Probability models to assess the seismic safety of rigid block-like structures and the effectiveness of two safety devices

Alessandro Contento, Paolo Gardoni, Angelo Di Egidio, Andrea M. De Leo

Research output: Contribution to journalConference articlepeer-review


When subject to earthquakes, some objects and structures, such as statues, obelisks, storage systems, and transformers, show a dynamic behavior that can be modeled considering the object/structure as a rigid block. Several papers have studied the dynamic behavior of both stand-alone rigid blocks and systems where rigid blocks have been paired with safety devices to prevent or delay the overturning of the blocks. Although the safety devices have generally been proven to be effective, their effectiveness changes substantially varying the parameters that characterize the system and the seismic input. This paper compares the seismic responses of stand along rigid blocks with those of blocks coupled with two candidate safety devices: an isolating base and a pendulum mass damper. To account for the relevant uncertainties, probabilistic seismic demand models are developed using a Bayesian approach. The probabilistic models are then used along with the overturning capacities of the blocks to construct fragility curves that give a prediction of the probability of overturning occurrence as a function of some characteristics of the blocks, of the safety devices, as well as of the seismic excitation, i.e. the slenderness of the body and the peak ground acceleration. The data needed to develop the probabilistic model are obtained integrating the nonlinear equations of motion of the two systems subject to selected ground motions. In the end, some numerical examples are proposed.

Original languageEnglish (US)
Pages (from-to)1164-1169
Number of pages6
JournalProcedia Engineering
StatePublished - 2017
Event10th International Conference on Structural Dynamics, EURODYN 2017 - Rome, Italy
Duration: Sep 10 2017Sep 13 2017


  • Rigid block
  • failure probability
  • logistic regression
  • probabilistic models

ASJC Scopus subject areas

  • Engineering(all)


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