Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/116485
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Civil and Environmental Engineering | - |
| dc.creator | Luo, Lanxin | - |
| dc.date.accessioned | 2026-01-02T22:00:43Z | - |
| dc.date.available | 2026-01-02T22:00:43Z | - |
| dc.identifier.uri | https://ows.lib.polyu.edu.hk/s/ows/item/4398 | - |
| dc.identifier.uri | http://hdl.handle.net/10397/116485 | - |
| dc.language.iso | English | - |
| dc.rights | All rights reserved | - |
| dc.subject | Structural Health Monitoring | - |
| dc.subject | Finite Element Models | - |
| dc.subject | Neural Networks | - |
| dc.subject | Nonlinear Boundary Condition | - |
| dc.subject | Structural Dynamics | - |
| dc.title | Resilience and Innovation: Hybrid Modeling in Structural Health Monitoring | - |
| dc.type | Feature Story | - |
| dc.type | OWS | - |
| dcterms.abstract | LUO Lanxin worked on a PhD project in hybrid modeling under the supervision of Prof. Yong Xia, Prof. Limin Sun, and Prof. Yixian Li. He aimed to combine physics-based and data-driven models for improved nonlinear boundary condition identification in structural health monitoring. Initially, Lanxin faced challenges integrating these approaches, but through perseverance and guidance, he found a suitable response solver and built a successful hybrid model. Presenting his work at the IABSE Symposium Tokyo 2025, he received the “Outstanding Young Engineer Contribution Award.” The encouragement from his mentors, peers, and PolyU’s resources, including a presentation course, played a crucial role in his progress. This journey not only enhanced his technical skills but also taught him the value of resilience and collaboration in research. | - |
| dcterms.accessRights | open access | - |
| dcterms.issued | 2025-12 | - |
| dcterms.LCSH | Structural health monitoring | - |
| dcterms.LCSH | Structural analysis (Engineering) | - |
| dcterms.educationalLevel | Postgraduate | - |
| Appears in Collections: | Outstanding Work by Students | |
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