Journal
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
Volume 85, Issue -, Pages 117-129Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.soildyn.2016.03.015
Keywords
Earthquake ground motions; Time series prediction; Multi-step prediction; Empirical mode decomposition; Extreme learning machine; Seismic responses of SDOF systems
Funding
- National Natural Science Foundation of China [51478086, 11332004]
- Key Laboratory Foundation of Science and Technology Innovation in Shaanxi Province (State Key Laboratory Base of Ecohydraulic Engineering in Arid Area, Xi'an University of Technology) [2013SZS02-K02]
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This paper proposes a new multi-step prediction method of EMD-ELM (empirical mode decomposition extreme learning machine) to achieve the short-term prediction of strong earthquake ground motions. Firstly, the acceleration time histories of near-fault ground motions with nonstationary property are decomposed into several components of intrinsic mode functions (IMFs) with different characteristic scales by the technique of EMD. Subsequently, the ELM method is utilized to predict the IMF components. Moreover, the predicted values of each IMF component are superimposed, and the short-term prediction of ground motions is attained with low error. The predicted results of near-fault acceleration records demonstrate that the EMD-ELM method can realize multi-step prediction of acceleration records with relatively high accuracy. Finally, the elastic and inelastic acceleration, velocity and displacement responses of single degree of freedom (SDOF) systems are also predicted with satisfactory accuracy by EMD-ELM method. (C) 2016 Elsevier Ltd. All rights reserved.
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