Multiple inelastic mechanisms analysis (MIMA): A simplified method for the estimation of the seismic response of RC frame buildings

In the last decade, a number of methodologies for the seismic performance assessment of existing buildings have been proposed. They are based on comprehensive probabilistic procedures where uncertainties related to ground motions, construction and material quality are explicitly accounted for. Structural analysis is performed to evaluate the seismic demand to the structure through a number of Engineering Demand Parameters (EDPs) (i.e.: interstory drift ratios, floor accelerations, etc.). In this respect, the use of Nonlinear Response History Analyses (NRHAs) appears to be computationally expensive, time demanding and potentially tricky for most practicing engineers. On the other hand, simplified approaches, based on linear static analysis, are affected by strong limitations of use and loss of accuracy towards seismic performance assessment. In this paper, an alternative approach, referred to as Multiple Inelastic Mechanisms Analysis (MIMA), based on the principles of the Displacement-based Assessment (DBA) method, is proposed in the attempt of reducing the complexity related to NRHAs, while maintaining a sufficient accurateness in the estimation of expected losses. MIMA is a practice-oriented approach that provides the seismic response of a structure at different intensity levels. The most important novelties of MIMA with respect to the current state-of-the-art are the possibility of accounting for the contribution of different inelastic mechanisms, the record-to-record variability, and the effects of infills and stairs. In the current version, the proposed approach is specialized for pre-70 RC frame buildings. However, it could be extended to other building types with minor efforts. In this paper, MIMA is applied on a six-stories building, representative of typical RC frame buildings realized in Italy (and other European countries) in the ‘60s and ‘70s. A good accordance between approximate predictions and accurate results (from NRHAs) is observed.