A hybrid fuzzy inference model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons

Min Yuan Cheng, Minh Tu Cao*, Duc Hoc Tran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

The suction caisson is an essential part of the foundation system used in offshore platforms. The failure of a single suction caisson may cause the collapse of an entire offshore system. Hence, accurately predicting the uplift capacity of suction caissons is of critical importance to platform function and reliability. This study proposes the intelligent fuzzy radial basis function neural network inference model (IFRIM) to predict the uplift capacity of suction caissons. IFRIM is a hybrid of the radial basis function neural network (RBFNN), fuzzy logic (FL), and artificial bee colony (ABC) algorithm. In the IFRIM, FL deals with imprecise and uncertain information; RBFNN acts as a supervised learning technique to address fuzzy input-output mapping relationships; and ABC searches for the most appropriate parameter settings for RBFNN and FL. Comparison results show IFRIM to be the fittest model for predicting the uplift capacity of suction caissons in terms of accuracy and reliability. A 10-fold cross-validation approach found that the IFRIM reduced the RMSE and MAPE at least 70% and 90%, respectively, below other tested models.

Original languageEnglish
Pages (from-to)60-69
Number of pages10
JournalAutomation in construction
Volume41
DOIs
StatePublished - May 2014

Keywords

  • Artificial bee colony
  • Artificial intelligence
  • Fuzzy logic
  • Radial basis function neural network
  • Suction caisson
  • Uplift capacity

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