Journal
EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING
Volume 21, Issue 11, Pages 1341-1362Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/19648189.2016.1169225
Keywords
slope reliability analysis; extreme learning machine; artificial bee colony algorithm; surrogate model
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Funding
- Fundamental Research Funds for the Central Universities [DUT15LK11]
- Open Research Fund of the State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology [GZ15207]
- National Natural Science Foundation of China [51109028]
- State Scholarship Fund of China [201208210208]
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Geotechnical engineering problems are characterised by many sources of uncertainty, and reliability analysis is needed to take the uncertainties into account. An intelligent surrogate model based on extreme learning machine is proposed for slope system reliability analysis. The weights and bias which play an important role in the performance of ELM are optimised by a nature inspired artificial bee colony algorithm. The system failure probability of soil slopes is estimated by Monte Carlo simulation via the proposed surrogate model. Experimental results show that the proposed method is feasible, effective and simple to implement system reliability analysis of soil slopes.
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