Data-driven prediction of natural period for existing RC high-rise buildings using probabilistic machine learning methods

Abstract

The natural period of existing reinforced concrete (RC) high-rise buildings is a fundamental structural parameter for dynamic analysis and condition assessment, while the estimation of such structural characteristic is an important task for life-cycle maintenance. A commonly adopted approach for predicting natural periods involves the use of design-oriented empirical formulas. However, as shown by real-world observations in both this study and others, these formulas with limited building features may not fully capture the complexity and uncertainty of the natural period for existing buildings. Therefore, it is desired to develop an alternative method that is both feature-based and data-driven, to predict natural periods accurately. This study proposes a feature-based probabilistic machine learning method to determine the natural periods and quantify the uncertainty intervals of the predictions for existing RC high-rise buildings. Specifically, the proposed method comprises a random-forest method using quantile regression. Through in situ measurements and an extensive literature review, a database comprising over 500 actual records was constructed, including the building features and their dynamic properties. The results show that the proposed method achieves a coefficient of determination of 0.96 and an uncertainty interval confined within 20 % for the test set. These findings indicate that the proposed method may be both more accurate and reliable. Furthermore, an in-depth investigation into the model interpretability was conducted to reveal the relative importance of different building features. The analyses indicate that the building height emerges as the most crucial feature, while the building width and its lateral resistant system may also demonstrate noticeable influence on predicting natural periods.