Uncertainties in predictive models for concrete structures performance can influence adversely the timing of management\r\nactivities. A methodology has been developed that uses data obtained through proactive health monitoring to increase the\r\nconfidence in predicted performance by reducing the associated uncertainties. Due to temporal and spatial variations associated\r\nwith climatic changes, exposure conditions, workmanship, and concrete quality, the actual performance could vary at different\r\nlocations of the member. In this respect, the use of multiple sensors may be beneficial, notwithstanding cost and other constraints.\r\nTwo distinct cases are identified for which an updating methodology based on data from multiple sensors needs to be developed.\r\nIn the first case the interest lies in improving the performance prediction for an entire member (or a structure) incorporating\r\nspatial and temporal effects. For this purpose, the member is divided into small zones with the assumption that a sensor can\r\nbe located in each zone. In the second case, the objective is to minimise uncertainties in performance prediction, or to increase\r\nthe redundancy of health monitoring systems, at critical locations. The development of updating methodologies for the abovementioned\r\nscenarios is described in this paper. Its implications on the management activities, for example, establishing the timing\r\nof principal inspections, are evaluated and discussed.
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