Strain prediction for historical timber buildings with a hybrid Prophet-XGBoost model

Abstract

The accurate characterization of environmental effects, especially temperature and humidity, on historical timber buildings is essential for the timely detection of deterioration and structural performance damage. This research aims to effectively exclude environmental impacts in future structural state assessments by constructing an integrated model based on combining the Prophet and extreme gradient boosting (XGBoost) methods. This hybrid model integrates the two single algorithms based on their specialties. Prophet is applied to time series analysis with the addition of humidity effects to characterize their impact on series periodicity, and XGBoost based on improved Bayesian optimization (BayesOpt) is used to describe the effects of ambient temperature and humidity on the structural responses. The hybrid model is constructed by a new optimized weighting method. The performance of the proposed model is compared with that of single models, singular spectrum analysis and polynomial regression (SSA-PR) model and two traditional weighting-based hybrid models on data collected from the structural health monitoring system of the Feiyun Wood Pavilion (FWP). The results show that the inclusion of humidity effects enables the model to more accurately characterize the relationships between environmental factors and strain, and the predictive performance of the proposed hybrid model is better than that of other models.