Data-driven numerical model updating of reinforced concrete structures using artificial neural networks (Record no. 25578)

MARC details
000 -LEADER
fixed length control field 02835nas a2200385 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260818085742.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260202s20259999th u ms t 000 eng d
035 ## - SYSTEM CONTROL NUMBER
System control number .b12474095
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number AIT Thesis no.ST-25-12
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Peerawut Watsaratiyanont
245 10 - TITLE STATEMENT
Title Data-driven numerical model updating of reinforced concrete structures using artificial neural networks
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Pathum Thani, Thailand :
Name of publisher, distributor, etc. Asian Institute of Technology,
Date of publication, distribution, etc. 2025
300 ## - PHYSICAL DESCRIPTION
Extent 79 leaves :
Other physical details ill.+
Accompanying material 1 online resource
490 1# - SERIES STATEMENT
Series statement Thesis ;
Volume/sequential designation no. ST-25-12
500 ## - GENERAL NOTE
General note A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Structural Engineering
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M. Eng.) - Asian Institute of Technology, 2025
520 ## - SUMMARY, ETC.
Summary, etc. This research develops and evaluates a data-driven model updating framework for reinforced concrete (RC) building structures using Artificial Neural Networks (ANN) and the 3D Applied Element Method (AEM). The proposed method aims to improve the accuracy of gradient-based model updating by providing more realistic initial estimates of material properties based on measured modal parameters.Databases were generated by varying Young{u2019}s modulus in 3D AEM models and recording corresponding modal responses. ANN models trained on these databases achieved high predictive accuracy in low- and moderate-resolution configurations. Verification on a controlled numerical model demonstrated improved convergence and robustness compared to conventional random-initial-guess approaches. Validation on two experimental case studies showed that the method produces updated models with plausible material properties and improved agreement with measured modal data.The results confirm that the proposed framework effectively integrates data-driven prediction with numerical updating, providing a practical and efficient approach for RC building model updating.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Reinforced concrete
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Concrete construction
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial Intelligence
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Krishna, Chaitanya,
Relator term Chairperson
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Pennung Warnitchai,
Relator term Examination Committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Panon Latcharote,
Relator term Examination Committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Raktipong Sahamitmongkol,
Relator term Examination Committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Royal Thai Government Fellowship,
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-25-12
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23418">http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B23418</a>
907 ## - LOCAL DATA ELEMENT G, LDG (RLIN)
a .b12474095
b mnarc
c a
902 ## - LOCAL DATA ELEMENT B, LDB (RLIN)
a 260209
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Operator's initials, OID (RLIN) 0
Cataloger's initials, CIN (RLIN) 260205
First date, FD (RLIN) m
-- h
-- a
-- 0
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnarc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 67-Electronic Resource
909 ## - LOCAL ITEMS USED
Barcode Barcode : -
CREATED CREATED : 2026-02-02
RECORD Id RECORD # : i13570663
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 Total checkouts Full call number Date last seen Copy number Price effective from Koha item type
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 18/08/2026   AIT Thesis no.ST-25-12 18/08/2026 1 18/08/2026 67-Electronic Resource
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