Abstract
Efficient prognosis based on structural health monitoring (SHM) information can improve the accuracy associated with structural performance assessment and prediction, and lead to more rational life-cycle management of civil infrastructure systems. This chapter deals with the statistical and probabilistic aspects for efficient prognosis using SHM data. The concepts of the statistics of extremes and decision analysis are employed for cost-effective monitoring planning considering availability of monitoring data and performance prediction error. In order to quantify this error, the loss function is used. Furthermore, the general concept of life-cycle evaluation, the possible effects of SHM on structural performance and service life prediction, and a practical approach to integrating SHM into life-cycle performance analysis of deteriorating civil infrastructures are presented.
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