A Bayesian approach to model selection and averaging of hydrostatic-season-temperature-time model

This paper presents a probabilistic approach to model the static dam responses, and monitor its performance using structural health monitoring (SHM) data. In general, the main factors that affects the dam responses are hydrostatic pressure, temperature changes, seasonal variation, and age-related deterioration. In this paper, using these variables two models (i.e., Model-1 and Model-2) have been developed to model, predict and monitor the dam performances. The Model-1 is a parsimonious model based on Bayesian model selection principle and can be applied if long-term monitoring data is available. On the other hand, Model-2 is an ensemble model which accounts for the model uncertainty and can be applied during the initial service-life with a few years of monitoring data. In both cases, the model parameters were estimated and updated using Bayesian method where the prior knowledge and SHM data obtained from dam are integrated. Finally, the model residuals i.e., the difference between the measured and predicted dam responses were used to detect any anomaly in the dam performance. The proposed method is illustrated through the crest displacement data obtained from Dongjiang dam located in China.