Structure damage detection using neural networks (Record no. 114039)

MARC details
000 -LEADER
fixed length control field 03593nas|a2200409 i 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260818222854.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 150900s2000 th uzm rtt 00| a1eng d
035 ## - SYSTEM CONTROL NUMBER
System control number .b11807696
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number AIT Thesis no.ST-00-05
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Woottigrai Techaungkool
245 10 - TITLE STATEMENT
Title Structure damage detection using neural networks
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Bangkok :
Name of publisher, distributor, etc. Asian Institute of Technology,
Date of publication, distribution, etc. 2000
300 ## - PHYSICAL DESCRIPTION
Extent 85 leaves
490 1# - SERIES STATEMENT
Series statement Thesis ;
Volume/sequential designation no. ST-00-05
500 ## - GENERAL NOTE
General note A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Civil Engineering
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M.Eng.) - Asian Institute of Technology, 2000
520 ## - SUMMARY, ETC.
Summary, etc. Damage detection is a challenging problem that is under vigorous investigation by numerous research groups. When a structure suffers localized damage, its dynamic properties can change. Specially, damage can cause a stiffness reduction, with an inherent reduction in . natural frequencies, and increase in modal damping, and a change to the modal shapes. The development of experimental modal analysis techniques has facilitates the accurate measurement of modal parameters. Alongside this work, several methods have been developed to detect structural parameter change (structural damage) by using location-dependent changes in the modal data. This study focuses on the application of neural networks approach to the assessment of structural damage based on the modal test data. The procedure of using the neural networks to detect structural damage is outlined. A kind of training algorithms called as Back-propagation is used to recognize the modal data from numerical simulations, and the location and the extent of damage of a structure can be recognized by comparison of the outputs from the trained networks fed the modal test data obtained from the structure at the undamage and damage states. Some essential features of this algorithm that influences its searching efficiency are also discussed, especially, several practical concerns involving the structural damage detection are addressed, including the problem of incomplete mode shape measurements, the robustness of detection, and nonlinearity of system. The proposed approach is applied to measured mode data of a real 5 story steel frame. The comparison of numeric results with those from the traditional damage detection method indicates that the proposed method has the potential as a practical tool for a structure damage detection methodology.
650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Structural dynamics
650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Neural networks (Computer science)
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zhu, Hongping,
Relator term Chairperson
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Worsak Kanok-Nukulchai,
Relator term Examination Committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Pennung Warnitchai,
Relator term Examination Committee
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Barry, William Joseph,
Relator term Examination Committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element AIT Partial Scholarship,
Relator term Scholarship donor
810 2# - SERIES ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Asian Institute of Technology.
Title of a work Thesis ;
Volume/sequential designation no. ST-00-05
856 ## - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/detail.php?q=B06830">http://203.159.5.9/ait-thesis/detail.php?q=B06830</a>
907 ## - LOCAL DATA ELEMENT G, LDG (RLIN)
a .b11807696
b mnait
c u
902 ## - LOCAL DATA ELEMENT B, LDB (RLIN)
a 240331
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Operator's initials, OID (RLIN) 0
Cataloger's initials, CIN (RLIN) 000915
First date, FD (RLIN) m
-- a
-- u
-- 0
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnait
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnarc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 22-AIT Thesis (Replacement)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 40-Archives
909 ## - LOCAL ITEMS USED
Barcode Barcode : 30050120747224
CREATED CREATED : 2014-05-30
RECORD Id RECORD # : i12804952
LPATRON LPATRON : 1028145
LCHKIN LCHKIN : 2018-03-09
RENEWALS # RENEWALS : 0
-- # OVERDUE : 0
-- IUSE3 : 0
-- TOT CHKOUT : 1
-- TOT RENEW : 0
909 ## - LOCAL ITEMS USED
Barcode Barcode : 30050160102058
CREATED CREATED : 2016-04-21
RECORD Id RECORD # : i12943769
LPATRON LPATRON : 0
LCHKIN LCHKIN : -
RENEWALS # RENEWALS : 0
-- # OVERDUE : 0
-- IUSE3 : 0
-- TOT CHKOUT : 0
-- TOT RENEW : 0
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Cost, normal purchase price Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library AIT Publications 18/08/2026 50.00   AIT Thesis no.ST-00-05 30050120747224 18/08/2026 3 18/08/2026 22-AIT Thesis (Replacement)
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 18/08/2026     AIT Thesis no.ST-00-05 30050160102058 18/08/2026 1 18/08/2026 40-Archives
คัดลอกแล้ว!