TY - JOUR
T1 - Maintenance decision model for steel bridges
T2 - a case in the Netherlands
AU - Attema, Thomas
AU - Kosgodagan Acharige, Alex
AU - Morales Napoles, Oswaldo
AU - Maljaars, Johan
PY - 2017/2/1
Y1 - 2017/2/1
N2 - A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.
AB - A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.
KW - bridge deck
KW - Fatigue
KW - linear elastic fracture mechanics
KW - monitoring
KW - non-parametric Bayesian networks
UR - http://www.scopus.com/inward/record.url?scp=84992467556&partnerID=8YFLogxK
U2 - 10.1080/15732479.2016.1158194
DO - 10.1080/15732479.2016.1158194
M3 - Article
AN - SCOPUS:84992467556
SN - 1573-2479
VL - 13
SP - 242
EP - 253
JO - Structure and Infrastructure Engineering
JF - Structure and Infrastructure Engineering
IS - 2
ER -