Long-term structural health monitoring (SHM) of engineering structures is an excellent procedure
for assessing and predicting damage occurrences. Monitoring continuously a structure means that large
amounts of data will be collected and provided to the bridge owner for utilization. There is a risk that the
amount of data becomes more important than the quality and interpretation of the data. Developing new metrics
that use SHM information in a probabilistic approach when assessing the structural performance is, consequently,
a great challenge. The objective of this paper is to use performance functions based on monitoring
information that consider uncertainties and correlation in recorded measures. The proposed approach is applied
to an existing bridge for which a long-term monitored program was performed by the Engineering Research
Center for Advanced Technology for Large Structural Systems (ATLSS), at Lehigh University, Bethlehem,
Pennsylvania.