Decision support with Structural Health Monitoring
Adverse operational conditions, aging and deterioration are, among others, some of the main threats that structures and infrastructure systems are subjected to throughout their life-cycle. The technological advancements in developing sensors, capable of providing diversified measurements of structural response (e.g. accelerations, strains, temperatures, loads, etc.), have lead to vast scientific and practical developments in the field of Structural Health Monitoring (SHM). Various techniques for translating the raw measurement data into indicators of structural “health” have been made readily available.
Despite these technological advancements, visual inspection still remains the primary, and oftentimes sole, means for condition-based assessment, in the current approach to infrastructure operation and maintenance. SHM systems may be exploited as a complementary source of information on the condition of a system and may serve for supporting decisions regarding the management of infrastructures throughout their life-cycle. However, it is currently difficult to quantify the effect of SHM on optimal operation and maintenance and hence on the total life-cycle cost. The goal of this project is the development of a framework, which employs efficient methods and tools, able to quantify and optimize the Value of Information (VoI) from the SHM systems.
- Antonios Kamariotis, Konstantinos Tatsis, Eleni Chatzi, Kai Goebel, Daniel Straub (2024). A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance. Reliability Engineering & System Safety 242, 109723. https://doi.org/10.1016/j.ress.2023.109723
- Antonios Kamariotis, Luca Sardi, Iason Papaioannou, Eleni Chatzi, Daniel Straub (2023), On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration. Data-Centric Engineering 4, e17. https://doi.org/10.1017/dce.2023.13.
- Antonios Kamariotis, Eleni Chatzi, Daniel Straub (2023), A framework for quantifying the value of vibration-based structural health monitoring, Mechanical Systems and Signal Processing 184, 109708, https://doi.org/10.1016/j.ymssp.2022.109708
- Antonios Kamariotis, Eleni Chatzi, Daniel Straub (2022), Value of information from vibration-based structural health monitoring extracted via Bayesian model updating, Mechanical Systems and Signal Processing 166,108465, https://doi.org/10.1016/j.ymssp.2021.108465
- Kamariotis A., Chatzi E., Straub D. (2022). Quantifying the value of vibration-based structural health monitoring considering environmental variability. In: Proceedings of the 13th International Workshop on Structural Health Monitoring, Stanford University, CA, USA.
- Kamariotis A., Chatzi E., Straub D. (2022). The effect of the likelihood function on the value of SHM extracted via sequential Bayesian updating. In: Proceedings of ICOSSAR 2021-2022, 13th International Conference on
Structural Safety & Reliability, Tongji University, Shanghai, China.
- Kamariotis A., Sardi L., Papaioannou I., Chatzi E., Straub D. (2022). A comparative assessment of online and offline Bayesian estimation of deterioration model parameters. In: Proceedings of IMAC XL, Orlando, Florida, USA.
- Kamariotis A., Chatzi E., Straub D. (2021). Value of Information from SHM via estimating deterioration jump processes with particle filtering. In: Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference (EMI/PMC 2021). Virtual event.
- Kamariotis, A., Straub, D., Chatzi, E. (2020). Optimal maintenance decisions supported by SHM: A benchmark study. In: Seventh International Symposium on Life-Cycle Civil Engineering 2020, Shanghai, China.