000019751 001__ 19751
000019751 005__ 20170118182334.0
000019751 04107 $$aeng
000019751 046__ $$k2017-01-09
000019751 100__ $$aGehl, Pierre
000019751 24500 $$aBayesian Networks for the Derivation of Probabilistic Functionality Loss Curves for Bridge Systems

000019751 24630 $$n16.$$pProceedings of the 16th World Conference on Earthquake Engineering
000019751 260__ $$b
000019751 506__ $$arestricted
000019751 520__ $$2eng$$aIn the context of infrastructure risk assessment, the application of fragility curves to elements such as bridges should mostly serve the purpose of quantifying the performance losses at the system level (e.g. disruption of traffic, additional travel times), since these losses usually outweigh the direct costs associated with the physical damage of infrastructure. To this end, a methodology is proposed for the derivation of probabilistic functionality curves for bridge systems: these curves directly provide the probability of exceedance of various loss metrics given the level of seismic intensity. The main steps of the proposed approach are the following: - Identification of the failure modes for the various components of the bridge system (e.g. piers, bearings, deck, abutments, etc.). - Derivation of specific component fragility curves for each component damage state. - Estimation of the functionality losses that are associated with each component failure mode, through an expertbased survey. - Construction of a Bayesian Network that describes the failure of the system, from the seismic intensity to the component damage states and the subsequent functionality losses. - Use of the Bayesian Network to generate the joint probability of occurrence of various levels of functionality losses given the seismic intensity. This approach is then applied to a generic multi-span simply-supported reinforced concrete bridge, for which component fragility curves are analytically derived through non-linear time-history analyses. The considered loss metrics are the repair duration, the proportion of closed lanes and the speed limit reduction, so that these parameters can be directly fed into traffic modelling tools for the computation of induced delays and the optimization of restoration strategies.

000019751 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000019751 653__ $$abridges, Bayesian Networks, functional losses, restoration, fragility curves

000019751 7112_ $$a16th World Conference on Earthquake Engineering$$cSantiago (CL)$$d2017-01-09 / 2017-01-13$$gWCEE16
000019751 720__ $$aGehl, Pierre$$iD'Ayala, Dina
000019751 8560_ $$ffischerc@itam.cas.cz
000019751 8564_ $$s291845$$uhttps://invenio.itam.cas.cz/record/19751/files/4311.pdf$$yOriginal version of the author's contribution as presented on USB, paper 4311.
000019751 962__ $$r16048
000019751 980__ $$aPAPER