Environmental effects on natural frequencies of the San Pietro bell tower in Perugia, Italy, and their removal for structural performance assessment

Continuously identified natural frequencies of vibration can provide unique information for low-cost automated condition assessment of civil constructions and infrastructures. However, the effects of changes in environmental parameters, such as temperature and humidity, need to be effectively investigated and accurately removed from identified frequency data for an effective performance assessment. This task is particularly challenging in the case of historical constructions that are typically massive and heterogeneous masonry structures characterized by complex variations of materials' properties with varying environmental parameters and by a differential heat conduction process where thermal capacity plays a major role. While there is abundance of documented monitoring data highlighting correlations between environmental parameters and natural frequencies in the case of new structures, such as long-span bridges, similar studies for historical constructions are still missing, with only a few literature works occasionally reporting increments in natural frequencies with increasing temperature of construction materials due to the closure of internal micro-cracks in the mortar layers caused by thermal expansion. In order to gain some knowledge on the effects of changes in temperature and humidity on the natural frequencies of slender masonry buildings, the paper focuses on the case study of an Italian monumental bell tower that has been monitored by the authors for more than nine months. Correlations between natural frequencies and environmental parameters are investigated in detail and the predictive capabilities of linear statistical regressive models based on the use of several environmental continuous monitoring sensors are assessed. At the end, three basic mechanisms governing environmentally-induced changes in the dynamic behavior of the tower are identified and essential information is achieved on the optimal location and minimum number of environmental sensors that are necessary in a structural health monitoring perspective.