Development of Bayesian Structural Health Monitoring Methodology and Its Enhancement by Optimal Sensor Placement and Optimal Model Class Selection
- Heung Fai LAM (Principal Investigator / Project Coordinator)Department of Architecture and Civil Engineering
- Jame Leslie BECK (Co-Investigator)
- Qiusheng LI (Co-Investigator)Department of Architecture and Civil Engineering
- Costas Papadimitriou (Co-Investigator)
DescriptionIn recent years, there has been great interest in the development of a structural health monitoring (SHM) methodology using vibration measurements. The value of such a methodology, allowing for early detection of damage in a structure, is obviously immense considering the large number of aging infrastructures in Hong Kong and worldwide. This proposal puts forward a "two-step" Bayesian SHM methodology with particular consideration on the use of optimal sensor placement and optimal model class selection techniques in enhancing the performance of SHM. Bayesian probabilistic approach is followed in both the first and second step to identify the damage feature (such as damage-induced changes in model parameters) and the damage scenario (the damage locations and the corresponding damage severity), respectively. The proposed SHM methodology will be verified first by computer-simulated examples with increasing complexity, and then tested by measurements from a scale-model of a typical Hong Kong building under laboratory conditions.
|Effective start/end date
|1/01/07 → 25/02/10