Simplified seismic assessment of infilled RC frame structures

Abstract

Earthquake-induced structural damage, characterised by the exceedance of different damage states during ground shaking, is typically quantified using fragility curves generated through non-linear dynamic analyses often requiring a large computational effort. This level of effort has led to the necessity of simplified methods and approximate analysis tools. In this regard, SPO2IDA has emerged as a convenient tool for the assessment of structures. It relates a structure’s backbone characteristics to a set of incremental dynamic analysis (IDA) curves using static pushover analysis (SPO) results and a library of empirical fitting coefficients for the different branches of the idealised SPO backbone. It permits the quantification of structural performance up to structural collapse as a function of seismic intensity in a simple and efficient manner. It has been developed mainly for ductile structures that can be sufficiently represented via a SPO backbone with a certain ductile post-yield hardening followed by a post-peak degradation. This behaviour is quite representative of ductile RC and steel moment-resisting frames and has resulted in the tool being widely adopted. However, the same may not be observed when dealing with reinforced concrete (RC) frames with masonry infill, a structural typology that still requires significant addressing in the earthquake engineering field. The present study describes an extension to this methodology for structural typologies with a more particular backbone behaviour, typical of RC frames with masonry infill panels, since differences in backbone behaviour compared to typical structures render the extension of the original tool inappropriate and at times unconservative. Extensive analyses were conducted to investigate the behaviour and trends when pushing infilled RC frames up to complete structural collapse. A new library of empirical coefficients was then fitted and proposed by considering a large database of representative backbones to result in an extended SPO2IDA proposal for infilled RC frames. It is then shown how these coefficients provide a much-improved matching, when compared to the original tool for this specific case, both in terms of the produced IDA traces and also the drift-based mean annual rates of exceedance.

Appendix

The following are the tables of the coefficients fitted via two step regression in Sect. 4.3.

For the α₁ and β₁ terms defined in Eqs. 13 and 14, Tables 8, 9, 10, 11, 12 and 13 provide the fitted coefficients, respectively, which are presented for the 16%, 50% and 84% IDA fractiles.

Table 8 Fitted coefficients for the α₁ term, each corresponding to the 50% values

Table 9 Fitted coefficients for the α₁ term, each corresponding to the 16% values

Table 10 Fitted coefficients for the α₁ term, each corresponding to the 84% values

Table 11 Fitted coefficients for the β₁ term, each corresponding to the 50% values

Table 12 Fitted coefficients for the β₁ term, each corresponding to the 16% values

Table 13 Fitted coefficients for the β₁ term, each corresponding to the 84% values

For the α₂, β₂ and γ₂ terms defined in Eqs. 16–18, Table 14 provides the fitted coefficients, which are presented for the 16%, 50% and 84% IDA fractiles.

Table 14 Fitted coefficients for the α₂, β₂ and γ₂ terms

For the α₃ and β₃ terms defined in Eqs. 20 and 21, Tables 15 and 16 provide the fitted coefficients, respectively, which are presented for the 16%, 50% and 84% IDA fractiles.

Table 15 Fitted coefficients for the α₃ term, each corresponding to the 16%, 50% and 84% values

Table 16 Fitted coefficients for the β₃ term, each corresponding to the respective 16%, 50% and 84% values

For the α₄, β₄ and γ₄ terms defined in Eqs. 23 and 24, Table 17 provides the fitted coefficients, which are presented for the 16%, 50% and 84% IDA fractiles.

Table 17 Fitted coefficients for the α₄, β₄ and γ₄ terms