One branch of structural health monitoring (SHM) utilizes dynamic response measurements to assess the structural integrity of civil infrastructures. In particular, modal frequency is a widely adopted indicator for structural damage since its square is proportional to structural stiffness. However, it has been demonstrated in various SHM projects that this indicator is substantially affected by fluctuating environmental conditions. In order to provide reliable and consistent information on the health status of the monitored structures, it is necessary to develop a method to filter this interference. This study attempts to model and quantify the environmental infl uence on the modal frequencies of reinforced concrete buildings. Daily structural response measurements of a twenty-two story reinforced concrete building were collected and analyzed over a one-year period. The Bayesian spectral density approach was utilized to identify the modal frequencies of this building and it was clearly seen that the temperature and humidity fluctuation induced notable variations. A mathematical model was developed to quantify the environmental effects and model complexity was taken into consideration. Based on a Timoshenko beam model, the full model class was constructed and other reduced-order model class candidates were obtained. Then, the Bayesian modal class selection approach was employed to select the one with the most suitable complexity. The proposed model successfully characterizes the environmental influence on the modal frequencies. Furthermore, the estimated uncertainty of the model parameters allows for assessment of the reliability of the prediction. This study not only improves the understanding about the monitored structure, but also establishes a systematic approach for reliable health assessment of reinforced concrete buildings.
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Supported by: Research Committee, University of Macau, China Under Grant No. RG077/07-08S/09R/YKV/FST
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Yuen, KV., Kuok, SC. Modeling of environmental influence in structural health assessment for reinforced concrete buildings. Earthq. Eng. Eng. Vib. 9, 295–306 (2010). https://doi.org/10.1007/s11803-010-0014-4
- Bayesian inference
- model selection
- reinforced concrete building
- structural health monitoring
- temperature and humidity effects
- Timoshenko beam