Prediction of Pile Bearing Capacity Using Artificial Neural Networks

In Mo Lee, Jeong Hark Lee

Research output: Contribution to journalArticle

144 Citations (Scopus)

Abstract

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
Volume18
Issue number3
DOIs
Publication statusPublished - 1996 Dec 1

Fingerprint

Bearing capacity
bearing capacity
artificial neural network
Piles
pile
Neural networks
prediction
back propagation
Circuit theory
Backpropagation
brain
Brain
test

ASJC Scopus subject areas

  • Computer Science Applications
  • Geotechnical Engineering and Engineering Geology

Cite this

Prediction of Pile Bearing Capacity Using Artificial Neural Networks. / Lee, In Mo; Lee, Jeong Hark.

In: Computers and Geotechnics, Vol. 18, No. 3, 01.12.1996, p. 189-200.

Research output: Contribution to journalArticle

@article{49da98b512ba4076a37540bafa4b796d,
title = "Prediction of Pile Bearing Capacity Using Artificial Neural Networks",
abstract = "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.",
author = "Lee, {In Mo} and Lee, {Jeong Hark}",
year = "1996",
month = "12",
day = "1",
doi = "10.1016/0266-352X(95)00027-8",
language = "English",
volume = "18",
pages = "189--200",
journal = "Computers and Geotechnics",
issn = "0266-352X",
publisher = "Elsevier BV",
number = "3",

}

TY - JOUR

T1 - Prediction of Pile Bearing Capacity Using Artificial Neural Networks

AU - Lee, In Mo

AU - Lee, Jeong Hark

PY - 1996/12/1

Y1 - 1996/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0030472893&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030472893&partnerID=8YFLogxK

U2 - 10.1016/0266-352X(95)00027-8

DO - 10.1016/0266-352X(95)00027-8

M3 - Article

VL - 18

SP - 189

EP - 200

JO - Computers and Geotechnics

JF - Computers and Geotechnics

SN - 0266-352X

IS - 3

ER -