Probabilistic prediction of tunnel geology using a Hybrid Neural-HMM

Sou Sen Leu, Tri Joko Wahyu Adi

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


Uncertain ground conditions represent the primary source of risk in underground tunnel construction. However, this problem can be solved by developing an accurate, probabilistic description of the geology. This paper presents a general model for probability based determination of tunnel geology that can be used as a basis for developing more effective decision support systems for tunneling design and construction. The proposed model is based on a Hidden Markov Model (HMM) and a neural network (NN). An approximate inference technique a Particle Filter (PF) Algorithm is used to simulate the geological parameters. This model overcomes the deficiencies of existing models by readily incorporating all available geologic information and updating geologic predictions based on observations given by the neural network. In order to validate the proposed model, the Drainage Water Tunnel Project at Zhong-He, Taipei, Taiwan was used. The results showed that the Neural-HMM model provides high accuracy in geological prediction.

Original languageEnglish
Pages (from-to)658-665
Number of pages8
JournalEngineering Applications of Artificial Intelligence
Issue number4
Publication statusPublished - Jun 2011
Externally publishedYes


  • Geologic prediction
  • Hidden Markov Model
  • Neural network
  • Particle Filter
  • Tunneling


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