Use of neural networks in the condition monitoring of ground anchorages

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

The GRANIT(1) system is a non-destructive integrity testing method for ground anchorages. It has won two major UK Awards-the Design Council Millennium Product Status in 1999 and the John Logie Baird Award in 1997. It makes use of novel artificial intelligence techniques in order to learn the complicated relationship that exists between an anchorage and its frequency response to an impulse. The GRANIT system has a worldwide patent and is currently licensed to AMEC plc.

It is widely recognised that non-destructive testing methods for ground anchorages need to be developed as a high priority [A National Agenda for Long-term and Fundamental Research for Civil Engineering in the United Kingdom (1992)], with only between 1 and 5% of anchorages currently being monitored in service [British Standard Code of Practice for Ground Anchorages (8081) (1989)] using currently available techniques. The GRANIT system is a solution for this requirement, and this paper describes how the use of artificial intelligence techniques enabled, for the first time, the cross-anchorage diagnosis of ground anchorages, where data taken from one anchorage was used to train a neural network, which was then used to diagnose the condition of an adjacent anchorage.

The results presented in this paper describe the training of a neural network on data taken from a bolt anchorage, and the diagnosis, using this neural network, of further test data taken from the same anchorage. Data taken from an adjacent anchorage of similar construction is also presented to the neural network, and the cross-anchorage diagnosis of the load level of the second anchorage is achieved. (C) 2003 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)753-761
Number of pages8
JournalAdvances in Engineering Software
Volume34
Issue number11-12
DOIs
Publication statusPublished - Dec 2003

Keywords

  • artificial intelligence
  • condition monitoring
  • dynamic response
  • ground anchorages
  • non-destructive testing
  • vibration

Cite this

@article{a0a4525e94274f77bcf25b528e79dc6e,
title = "Use of neural networks in the condition monitoring of ground anchorages",
abstract = "The GRANIT(1) system is a non-destructive integrity testing method for ground anchorages. It has won two major UK Awards-the Design Council Millennium Product Status in 1999 and the John Logie Baird Award in 1997. It makes use of novel artificial intelligence techniques in order to learn the complicated relationship that exists between an anchorage and its frequency response to an impulse. The GRANIT system has a worldwide patent and is currently licensed to AMEC plc.It is widely recognised that non-destructive testing methods for ground anchorages need to be developed as a high priority [A National Agenda for Long-term and Fundamental Research for Civil Engineering in the United Kingdom (1992)], with only between 1 and 5{\%} of anchorages currently being monitored in service [British Standard Code of Practice for Ground Anchorages (8081) (1989)] using currently available techniques. The GRANIT system is a solution for this requirement, and this paper describes how the use of artificial intelligence techniques enabled, for the first time, the cross-anchorage diagnosis of ground anchorages, where data taken from one anchorage was used to train a neural network, which was then used to diagnose the condition of an adjacent anchorage.The results presented in this paper describe the training of a neural network on data taken from a bolt anchorage, and the diagnosis, using this neural network, of further test data taken from the same anchorage. Data taken from an adjacent anchorage of similar construction is also presented to the neural network, and the cross-anchorage diagnosis of the load level of the second anchorage is achieved. (C) 2003 Elsevier Ltd. All rights reserved.",
keywords = "artificial intelligence, condition monitoring, dynamic response, ground anchorages, non-destructive testing, vibration",
author = "Andrew Starkey and Ana Ivanovic and Neilson, {Richard David} and Rodger, {Albert Alexander}",
year = "2003",
month = "12",
doi = "10.1016/S0965-9978(03)00103-0",
language = "English",
volume = "34",
pages = "753--761",
journal = "Advances in Engineering Software",
issn = "0965-9978",
publisher = "Elsevier Limited",
number = "11-12",

}

TY - JOUR

T1 - Use of neural networks in the condition monitoring of ground anchorages

AU - Starkey, Andrew

AU - Ivanovic, Ana

AU - Neilson, Richard David

AU - Rodger, Albert Alexander

PY - 2003/12

Y1 - 2003/12

N2 - The GRANIT(1) system is a non-destructive integrity testing method for ground anchorages. It has won two major UK Awards-the Design Council Millennium Product Status in 1999 and the John Logie Baird Award in 1997. It makes use of novel artificial intelligence techniques in order to learn the complicated relationship that exists between an anchorage and its frequency response to an impulse. The GRANIT system has a worldwide patent and is currently licensed to AMEC plc.It is widely recognised that non-destructive testing methods for ground anchorages need to be developed as a high priority [A National Agenda for Long-term and Fundamental Research for Civil Engineering in the United Kingdom (1992)], with only between 1 and 5% of anchorages currently being monitored in service [British Standard Code of Practice for Ground Anchorages (8081) (1989)] using currently available techniques. The GRANIT system is a solution for this requirement, and this paper describes how the use of artificial intelligence techniques enabled, for the first time, the cross-anchorage diagnosis of ground anchorages, where data taken from one anchorage was used to train a neural network, which was then used to diagnose the condition of an adjacent anchorage.The results presented in this paper describe the training of a neural network on data taken from a bolt anchorage, and the diagnosis, using this neural network, of further test data taken from the same anchorage. Data taken from an adjacent anchorage of similar construction is also presented to the neural network, and the cross-anchorage diagnosis of the load level of the second anchorage is achieved. (C) 2003 Elsevier Ltd. All rights reserved.

AB - The GRANIT(1) system is a non-destructive integrity testing method for ground anchorages. It has won two major UK Awards-the Design Council Millennium Product Status in 1999 and the John Logie Baird Award in 1997. It makes use of novel artificial intelligence techniques in order to learn the complicated relationship that exists between an anchorage and its frequency response to an impulse. The GRANIT system has a worldwide patent and is currently licensed to AMEC plc.It is widely recognised that non-destructive testing methods for ground anchorages need to be developed as a high priority [A National Agenda for Long-term and Fundamental Research for Civil Engineering in the United Kingdom (1992)], with only between 1 and 5% of anchorages currently being monitored in service [British Standard Code of Practice for Ground Anchorages (8081) (1989)] using currently available techniques. The GRANIT system is a solution for this requirement, and this paper describes how the use of artificial intelligence techniques enabled, for the first time, the cross-anchorage diagnosis of ground anchorages, where data taken from one anchorage was used to train a neural network, which was then used to diagnose the condition of an adjacent anchorage.The results presented in this paper describe the training of a neural network on data taken from a bolt anchorage, and the diagnosis, using this neural network, of further test data taken from the same anchorage. Data taken from an adjacent anchorage of similar construction is also presented to the neural network, and the cross-anchorage diagnosis of the load level of the second anchorage is achieved. (C) 2003 Elsevier Ltd. All rights reserved.

KW - artificial intelligence

KW - condition monitoring

KW - dynamic response

KW - ground anchorages

KW - non-destructive testing

KW - vibration

U2 - 10.1016/S0965-9978(03)00103-0

DO - 10.1016/S0965-9978(03)00103-0

M3 - Article

VL - 34

SP - 753

EP - 761

JO - Advances in Engineering Software

JF - Advances in Engineering Software

SN - 0965-9978

IS - 11-12

ER -