Reducing Uncertainties Regarding Remaining Lives of Structures Using Computer-aided Data Interpretation

The growing importance of economic and environmental issues associated with the replacement of transportation infrastructure creates a demand for new techniques that are able to predict accurately the behaviour of existing structures. While monitoring structural behaviour can help reduce uncertainties, measured data alone is not sufficient to determine the behaviour of a structure. Models are necessary to infer causes from measurements and to perform prognostics because direct measurements are often not able to indicate causes directly. Furthermore, due to the large amount of data involved, it is in most cases not possible to process it without relying on automated computer-aided interpretation approaches. This paper presents a methodology that can improve the prognosis of complex systems by reducing uncertainties stress range predictions. An example made on a full-scale bridge shows the applicability and the benefits of the methodology for the prognosis of stresses used in the determination of the remaining fatigue life.