Chelapramkandy, Rifan; Ghosh, Jayadipta; Freddi, Fabio; (2024) Surrogate modeling for seismic fragility prediction of masonry infilled RC frames. In: Proceedings of the 18th World Conference on Earthquake Engineering (WCEE 2024). World Conference on Earthquake Engineering (WCEE): Milan, Italy.

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Abstract

Reinforced concrete (RC) frames with masonry infills have continued to remain a popular construction typology across the globe, including regions characterized by moderate to high seismic activity. Owing to the brittle nature of the masonry infills, their influence on the seismic analysis and design of framed structures has been typically neglected. However, scientific literature and field reconnaissance surveys indicate that the strength, stiffness, and distribution of masonry infills within RC frames can significantly influence their seismic performance. Typical finite element modeling of masonry infills within RC frames for nonlinear time history analysis comprises of single or multiple struts modeling approaches that require detailed information on the characteristic back-bone curve of the masonry infill. However, past studies report considerable variability associated with the material parameters of masonry infills (such as strength and elasticity) that may affect the seismic response and fragility of RC framed structures. This paper proposes a novel framework that develops parameterized seismic fragility functions for infilled RC frames conditioned on critical infill material parameters in addition to ground motion intensity. Unlike typical unidimensional fragility functions, the parameterized multidimensional fragility models offer flexibility to efficiently asses the influence of infill material parameters on seismic vulnerability. Such models are developed in the present study through a systematic approach rooted in statistical learning techniques. Initially, an experimental design is devised that considers an optimal combination of infill material parameters for computer simulations. Next, the seismic response from these simulations is obtained to develop surrogate models that predict engineering demand parameters (e.g., interstory drifts) as a function of infill material parameters and ground motion intensity. Lastly, the seismic demands obtained from the surrogate models are compared with seismic capacity estimates to generate the parameterized seismic fragility functions. The proposed methodology is applied to a case-study low-ductility RC frame with masonry infills to underline the gain in computational efficiency and accuracy for seismic response and vulnerability prediction.

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