Abstract
In practical structural health monitoring systems, the existence of the adhesive nonlinearity may jeopardize the damage diagnosis if the non-damage-related sources are non-negligible. In this work, a system optimization is carried out with the focus on the wave generating process based on a previously established nonlinear shear-lag model, aiming at mitigating the adhesive nonlinearity. The effectiveness of the optimization strategy is validated in both finite element and experimental perspectives. For the former, as the adhesive nonlinearity and the material nonlinearity of plate can be separately introduced to the system, their corresponding nonlinear responses can be directly compared between the unoptimized and the optimized systems. The dominant influence of the material nonlinearity of the plate in the optimized system is highlighted. For the latter, the cumulative effect of the S mode Lamb wave responses is investigated to determine the dominant nonlinear source. Results show that a clear cumulative phenomenon can be observed in the optimized system, which means the adhesive nonlinearity is effectively mitigated.
Original language | English |
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Title of host publication | Structural Health Monitoring 2017 |
Subtitle of host publication | Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017 |
Publisher | DEStech Publications |
Pages | 588-595 |
Number of pages | 8 |
Volume | 1 |
ISBN (Electronic) | 9781605953304 |
Publication status | Published - 1 Jan 2017 |
Event | 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 - Stanford University, Stanford, United States Duration: 12 Sept 2017 → 14 Sept 2017 |
Conference
Conference | 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 |
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Country/Territory | United States |
City | Stanford |
Period | 12/09/17 → 14/09/17 |
ASJC Scopus subject areas
- Health Information Management
- Computer Science Applications