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Deep Gaussian process regression for damping of a long-span bridge under varying environmental and operational conditions

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Abstract

Modal parameters are critical for wind resistant design and vibrational serviceability assessments of long-span cable-supported bridges. In contrast to the successful research efforts into natural frequencies, there are still challenges in modeling the damping ratio due to the following aspects: (1) inherent errors in damping estimates, (2) lack of insight into the damping mechanisms, and (3) epistemic uncertainties on the effects of environmental and operational conditions (EOCs). This paper proposes a probabilistic regression model for damping using Deep Gaussian Processes (DGP) on damping estimates compiled from 2.5 years of structural health monitoring (SHM) data from a cable-stayed bridge. Input features representative of EOCs theorized to be related to damping ratios from past literature were used. Two data cleaning strategies based on statistics and knowledge-based criteria were used for enhancing the model performance. A comparative study with DGPs and different regression models were carried out to confirm the robustness of DGPs across different datasets. A knowledge-based feature engineering process examined the most significant predictor of the damping ratios. The proposed data-driven regression model can enable a probabilistic consideration of damping in structural design and vibrational serviceability assessments.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government [NRF-2020R1A2B5B01001657 (Doyun Hwang and Ho-Kyung Kim) and RS-2023-00213436 (Sunjoong Kim)] and the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport [RS-2023-00250727 (Doyun Hwang, Sunjoong Kim, and Ho-Kyung Kim)]. The authors would like to thank the Korea Authority of Land and Infrastructure Safety (KALIS) for sharing the Jindo Bridge SHM system data. The photo of the Jindo Bridges was provided by the Jindo County Office and used in accordance with the Korea Open Government License (KOGL). Visualizations were made with Matplotlib [84] and MATLAB version 9.11.0.1873467 (R2021b), with colormaps by Lansey [85].

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Authors and Affiliations

  1. Institute of Construction and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Seoul, 08826, Republic of Korea

    Doyun Hwang & Ho-Kyung Kim

  2. Department of Civil Engineering, University of Seoul, 163 Seoulsiripdae-ro, Seoul, 02504, Republic of Korea

    Sunjoong Kim

  3. Department of Civil and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Seoul, 08826, Republic of Korea

    Ho-Kyung Kim

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DH: methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, review and editing, and visualization. SK: conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, review& editing, and supervision. H-KK: resources, writing—review and editing, project administration, and funding acquisition.

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Correspondence to Sunjoong Kim.

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Hwang, D., Kim, S. & Kim, HK. Deep Gaussian process regression for damping of a long-span bridge under varying environmental and operational conditions. J Civil Struct Health Monit 13, 1431–1445 (2023). https://doi.org/10.1007/s13349-023-00710-8

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