AbstractLife-cycle structural assessment of existing bridges under aging and deterioration processes is of paramount importance for authorities managing road networks. In fact, in most developed countries many bridges and infrastructure facilities are approaching 50 years of lifetime or are even older, and a significant percentage of them are prone to be rated as structurally deficient. In this context, a quick and reliable estimation of the bridge condition is needed for the prioritization of maintenance and repair interventions and the optimal allocation of resources over large bridge stocks. In the last decades, computerized bridge management systems (BMSs) emerged worldwide to support decision makers in the definition of operational management policies. BMSs often incorporate soft computing tools, such as artificial neural networks (ANNs), which can provide reliable bridge assessments over time under uncertainty on the basis of limited data stored in bridge databases. This paper proposes an approach to the life-cycle assessment of deteriorating reinforced concrete (RC) bridges based on ANNs. Two-layer ANNs are formulated and trained for this purpose. The proposed approach is preliminarily applied to a time-variant structural capacity assessment of a RC bridge deck cross-section under chloride-induced corrosion to compare the results obtained with both full and limited input datasets. Finally, the methodology is applied to the prediction over time of the condition state of a group of existing RC bridges based on the information stored in the US National Bridge Inventory.