This paper investigates the use of rank aggregation strategies for the finite element model calibration of monitored masonry structures subjected to earthquakes. Ranking is used to obtain optimal results from several competing optimization strategies, with the final aim of establishing a numerical model of reference to support the existing monitoring systems installed on the structures.
The identification of hysteretic degrading systems exposed to nonstationary loading is a paramount research topic, especially in the case of structures subjected to ground motion excitations. In this paper, the data recorded by a masonry building are used to detect the presence of seismic damage. To this aim, a parametric nonlinear identification is performed by adopting a Bouc– Wen-type multiple oscillator model. Starting from the results of the identification process, a damage index based on the degrading stiffness matrix is defined.
Effective diagnostic and monitoring systems are highly needed in the building and infrastructure sector, to provide a comprehensive assessment of the structural health state and improve the maintenance and restoration planning. Vibration-based techniques, and especially ambient vibration testing, have proved to be particularly suitable for both periodic and continuous monitoring of existing structures. As a general requirement, permanent systems must include a sensing network able to run a continuous surveillance and provide reliable analyses based on different information sources.
One of the main drawbacks of using entropy-based indicators for damage detection is known to be their sensitivity to the energy introduced into the system. Indeed, energy supply can lead to a more deterministic behavior of the structure and thus to a reduction of the entropy. As a solution to these issues, in this paper an indicator based on two measures of spectral entropy is proposed to assess the occurrence of damage in masonry buildings, even in the presence of an external unmeasured input (e.g. minor seismic event).