PSI - Issue 64

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ScienceDirect

Procedia Structural Integrity 64 (2024) 476–483 Structural Integrity Procedia 00 (2024) 000–000 Structural Integrity Procedia 00 (2024) 000–000

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

SMAR 2024 – 7th International Conference on Smart Monitoring, Assessement and Rehabilitation of Civil Structures Measured displacement data analysis and modelling for structural health monitoring Mohammad Shamim Miah a, ∗ , Werner Lienhart a a Institute of Engineering Geodesy and Measurement Systems, Graz University of Technology, 8010 Graz, Austria Abstract Non-physics-based modeling is gaining attention in the area of structural health monitoring due to underlying advan tages e.g. finite-element or physics-based model might not exist at all. Therefore, in case of aforementioned scenario, it may be useful to have a model that is developed via the use of time-series data instead of knowing the exact underlying physics. Such model can be developed using parametric, non-parametric, and system identification ap proach, where, later, the state-space based model quantities can be derived. In this study, experimentally measured time-series data (e.g. displacement) have been utilized to develop a representative model. Initially, the data has been passed through a screening and selection process including data cleaning and filtering. Subsequently, the filtered data has been validated with the original data to make sure that overall dynamics remain same. Later, cleaned data have been employed to have an illustrative model via employing autoregressive type models. The influence of the model orders have been investigated and a comparison of different results have been presented. Further, the least-squares approach has been adopted to minimizes the prediction errors to optimize overall performance. However, it needs to be mentioned as herein out-only measured data has been used therefore no transfer function can be derived also stabilization plot is not possible without having a transfer function. Finally, the developed models output have been validated in both time and frequency domain. In short, it has been observed that the model output may vary signif icantly depending on the model orders and noise contamination of the original signal. The investigated approach is beneficial for monitoring while physics-based models either not available or not possible to derive. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of SMAR 2024 Organizers. Keywords: Structural health monitoring; Least-squares approach; Autoregressive model; Data-based model; Time and frequency domain SMAR 2024 – 7th International Conference on Smart Monitoring, Assessement and Rehabilitation of Civil Structures Measured displacement data analysis and modelling for structural health monitoring Mohammad Shamim Miah a, ∗ , Werner Lienhart a a Institute of Engineering Geodesy and Measurement Systems, Graz University of Technology, 8010 Graz, Austria Abstract Non-physics-based modeling is gaining attention in the area of structural health monitoring due to underlying advan tages e.g. finite-element or physics-based model might not exist at all. Therefore, in case of aforementioned scenario, it may be useful to have a model that is developed via the use of time-series data instead of knowing the exact underlying physics. Such model can be developed using parametric, non-parametric, and system identification ap proach, where, later, the state-space based model quantities can be derived. In this study, experimentally measured time-series data (e.g. displacement) have been utilized to develop a representative model. Initially, the data has been passed through a screening and selection process including data cleaning and filtering. Subsequently, the filtered data has been validated with the original data to make sure that overall dynamics remain same. Later, cleaned data have been employed to have an illustrative model via employing autoregressive type models. The influence of the model orders have been investigated and a comparison of different results have been presented. Further, the least-squares approach has been adopted to minimizes the prediction errors to optimize overall performance. However, it needs to be mentioned as herein out-only measured data has been used therefore no transfer function can be derived also stabilization plot is not possible without having a transfer function. Finally, the developed models output have been validated in both time and frequency domain. In short, it has been observed that the model output may vary signif icantly depending on the model orders and noise contamination of the original signal. The investigated approach is beneficial for monitoring while physics-based models either not available or not possible to derive. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of SMAR 2024 Organizers. Keywords: Structural health monitoring; Least-squares approach; Autoregressive model; Data-based model; Time and frequency domain © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers

∗ Corresponding author. Tel.: +43-316-873-6328 ; fax: +43-316-873-6820. E-mail address: miah@tugraz.at ∗ Corresponding author. Tel.: +43-316-873-6328 ; fax: +43-316-873-6820. E-mail address: miah@tugraz.at

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 10.1016/j.prostr.2024.09.288 2210-7843 © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of SMAR 2024 Organizers. 2210-7843 © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of SMAR 2024 Organizers.