Time Reliability of Empirical Models for the Prediction of Building Parameters: The Case of Palazzo Lombardia

Palazzo Lombardia is one of the tallest buildings in Milan and the site of the regional government. For these reasons, some years ago a monitoring system was installed in order to assure its continuous operation. The system is based on accelerometers and clinometers at different floors used for dynamic and static monitoring, respectively. A statistical model was developed, such that the time trend of the first eigenfrequencies of the building were modelled through the trend of the clinometer signals and the root mean square (RMS) of some of the accelerometers. This because it was observed that the clinometer signals and the acceleration RMSs are linked to different environmental variables. As examples: the changes of the static configuration of the building due to sun exposure can be described by clinometer signals and acceleration RMSs can take into account the effect of wind. The use of these signals and indexes simplifies the development of the predictive model, compared to the use of measured environmental quantities. The model showed good performances in foreseeing the trend of the first eigenfrequencies. This paper analyses how the reliability of the model, developed with data acquired in 2015–2016, has changed relying on new data acquired in 2021–2022.