Bayesian modeling updating of miter gates with uncertain boundary conditions

Fernando Gomez, Billie F. Spencer, Matthew D. Smith

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

Navigational locks are critical infrastructure components; however, traditional inspection of the lock components, requires closing and dewatering the lock to perform visual inspections, which stops fluvial traffic and consequently presents significant economic losses. Moreover, the gates are typically the critical component in the navigational locks; due to impact or environmental deterioration, a gap occurs in the quoin between the gate and the wall, then the gap produces redistribution of the stresses that are rarely considered during the design of the gates, and this modification in the response accelerate the deterioration in some components of the gate. To overcome this challenge, previous researchers have presented structural health monitoring strategies for miter gates, for example, automated damage detection using strain gage measurements based on principal component analysis have been proposed. In this study, Bayesian model updating of miter gates with uncertain boundary conditions is presented with the following details: the uncertain boundary conditions are represented by the gap profile in the boundary with random depths at discrete locations, the slope of the strain gage measurements with respect to water levels is used as a representative damage feature, and a detailed finite element in Abaqus of the gate and the support is considered, where the gaps are modeled as nonlinear gap elements. Due to the large computational time required for the finite element model used, transitional Markov chain Monte Carlo simulation is implemented to sample the model parameters in high probability regions, from which statistical estimators are obtained. The results demonstrate the potential of the approach to perform Bayesian-based damage prognosis using digital twins.

Idioma originalInglés
Título de la publicación alojadaStructural Health Monitoring 2019
Subtítulo de la publicación alojadaEnabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
EditoresFu-Kuo Chang, Alfredo Guemes, Fotis Kopsaftopoulos
EditorialDEStech Publications Inc.
Páginas310-316
Número de páginas7
ISBN (versión digital)9781605956015
DOI
EstadoPublicada - 2019
Publicado de forma externa
Evento12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 - Stanford, Estados Unidos
Duración: 10 sep. 201912 sep. 2019

Serie de la publicación

NombreStructural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
Volumen1

Conferencia

Conferencia12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019
País/TerritorioEstados Unidos
CiudadStanford
Período10/09/1912/09/19

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