Hybrid intelligent inference model for enhancing prediction accuracy of scour depth around bridge piers

Min Yuan Cheng, Minh Tu Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Bridge-pier scouring is a main cause of bridge failures. Thus, accurately predicting the scour depth around bridge piers is critical, both to specify adequate depths for new bridge foundations and to assess/monitor the safety of existing bridges. This study proposes a novel artificial intelligence (AI) model, the intelligent fuzzy radial basis function neural network inference model (IFRIM), to estimate future scour depth around bridge piers. IFRIM is a hybrid of the radial basis function neural network (RBFNN), fuzzy logic (FL), and the artificial bee Cclony (ABC) algorithm. In the IFRIM, FL is used to handle the uncertainties in input information, RBFNN is used to handle the fuzzy input–output mapping relationships, and the ABC search engine employs optimisation to identify the most suitable tuning parameters for RBFNN and FL based on minimal error estimation. A 10-fold cross-validation method finds that the IFRIM model achieves at least 21% and 14.5% reductions in root mean square error and mean absolute error values, respectively, compared with other AI techniques. Study results support the IFRIM as a promising new tool for civil engineers to predict future scour depth around bridge piers.

Original languageEnglish
Pages (from-to)1178-1189
Number of pages12
JournalStructure and Infrastructure Engineering
Volume11
Issue number9
DOIs
StatePublished - 2 Sep 2015

Keywords

  • artificial bee colony
  • artificial intelligence
  • bridge piers
  • fuzzy logic
  • radial basis function neural networks
  • scour depth

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