During the last three decades, multiple studies have been conducted to develop bridge columns that either sustain low damage or remain fully functional during moderate to strong earthquakes. Among such bridge columns, which are compatible with Accelerated Bridge Construction (ABC) and also offer self-centering, are precast concrete rocking columns. This dissertation intends to propose and evaluate an innovative class of seismically resilient precast concrete rocking columns using shape memory alloys (SMA).

The proposed precast concrete bridge columns, which are termed SMA-restrained rocking (SRR) columns, are connected to their adjacent substructure components through two series of unbonded links, namely, SMA links (to achieve self-centering) and replaceable energy dissipation (ED) links. After introducing three SRR column design variations, a displacement-based procedure is proposed for their effective seismic design. To investigate the performance of the proposed columns under monotonic and cyclic lateral loading and to examine the effective ranges of two key design parameters, nonlinear 3D solid finite element (FE) models are used.

Following the initial evaluations of SRR columns under monotonic/cyclic loading, the seismic performances of two reinforced concrete (RC) bridges with SRR columns are evaluated through multiple time history analyses. To examine the potential effects of ground motion characteristics, three ensembles of ground motions including far-field, near-fault without velocity pulse, and near-fault with velocity pulse are considered. In addition, the effects of ambient temperature on the behavior of SRR columns due to the thermomechanical properties of SMA links are studied. The analysis results show that the SRR columns' capability of sustaining damage is practically insensitive to site-to-source distance and velocity pulse content of ground motions and ambient temperature.

In order to evaluate the seismic fragility of bridges with SRR columns, a two-span RC bridge is considered. To produce seismic fragility functions, initially, multi-parameter probabilistic seismic demand models (PSDMs) are generated through different machine learning techniques and considering various sources of uncertainty. Subsequently, multi-parameter fragility functions are developed for various bridge damage states using neural networks. 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.