000190765 001__ 190765
000190765 005__ 20180317093924.0
000190765 037__ $$aCONF
000190765 245__ $$aImproving Remaining-Fatigue-Life Evaluation Using Data Interpretation
000190765 269__ $$a2013
000190765 260__ $$c2013
000190765 336__ $$aConference Papers
000190765 520__ $$aThis paper presents a methodology that improves fatigue-performance evaluations using model-based data interpretation. The accuracy of stress-range values is essential for quantifying fatigue damage. These values are usually predicted using physics-based models such as those used within finite element analyses. In the modelling process, simplifications are inevitable, thus causing systematic errors in model predictions. Structural health monitoring coupled with model-based data-interpretation approaches have the potential to reduce uncertainties associated with the evaluation of stress-range predictions. Because of the presence of modelling and measurement uncertainties, many models may explain the true structural behaviour. A model falsification approach, which is able to cope with incomplete knowledge of uncertainties, is used to isolate candidate models from an initial population of models. This approach is robust for systematic errors that are correlated spatially. The candidate models that are identified using the model-falsification approach predict stress ranges in structural members, from which the remaining fatigue life is determined. Due to the uncertainty reduction in model predictions during data interpretation, the accuracy of the fatigue prognosis is improved. A steel beam composed of a circular hollow-section truss is studied for illustration. Monitoring data that is interpreted using a model-falsification methodology shows potential for improving evaluations of remaining fatigue life.
000190765 6531_ $$aModel-based data interpretation
000190765 6531_ $$auncertainty reduction
000190765 6531_ $$aremaining-fatigue-life prediction.
000190765 700__ $$0245609$$aPasquier, Romain$$g175755
000190765 700__ $$0247528$$aReuland, Yves$$g187105
000190765 700__ $$0241981$$aSmith, Ian F. C.$$g106443
000190765 7112_ $$a6th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII)$$cHong Kong$$dDecember 9-11, 2013
000190765 8564_ $$s1287968$$uhttps://infoscience.epfl.ch/record/190765/files/Pasquier_et_al_SHMII6.pdf$$yPreprint$$zPreprint
000190765 909CO $$ooai:infoscience.tind.io:190765$$pENAC$$pconf
000190765 909C0 $$0252031$$pIMAC$$xU10237
000190765 917Z8 $$x175755
000190765 917Z8 $$x175755
000190765 937__ $$aEPFL-CONF-190765
000190765 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000190765 980__ $$aCONF