A Bayesian-based inspection-monitoring data fusion approach for historical buildings and its post-earthquake application to a monumental masonry palace

Abstract

Many countries exposed to high levels of seismic risk, including Italy, are facing a huge challenge in promptly quantifying post-earthquake damages to their built historical heritage. In this context, structural health monitoring (SHM) plays a fundamental role allowing to continuously track changes in selected damage-sensitive features. However, monitoring data interpretation is often not univocal and may be affected by large uncertainty, provoking false positives and false negatives. Hence, this research proposes a novel approach for post-earthquake structural condition assessment by exploiting the aggregation of different sources of information, notably steering from both monitoring and visual inspec- tion campaigns, in order to take risk-informed decisions. More in depth, an automatic tool is proposed to detect and locate structural damages in monumental structures with the aid of a data fusion approach including vibration-based system identification, static and dynamic measurements, finite element (FE) and surrogate modeling, Bayesian-based model updating and visual inspections. In a preliminary phase, potential damage-sensitive regions in the structure are identified through FE-based numerical analysis and engineering judgment. Then, the solution of the inverse problem aimed at deriving the Bayesian posterior statistics of the uncertain parameters is entrusted to a computational-effective surrogate model (SM). Finally, the Bayesian-based updated parameters are adjusted considering the different allowable sources of information to achieve a final assessment. The effectiveness of the proposed approach is demonstrated by using the recorded data acquired in the Consoli Palace, an historical building located in Umbria, central Italy, which has been continuously monitored since 2017 using dynamic, static and environmental sensors and which has been hit by low-intensity earthquakes in 2021.