The 2017 New Zealand Society for Earthquake Engineering (NZSEE) guidelines for seismic assessment of buildings recommends using SLaMA before implementing numerical analyses. The method and the NZSEE guidelines have been enhanced from the 2006 version, resulting into an efficient procedure, balancing simplicity and accuracy. This paper presents a numerical study, initiated as part of the development of the SLaMA-2017 method, to investigate the accuracy of the analytical approach via comparison with numerical 2D-pushover on 40 RC frames. SLaMA is effective in capturing the plastic mechanism of the frames, including global or soft-storey mechanisms. Further-yet-simple refinements of the procedure are suggested.

This study investigates the effects of ground-motion sequences on fragility and vulnerability of reinforced concrete (RC) moment-resisting frames (MRFs). Two four-storey, four-bay RC MRFs are selected as case studies. These structures, which share the same geometry, are representative of distinct vulnerability classes in the Mediterranean region and are characterized by different material properties, cross-section dimensions, and detailing. The first case study is a ductile MRF designed according to Eurocode 8 (i.e., a special-code frame), while the second is a non-ductile MRF designed to sustain only gravity loads (i.e., a pre-code frame). The influence of masonry infills on their seismic performance is also investigated. Advanced numerical models are developed to perform cloud-based sequential nonlinear time history analyses using ground-motion sequences assembled by randomly pairing two real records via Latin hypercube sampling. Different structure-specific damage states are considered to derive fragility curves for the undamaged structures, when subjected to a single ground-motion record, and state-dependent fragility curves by considering the additional damage induced by a second ground-motion record within the sequence. Damage-to-loss models are then used to derive mean vulnerability relationships. Results of the analysis show the importance of considering the effect of damage accumulation in the pre-code frames. Moreover, the presence of infills shows an overall positive contribution to the seismic performance of both frame types. Vector-valued vulnerability relationships accounting for the damaging effect of two ground-motion records are finally presented in the form of mean vulnerability surfaces.

Seismic fragility assessment of building portfolios is often based on the analysis of "average" building models representative of structural types (or building classes), thus neglecting building-to-building variability within a structural type. This paper proposes the use of gaussian process (GP) regressions to develop flexible and accurate metamodels explicitly mapping building-class attributes to the seismic fragility parameters. The proposed metamodels can enable analysts to account for building-to-building variability in simulation-based seismic risk assessment of building portfolios. Unlike other commonlyused metamodels, GP regressions do not require the a-priori definition of a prediction function and they quantify the uncertainty on the predictions in a refined and explicit fashion. The proposed method is demonstrated for a portfolio of seismicallydeficient reinforced concrete school buildings with construction details typical of some developing countries. Based on the available information about the building attributes (geometry, materials, detailing), building realisations are generated based on two alternative approaches, which are critically compared: design of experiment and Monte Carlo sampling. Cloud-based time-history analysis for each building realisation is performed using unscaled real ground-motion records; fragility relationships are derived for four structure-specific damage states. A GP regression is then developed for each considered fragility parameter (i.e. median and dispersion). To further increase the tractability of the methodology, alternative metamodels are defined based on numerical non-linear static pushover analyses or analytical "by-hand" pushover analyses, through the Simple Lateral Mechanism Analysis (SLaMA) method. The results show that, for the considered portfolio, the fitted GP regressions have a high predictive power in surrogating the modelled fragility, demonstrating the feasibility of the approach in practice. It is also shown that the choice of the sampling technique could be based on the input data availability, rather than on the expected computational burden. Finally, the use of simplified methods for response analysis shows acceptable error levels with respect to the full time-history analysis results. Such simplified methods can be promising alternatives to generate large training datasets for the proposed GP regressions. This increases the potential of training metamodels in practical portfolio risk assessment applications, in which a high number of building types, each characterised by a large number of attributes, is generally involved.