Optimized maintenance of operating aging infrastructures is of paramount importance to ensure safe and cost effective operation during their original design lifetime and even beyond that. Modern answers to the problem should focus on automated planning and decision making techniques taking advantage of informative but uncertain data that become available during the structural life-cycle. In this paper such a solution framework is presented, based on partially observable Markov decision processes (POMDPs). In a POMDP framework, the evolution of the system is described by stochastic processes, real-time observation data update the system state estimations, and all possible future actions, about where, when and what type of inspection and repair should be performed, are taken into account in order to optimize the long-term life-cycle objectives. As a consequence of their advanced mathematical attributes, POMDP models are unfortunately hard to solve. In recent years, however, significant breakthroughs have been achieved, mainly due to the introduction of point-based value iteration algorithms. In this work, several POMDP point-based methods are examined, with various characteristics in the selection of the belief space points/subset and the value function update procedures. To investigate the strengths and limitations of the various solution methods for structural maintenance problems of deteriorating infrastructure and to draw conclusions regarding their efficiency and applicability to problems of this kind, a realistic nonstationary example is selected, concerning corrosion of reinforcing bars of concrete structures in a spatial stochastic context.