The development of transportation infrastructure systems is an essential part of modern civilization. Any functional deficiency of these systems may cause severe economic losses and social distress. Bridges are among the most vulnerable components of transportation networks. They are exposed to several deterioration processes and traffic loading scenarios during their service life, exacerbating the significant uncertainties on the life-cycle prediction of their structural response. Structural Health Monitoring (SHM) measurements can give significant information and support the damage detection of aging bridges, reducing the uncertainties associated to the structural performance and improving structural reliability of deteriorating systems. This paper presents a life-cycle probabilistic approach to incorporate SHM measurements via Bayesian updating in simulation-based reliability assessment of deteriorating bridges. The proposed methodology is applied to reinforced concrete (RC) bridges exposed to corrosion. The uncertainties of the corrosion model are updated based on SHM data. The Metropolis-Hastings (MH) algorithm is used to update the statistical parameters of the deterioration damage index at the prescribed observation time. The application to time-variant reliability assessment of a RC box-girder continuous bridge under corrosion shows the benefits of SHM to improve the accuracy of life-cycle prediction models.