ESTIMATION OF STOCHASTIC DAMPING REDUCTION FACTOR USING MONTE CARLO SIMULATION AND ARTIFICIAL NEURAL NETWORK METHOD

Seismic design methods based on the substitute equivalent linear elastic structure concept are based on the use of high-damping response spectra. They are being used also for the analysis of structures equipped with seismic isolation and energy dissipation systems. Damping is integrated in the response spectra using the so-called Damping Reduction Factors (DRF). It has been proposed in seismic codes to estimate high-damping response spectra from their 5% damping counterpart. The assessed structural damping value for a building over a ground motion may differ considerably from the value specified in the design. Due to the importance of damping in the structure’s seismic performance, the structural damping uncertainties should be taken on consideration in the design step. In this paper, the damping uncertainties effects on DRFs for the estimation of high damping response spectra, are examined. Monte Carlo technique is used to describe the damping uncertainties as a lognormal probability distribution. Artificial Neural Networks (ANN) and nonlinear regression are then applied to integrate the damping uncertainties. A DRF formulation is tentatively proposed to account for uncertainties in damping estimation for different levels of damping uncertainties.