Constructing input to neural networks for modeling temperature-caused modal variability: mean temperatures, effective temperatures, and principal components of temperatures

Zhou, H.F.; Ni, Y.Q.; Ko, J.M.

doi:10.1016/j.engstruct.2010.02.026

Cited by 102Structural Health Monitoring TechniquesStructural Engineering and Vibration AnalysisStructural Engineering and Materials Analysis

Constructing input to neural networks for modeling temperature-caused modal variability: mean temperatures, effective temperatures, and principal components of temperatures

Zhou, H.F., Ni, Y.Q., Ko, J.M.·2010·Engineering Structures

DOI 10.1016/j.engstruct.2010.02.026

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A total of 770 h monitoring data of natural frequencies and temperatures obtained from the Ting Kau Bridge in Hong Kong and their FE model was used to train and test a BPNN model.
“A total of 770 h monitoring data of natural frequencies and temperatures obtained from the Ting Kau Bridge in Hong Kong and their FE model was used to train and test a BPNN model.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [234]
Zhou et al. [225,234] proposed a BPNN-based approach to eliminate the effects of temperature.
“In this regard, Zhou et al. [225,234] proposed a BPNN-based approach to eliminate the effects of temperature.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [234]
The BPNN-based approach could detect the occurrence of damage when the change in damage-induced frequency was as small as 1%.
“Case studies indicated that the approach could detect the occurrence of damage when the change in damage-induced frequency was as small as 1%.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [234]

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Authors: Zhou, H. F., Ni, Y. Q., & Ko, J. M.
Affiliations: Hong Kong Polytechnic University (HK)
Corresponding author: Ni, Y.Q.
ORCID: Zhou, H.F.
Ni, Y.Q.
Venue: Engineering Structures
Publisher: Elsevier BV
Published: 2010-06
Volume: 32
Issue: 6
Pages: 1747-1759
ISSN: 0141-0296 (ISSN-L)
DOI: 10.1016/j.engstruct.2010.02.026
OpenAlex: W2087971550
Cited by: 102
References: 44
Citations by year: 2026: 1 · 2025: 10 · 2024: 4 · 2023: 7 · 2022: 10 · 2021: 12 · 2020: 7 · 2019: 5 · 2018: 9 · 2017: 5 · 2016: 11 · 2015: 2 · 2014: 6 · 2013: 6 · 2012: 4
FWCI: 13.21
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Engineering Structures (publishedVersion)
PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
Funders: Hong Kong Polytechnic University
Hong Kong Government
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Vibration Analysis
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
Keywords: effective temperature, mean temperature, BP neural network, cable-stayed bridges, bridge health monitoring

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Constructing input to neural networks for modeling temperature-caused modal variability: mean temperatures, effective temperatures, and principal components of temperatures

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