Technical Papers
Jul 23, 2021

Nonlinear Uncertainty Modeling between Bridge Frequencies and Multiple Environmental Factors Based on Monitoring Data

Publication: Journal of Performance of Constructed Facilities
Volume 35, Issue 5

Abstract

Due to the influence of multiple environmental factors, bridge frequencies can vary with time, which can affect the frequency variations that cause structural damage. However, the nonlinear effects of multiple environmental factors and other uncertain effects on structural frequencies cannot be properly considered, which is a major obstacle to achieving bridge damage detection based on structural frequency variations. Therefore, this paper focuses on establishing an appropriate mapping model between modal frequencies and multiple environmental factors, which can consider such nonlinear and uncertain effects simultaneously. Principal component analysis integrates the features of long-term environmental monitoring data into several principal components. To address nonlinearity and uncertainty in modeling, a Gaussian process regression model with principal components as inputs is developed to estimate the modal frequency distributions. Four groups of models with different inputs are validated in a cable-stayed bridge case. The proposed modeling method can map multiple environmental factors onto modal frequencies by considering both nonlinearity and uncertainty and accurately describe the environmental impacts on frequencies based on monitoring data.

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Data Availability Statement

All data, models, or code generated or used during the study are available from the corresponding author by request.

Acknowledgments

This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 52050050, 51978128, and 52078102), the LiaoNing Revitalization Talents Program (Grant No. XLYC1802035), and the Foundation for High Level Talent Innovation Support Program of Dalian (Grant No. 2017RD03).

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Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 35Issue 5October 2021

History

Received: Jan 26, 2021
Accepted: May 17, 2021
Published online: Jul 23, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 23, 2021

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Authors

Affiliations

Kai-Chen Ma [email protected]
Ph.D. Candidate, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]
Ting-Hua Yi, M.ASCE [email protected]
Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China (corresponding author). Email: [email protected]
Dong-Hui Yang [email protected]
Associate Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]
Hong-Nan Li, F.ASCE [email protected]
Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]
Chief Engineer, China Railway Bridge and Tunnel Technologies Co., Ltd., Nanjing 210061, China. Email: [email protected]

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Cited by

  • Continuous Health Assessment of Bridges under Sudden Environmental Variability by Local Unsupervised Learning, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4323, 38, 5, (2024).
  • Early Warning of Abnormal Bridge Frequencies Based on a Local Correlation Model under Multiple Environmental Conditions, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-5467, 28, 2, (2023).
  • Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study, Engineering Structures, 10.1016/j.engstruct.2022.115573, 279, (115573), (2023).
  • Bridge Performance Warning Based on Two-Stage Elimination of Environment-Induced Frequency, Journal of Performance of Constructed Facilities, 10.1061/(ASCE)CF.1943-5509.0001760, 36, 6, (2022).
  • Displacement model error-based method for symmetrical cable-stayed bridge performance warning after eliminating variable load effects, Journal of Civil Structural Health Monitoring, 10.1007/s13349-021-00529-1, 12, 1, (81-99), (2021).

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