Digital twin-based approaches for structural health monitoring (SHM) and damage prognosis (DP) are emerging as a powerful framework for intelligent maintenance of civil structures and infrastructure systems. Model updating of nonlinear mechanicsbased Finite Element (FE) models using input and output measurement data with advanced Bayesian inference methods is an effective way of constructing a digital twin. In this regard, the nonlinear FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic excitations applied by a large shake table is considered in this paper. This bridge column, designed according to US seismic design provisions, was tested on the NEES@UCSD Large High-Performance Outdoor Shake Table (LHPOST). The column was subjected to a sequence of ten recorded earthquake ground motions and was densely instrumented with an array of 278 sensors consisting of strain gauges, linear and string potentiometers, accelerometers and Global Positioning System (GPS) based displacement sensors to measure local and global responses during testing. This heterogeneous dataset is used to estimate/update the material and damping parameters of the developed mechanics-based distributed plasticity FE model of the bridge column. The sequential Monte Carlo (SMC) method (set of advanced simulationbased Bayesian inference methods) is used herein for the model updating process. The inherent architecture of SMC methods allows for parallel model evaluations, which is ideal for updating computationally expensive models.

Continuous monitoring of miter gates used in navigation locks is desirable in order to prioritize maintenance and avoid unexpected failures. Substantial economic losses to the marine cargo and associated industries are caused by the closure of these inland waterway structures. Strain gauges are often installed in many of these miter gates for data collection, and various inverse finite element techniques are used to convert the strain gauges data to damage-sensitive features. One of the damage features is the development of a contact-loss "gap" between the components (i.e. quoin blocks) that support the gate laterally, which leads to load re-distribution that can induce overload in some components of the gate. Arguably, a refined finite element model of such structure can be very computationally expensive even when using linear models. An efficient way to solve an inverse problem with time-consuming model evaluations is making use of parallel model evaluations using a Sequential Monte Carlo (SMC) algorithm and parallel solution of the finite element (FE) equations using a commercial FE software. A significant advantage of SMC algorithms is that model evaluations are independent and are able to be run in parallel. In this paper, an expensive high fidelity model of a miter gate is used to infer the gap extend given a noisy set of strain measurements.

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