In the presence of operational uncertainty, one of the greatest challenges in systems engineering is to ensure system effectiveness, mission capability and survivability. Safety management is shifting from passive, reactive and diagnosis-based approaches to autonomous architectures that will manage safety and survivability through active, proactive and prognosis-based solutions. Resilience engineering is an emerging discipline, with alternative recommendations on safer and more survivable system architectures. A resilient system can "absorb" the impact of change due to unexpected disturbances, while it "adapts" to change, in order to maintain its physical integrity and mission capability. A framework of proposed resilience estimations is the basis for a scenario-based assessment technique, driven by modeling and simulation-based (M&S) analysis, for obtaining system performance, health monitoring, damage propagation and overall mission capability responses. For the technique development and testing, a small-scale canonical problem has been formulated, involving a reconfigurable spring-mass-damper system, in a multi-spring configuration. Operational uncertainty is introduced through disturbance factors, such as external forces with varying magnitude, input frequency, event duration and occurrence time. Case studies with varying levels of damping and alternative reconfiguration strategies return the effects of operational uncertainty on system performance, mission capability, and survivability, as well as on the "restore", "absorb", and "adapt" resilience capacities. The Topological Investigation for Resilient and Effective Systems, through Increased Architecture Survivability (TIRESIAS) technique is demonstrated for a reduced scale, reconfigurable naval cooling network application. With uncertainty effects modeled through network leak combinations, TIRESIAS provides insight on leak effects to survival times, mission capability degradations, and on resilience function capacities, for the baseline configuration. Comparative case studies were conducted for different architecture configurations, which have been generated for different total number of control valves and valve locations on the topology.