Simplified Assessment of Expected Seismic Losses for as Built and Retrofitted Rc Buildings

d’Aragona, Marco Gaetani; Polese, Maria; Ludovico, Marco Di; Prota, Andrea

doi:10.7712/120117.5608.18010

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…To overcome this issue, faster and simpler approaches are required. In this sense, different approaches were proposed in the last years relying on static analysis performed on refined [3] or simplified models [4]- [6], or on NRHAs performed on simplified models (e.g., shear models [7]). The latter models has been demonstrated to describe the non-linear characteristics and failure modes of multi-story buildings with sufficient accuracy both at the building scale and at the regional scale, enabling to capture the story-level damage concentration due to seismic loads [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

The Use of Stick-It Model for Edp Assessment in Existing Rc Infilled Typologies

d’Aragona¹,

Polese²,

Ludovico³

et al. 2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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The estimation of expected seismic losses at regional scale represents a critical issue for the assessment of the seismic risk and for the evaluation of the seismic resilience of large communities. As a basic step, the assessment of economic losses requires the computation of the building performances in terms of engineering demand parameters such as interstorey drift ratios and peak floor accelerations in order to assess the distribution and the extent of the damage experienced by various building components. The Stick-IT model (Stick for Infilled frames Typologies) was recently proposed to predict the response, in terms of engineering demand parameters, for infilled RC building typologies. Stick-IT is a MDOF system consisting of a series of lumped masses connected by nonlinear shear link elements. The model parameters can be defined for building typologies starting from low level information that can be easily retrievable at the large scale via image-based processing techniques integrated with generic information about typical construction features, such as in plan dimensions, the number of stories or the percentage of infills openings and considering the infills consistency. This paper adopts the Stick-IT model to predict damage and expected losses for a set of RC infilled buildings located in L'Aquila town. The application shows the advantages and the potentiality of the proposed model when adopted for large scale loss assessments.

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