M. Bettia, G. Boscatob, N. Cavalaglic, A. Cecchib, F. Clementid 

  1. University of Florence, Via di S. Marta 3, 50139, Florence, Italy, 
  2. University IUAV of Venice, Dorsoduro 2206, 30123 Venice, Italy,, 
  3. University of Perugia, Via Duranti 93, 06125 Perugia, Italy, 
  4. Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy,

This mini-symposium aims to open a fruitful discussion on procedures and methodologies for the vibration-based monitoring and dynamic identification of historic constructions laying the foundation for a critical and conscious approach.

 In the last years, the vibration-based monitoring of historic constructions aimed at their preservation against natural hazards, weathering and aging effects of materials and complex structural systems has received growing interest among the scientific community. Thanks to the effectiveness of vibration-based procedures for the identification of their dynamic properties (natural frequencies, mode shapes and damping), the monitoring over time of these quantities can allow early damage and variation of structural integrity detection, highlighted by even small variations of them and of their correlation with other physical agents. 

All this process requires the development of advanced knowledge in solid mechanics and structural dynamics, computational modeling, statistical and stochastic processes, Bayesian procedures and more. In addition, the recent progresses of data sciences through artificially intelligence, neural network, and machine learning techniques while increasing the possibility to carry out vibration-based monitoring also with dense sensors network at the same time increases the complexity and uncertainty of the whole process. 

Recent studies regarding the application of such aspects on historic masonry constructions have reported promising results, though critical issues are still open and deserve to be deeply analyzed and discussed by scientific community. Following topics are considered: 

  • Structural Dynamic identification methods and uncertainty quantification 
  • Continuous dynamic monitoring techniques 
  • Damage detection, localization and quantification 
  • Automated model updating (optimization techniques, genetic algorithms, nonlinear of FE model updating…) 
  • Bayesian methods 
  • Statistical analysis of data monitoring for novelty detection 
  • Artificially Intelligence and Machine Learning techniques