Assessment of uncertainties in damping reduction factors using ANN for acceleration, velocity and displacement spectra

Abstract

Structure's damping force during an earthquake is very different from what was anticipated during design. This adds uncertainty to the process of designing structures exposed to seismic loads which may be a major cause of significant variation in the seismic response reliability of these structures. This work is focused on the investigation of the structural damping uncertainties effect on the structure’s response spectra through the assessment of uncertainties in the damping reduction factors (DRF) derived from the acceleration, velocity and displacement spectra. An Artificial Neural Networks (ANN) was also developed for the stochastic DRF calculation. The Monte Carlo method, one of the methods of computational algorithms that rely on repeated random sampling to obtain numerical results, is used for the estimation of the stochastic DRF. The obtained results indicates that the difference between the deterministic and the stochastic DRF are around of 21 % for displacement and velocity and 28.7 % for acceleration spectra. As a consequence, the DRF derived from the acceleration spectra is more sensible to the uncertainties inherent on damping than the DRF obtained from displacement and velocity. Therefore, it is important to take this conclusion into account when using these factors previously. The ANN constitutes a sample and efficiency method to predict the stochastic DRF since the error obtained is always less than 6 %. Practice oriented results are searched for, to be incorporated in future seismic standards.

Keywords

• Damping
• Uncertainty
• Damping reduction factor
• Artificial neural networks
• Response spectra