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Indexed by:期刊论文

Date of Publication:2016-06-01

Journal:SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

Included Journals:SCIE、EI

Volume:85

Page Number:117-129

ISSN No.:0267-7261

Key Words:Earthquake ground motions; Time series prediction; Multi-step prediction; Empirical mode decomposition; Extreme learning machine; Seismic responses of SDOF systems

Abstract: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.