Fig. 2: Time-dependent resilience estimates and recovery modeling in traditional and smart approaches.

a The annual resilience estimates derived from current and smart modeling approaches. Current models can depict infrastructure performance under progressive and shock events. Also, maintenance actions can be posed in terms of a limiting minimum estimate value. Uncertainty bounds may or may not change over time. Smart modeling approaches take advantage of different information sources and intelligent algorithms to infuse validated knowledge into the resilience estimation. In this way the uncertainty is temporal and conditional on the available information. The uncertainty in prediction may increase if the models are not updated, however the need for updating may reduce as algorithms, models, and sensors’ technology improves. Corrective maintenance could be scheduled ahead, or delayed, with respect to the traditional approach, given better information is at hand for decision-making. b Traditional and smart-based post-event functionality evolution. Current recovery modeling probably relies on pre-existing models adapted from other regions, without improvements observed in inference even if new system condition were sensed. Expert-opinion, data assimilation, data fusion, and dynamic updating of current recovery models can be used to better inform the infrastructure recovery evolution in smart modeling approaches. (Icons © Microsoft).