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Research on Chaotic Characteristic and Risk Evaluation of Safety Monitoring Time Series for High Rock Slope

  • Guilan LiangEmail author
Conference paper
Part of the Computational Risk Management book series (Comp. Risk Mgmt)

Abstract

High rock slope engineering is typical nonlinear system, its evolution process is chaotic, dissipated and uncertain. Chaotic system can’t be forecasted for long-term and needs discuss about maximum time scale of predictability. Nonlinear theory is proposed to research maximum time scale of predictability of safety monitoring chaotic time series and construct the model APSO-RBFNN to predict the chaotic time series in maximum time scale of predictability. The largest Lyapunov exponent and maximum time scale is calculate with small data sets method. In the maximum time scale of predictability, the essay applies APSO-RBFNN to chaotic time series for risk assessment. The engineering cases studies reveal that the forecasting values are in good agreement with the measured values and this model has high accuracy and a good prospect for risk assessment of nonlinear chaotic time series of geotechnical engineering.

Keywords

Chaotic time series high rock slope nonlinear theory risk assessment uncertainty analysis 

Notes

Acknowledgments

We are grateful for the monitoring data provided by CHIDI. We also acknowledge the financial support from the National Natural Science Foundation of China Project 50909038, Doctoral Fund of Ministry of Education of China Project 20090094120006, the Fundamental Research Funds for the Central Universities.

References

  1. Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 859–871Google Scholar
  2. Liang GL, Xu WY, Wei J (2007) Wavelet neural network based on adaptive particle swarm optimization and its application to displacement back analysis. Chinese J Rock Mech Eng 1251–1257Google Scholar
  3. Liang GL, Xu WY, He YZ (2008) Study and application of PSO-RBFNN model to nonlinear time series forecasting for geotechnical engineering. Rock Soil Mech 995–1000Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.College of Harbor, Coastal, and Offshore EngineeringHohai UniversityNanjingChina

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