Abstract
Modal frequency is an important indicator for structural health assessment. Previous studies have shown that this indicator is substantially affected by the fluctuation of ambient conditions, such as temperature and humidity. Therefore, recognizing the pattern between modal frequency and ambient conditions is necessary for reliable long-term structural health assessment. In this article, a novel machine-learning algorithm is proposed to automatically select relevance features in modal frequency-ambient condition pattern recognition based on structural dynamic response and ambient condition measurement. In contrast to the traditional feature selection approaches by examining a large number of combinations of extracted features, the proposed algorithm conducts continuous relevance feature selection by introducing a sophisticated hyperparameterization on the weight parameter vector controlling the relevancy of different features in the prediction model. The proposed algorithm is then utilized for structural health assessment for a reinforced concrete building based on 1-year daily measurements. It turns out that the optimal model class including the relevance features for each vibrational mode is capable to capture the pattern between the corresponding modal frequency and the ambient conditions.
Highlights
The goal of structural health monitoring (SHM) is to assess the health status of a structure based on structural responses and ambient conditions measurement.[1]
The proposed algorithm is utilized for structural health assessment for a reinforced concrete building based on 1-year daily measurements
In the remaining of this section, aiming for searching the optimal model class with the relevance features for each vibrational mode of the structure, an efficient strategy is proposed for optimization of the hyperparameter vector am along with the prediction-error variance s2m
Summary
The goal of structural health monitoring (SHM) is to assess the health status of a structure based on structural responses and ambient conditions measurement.[1]. The proposed approach is utilized for modal frequency-ambient condition pattern recognition for the monitored structure based on measurement. Toward the goal for reliability enhancement of the prediction model charactering modal frequency-ambient condition pattern, it is aimed to select the suitable relevance features based on the available dataset. Bmml,ei where bmml,ei is the maximum likelihood estimate of bm, i, and its associated feature Xi is retained in the model for modal frequency-ambient condition pattern of the mth mode in equation (2). In the remaining of this section, aiming for searching the optimal model class with the relevance features for each vibrational mode of the structure, an efficient strategy is proposed for optimization of the hyperparameter vector am along with the prediction-error variance s2m. À X^bm) a^m, iS^ m, i ð21Þ where ^bm is obtained by equation (6); S^ m is obtained by equation (7) and S^ m, i is the ith diagonal element of the posterior covariance matrix S^ m
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