Estimating future reliability and maintenance of aging ship structures is a growing challenge. Our improved ability to sense structural loads and responses through structural health monitoring systems has resulted in an increased awareness of how structures are being used. An attractive use of this data is the forecasting of future structural conditions. This research explores a model updating approach concentrating on structural fatigue failures and observed permanent set, examining fatigue capacity updating. A probabilistic S-N fatigue crack initiation model is presented. By using a lognormal modeling approach, the probability of time to crack initiation can be calculated analytically. This model is extended via efficient formulas for forecasting the expected number of fatigue cracks over time in grillage-type structures with multiple similar fatigue-prone details. The updating power of this network is extended via a permanent set model relating load and permanent set assuming uniform pressure. Coupling this permanent set model with the fatigue model, through local stress, is demonstrated to provide a more accurate prognosis than the fatigue model alone. The parameters of this model are represented using a Bayesian Network (BN). The BN is used to develop revised capacity parameter estimates and estimates of future rates of cracking through an inference approach based on observed failures. The model is tested against synthetic data for grillages with 100 fatigue-prone connections. Using the lognormal model, Monte Carlo simulations are run to generate cracking and permanent set histories. A Bayesian network is used to develop revised capacity parameter estimates and estimates of future rates of fatigue cracking through an inference approach based upon observed failures and updated permanent set data. The utility of fusing multiple measurements to update the future prognosis is evaluated.

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