Efficient Bayesian Inference of Miter Gates Using High-Fidelity Models

Abstract

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.

A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates

“…Beyond these condition ratings, however, structural health monitoring (SHM) systems have been developed for the miter gates to measure their distributed point strain response during operation, providing continuous data streams which may be mined for damage-related information. The SHM measurement systems are coupled with validated high-fidelity physicsbased finite element (FE) models [16,[19][20][21][22], allowing for inference/estimation of the damage gap using the strain measurements. This approach provides more confident estimates of the damage gap state over time.…”

“…This method is a stochastic approach for approximating the global optimum of the cost function shown in Eq. (19). The GSA method is mainly used when processing complicated non-linear objective functions with a large number of local minima.…”

“…The formation of this gap is due to the contact degradation between the quoin block attached to the gate and the quoin block attached to the wall that supports the gate laterally. The formation of the bearing gap can be detected using sensor data or from features derived from this data [2,19,[30][31][32]. Figure 6 idealizes the loss of contact in the physical-based FE model and shows the top view of the quoin blocks.…”

Diagnosis, Prognosis, and Maintenance Decision Making for Civil Infrastructure: Bayesian Data Analytics and Machine Learning

“…For civil systems, the approach is usually carried out by using a physics-based model (e.g. finite element (FE) model) of the structure [2][3][4]. It is fundamentally an inverse problem because the system parameters are estimated from measured response quantities.…”

“…In a general sense, Figure 1 shows the fundamental workflow of a "digital twin" for structural asset life cycle diagnosis and prognosis. For damage diagnosis, engineers can rely on supervised learning algorithms when sufficient life-cycle data is available [2][3][4]. On the other hand, when life-cycle data is limited, engineers typically rely on physics-based modeling (such as finite element (FE) models) and model updating techniques to estimate the unknown parameters required to infer the current state of the system as indicated in Fig.…”