1293

Design and Implement of the Urban Bridge Cluster Monitoring Online Assessment System
p.1298

Back Analysis on Deformation of Concrete Faced Rockfill Dam Based on Prototype Monitoring
p.1302

Arch Dam Deformation Prediction Model Based on PSO-SVM
p.1306

Experimental Study of Infrared Thermography for Detecting Internal Defect of Mortar Finishing Layer
p.1312

Research on Dam Deformation Analysis Model Considering Multi-Factors
p.1318

Research on Cable Force Testing Method of Cable-Stayed Bridge Model Test
p.1325

The Research of Several Problems that Merit Attention in Use of Rebar-Planting Anchorage Technique for Reforming and Strengthening Buildings
p.1333

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 351-352Arch Dam Deformation Prediction Model Based on...

Arch Dam Deformation Prediction Model Based on PSO-SVM

Abstract:

Arch dam has gradually evolved as one of dam type as main large-scale hydraulic project, dam deformation prediction is an important part of dam safety monitoring, and it is difficult to forecast because of the complicated nonlinear characteristics of the monitoring data. Support Vector Machine (SVM) could solve the small sample, nonlinear high dimension problem due to the excellent generalization ability, and hence it has been widely used in the forecast of arch dam deformation. However, the forecast results considerably depend on the choice of SVM model parameters. In this paper, Particle Swarm Optimization (PSO), which has the characteristic of fast global optimization, was applied to optimize the parameters in SVM, and then the dam deformation prediction model based on PSO-SVM could be established. The model is applied to a certain arch dam foundation prediction. The accuracy of this employed approach was examined by comparing it with multiple regression method. In a word, the experimental results indicate that the proposed method based on PSO-SVM can be used in arch dam deformation prediction.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 351-352)

Pages:

1306-1311

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.351-352.1306

Citation:

Cite this paper

Online since:

August 2013

Authors:

Jing Yang Liu, He Zhi Liu

Keywords:

Arch Dam, Deformation Prediction, Particle Swarm Optimization (PSO), Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Z.R. WU, etal: Hydraulic Structures Safety Control Theory and Application (Hohai University Press, China 1990).

Google Scholar

[2] A.W. MINNS, M. J HALL: Hydrological Science Journal, Vol 41(1996), pp.399-417.

Google Scholar

[3] V. Vapnik: Statistical learning theory (Wiley, New York, 1998).

Google Scholar

[4] R. Eberhart, J. Kennedy: A New Optimizer Using Particles Swarm Theory. Proceeding of Sixth International Symposium on Micro Machine and Human Science. (1995) pp.39-43.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[5] J. Kennedy, R. Eberhart: Particle swarm optimization. Proceeding of the 1995 IEEE international conference on neural network (1995) p.1942-(1948).

Google Scholar

[6] Y.H. SHI, R.C. Eberhart. Empirical study of particle swarm optimization. Proceedings of Congress on Evolutionary Computation, 1999, p.1945-(1950).

Google Scholar

[7] V. Vapnik: The nature of statistical learning theory (Springer-Verlag, New York, 1995).

Google Scholar

[8] J.A.K. Suykens, J. Vandewalle: Neural Processing Letters, Vol. 9(1999), pp.293-300.

Google Scholar