Prediction of Pile Bearing Capacity Using Artificial Neural Networks

In Mo Lee, Jeong Hark Lee

Research output: Contribution to journalArticlepeer-review

220 Citations (Scopus)


It is well known that the human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation neural networks were utilized to predict the ultimate bearing capacity of piles. For the verification of applicability of neural networks, results of model pile load tests performed by the authors were simulated. In addition, the results of in situ pile load tests obtained from a literature survey were also used. The results showed that the maximum error of prediction did not exceed 25%, except for some bias data. These limited results indicated the feasibility of utilizing neural networks for pile capacity prediction problems.

Original languageEnglish
Pages (from-to)189-200
Number of pages12
JournalComputers and Geotechnics
Issue number3
Publication statusPublished - 1996

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Computer Science Applications


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