  This paper evaluates the seismic fragility of a two-span reinforced concrete
(RC) bridge with shape memory alloy (SMA)-restrained rocking (SRR) columns
through machine learning (ML) techniques. SRR columns incorporate a combination
of replaceable superelastic NiTi (SMA) links and mild steel energy-dissipating
links to achieve self-centering and energy dissipation, respectively, while
their rocking joints are protected against compressive concrete damage through
steel jacketing. To produce seismic fragility functions, initially,
multi-parameter probabilistic seismic demand models (PSDMs) are generated for
various engineering demand parameters through five different ML techniques
(including neural network) and considering various sources of uncertainty, and
the most accurate PSDMs are selected. The selected PSDMs are then interpreted
using four different methods to investigate the effects of two key SRR column
design parameters (self-centering coefficient and SMA link initial strain) and
ambient temperature on the seismic performance of SRR columns. Subsequently,
using neural networks, the PSDMs developed earlier, and appropriate capacity
models, multi-parameter fragility functions are developed for various bridge
damage states. After examining the effects of the two SRR column design
parameters on the seismic fragility of the bridge, its seismic fragility is
compared with those of the same bridge with monolithic RC and posttensioned
(PT) rocking columns. It is shown that, in general, increasing the initial
strain of the SMA links and decreasing the self-centering coefficient as
possible (i.e., without compromising the self-centering) reduce the overall
bridge damage. In addition, even considering the ambient temperature's
uncertainty, SRR columns are proven, at least, as effective as PT columns in
mitigating the seismic damage of the bridges of monolithic RC columns.
