dc.creator | Teymouri D., Sedehi O., Katafygiotis L.S., Papadimitriou C. | en |
dc.date.accessioned | 2023-01-31T10:07:28Z | |
dc.date.available | 2023-01-31T10:07:28Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://hdl.handle.net/11615/79660 | |
dc.description.abstract | In this paper, a recursive Bayesian-filtering technique is presented for the joint estimation of the state and input forces. By introducing new prior distributions for the input forces, the direct transmission of the input into the state is eliminated, which allows removing low-frequency error components from the predictions and estimations. Eliminating such errors is of practical significance to the emerging fatigue monitoring methodologies. Furthermore, this new technique does not require a priori knowledge of the input covariance matrix and provides a powerful method to update the noise covariance matrices in a real-time manner. The performance of this algorithm is demonstrated using one numerical example and compared it with the state-of-the-art algorithms. Contrary to the present methods which often produce unreliable and inaccurate estimations, the proposed method provides remarkably accurate estimations for both the state and input. © 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019. All rights reserved. | en |
dc.language.iso | en | en |
dc.source | 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126496522&partnerID=40&md5=94ff3b08cab28e346696129f4562a0f1 | |
dc.subject | Bayesian networks | en |
dc.subject | Covariance matrix | en |
dc.subject | Accurate estimation | en |
dc.subject | Bayesian approaches | en |
dc.subject | Fatigue monitoring | en |
dc.subject | Noise covariance | en |
dc.subject | Prior distribution | en |
dc.subject | Priori knowledge | en |
dc.subject | Recursive Bayesian filtering | en |
dc.subject | State-of-the-art algorithms | en |
dc.subject | Frequency estimation | en |
dc.subject | Seoul National University | en |
dc.title | A new online Bayesian approach for the joint estimation of state and input forces using response-only measurements | en |
dc.type | conferenceItem | en |