Behavior of Flexible Pintles for Miter Gates

Fillmore, Travis B.; Smith, Matthew D.

doi:10.1061/(asce)ww.1943-5460.0000653

Cited by 3 publications

(1 citation statement)

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“…9) form a hinge about which the gate rotates. More detailed information about pintle behavior can be found in Fillmore and Smith (2021). When open, boats can enter or leave the lock chamber.…”

Section: Miter Gate Operation Load State and Feature Of Interestmentioning

confidence: 99%

A surrogate model to accelerate non-intrusive global–local simulations of cracked steel structures

Fillmore

Vega

et al. 2022

Struct Multidisc Optim

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Physics-based digital twins often require many computations to diagnose current and predict future damage states in structures. This research proposes a novel iterative global–local method, where the local numerical model is replaced with a surrogate to simulate cracking quickly on large steel structures. The iterative global–local method bridges the scales from the operational level of a large steel structure to that of a cracked component. The linear global domain is efficiently simulated using static condensation, and the cracked local domain is quickly simulated using the adaptive surrogate modeling method proposed herein. This work compares solution time and accuracy of the proposed surrogate iterative global–local method with a reference model, a submodeling model, and an iterative global–local method with no surrogate model for the local domain. It is found that the surrogate iterative global–local method gives the fastest solution time with comparatively accurate results.

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Section: Miter Gate Operation Load State and Feature Of Interestmentioning

confidence: 99%

A surrogate model to accelerate non-intrusive global–local simulations of cracked steel structures

Fillmore

Vega

et al. 2022

Struct Multidisc Optim

Self Cite

View full text Add to dashboard Cite

show abstract

Diagnostics and prognostics of multi-mode failure scenarios in miter gates using multiple data sources and a dynamic Bayesian network

Fillmore

Vega

et al. 2022

Struct Multidisc Optim

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Current health monitoring approaches for large structures mostly rely on a combination of distributed sensor networks and in-situ inspection. This paper presents a novel online diagnostics and prognostics framework for structures subject to multiple failure modes and demonstrates the proposed method with a high-fidelity finite element model using multiple data sources (i.e., strain gauges and images). The approach aims at an accurate simulation of the interaction between different failure features, and subsequently at the effective estimation and prediction of the damage states based on the generated structural physics. A dynamic Bayesian network is used which incorporates different data sources to evaluate the structures under different kinds of deterioration mechanisms. In diagnosis, the dynamic Bayesian network is used to approximate the damage-related parameters and estimate the timedependent variables. In prognosis, the dynamic Bayesian network gives a probabilistic prediction of the remaining useful life of the structure based on the evolution of the failures. It is found that the proposed framework is highly effective in performing online diagnosis and prognosis using combined data sources.

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