Links of a transportation network are subject to several forms of frequent non-severe disruptions 1 like traffic incidents, snowing, flooding, road-space reallocations, road-space infiltrations, 2 infrastructural failures, etc. Disregarding the cause, they may be viewed as multiple simultaneous 3 disruptions on links' capacities. Though the disruption or failure of a single link is widely 4 studied, the area of multiple simultaneous disruptions on transportation networks is under-5 researched. Also, most of the existing studies assume probability distributions of capacity 6 disruptions to be known. The objective of this paper is to study multiple simultaneous 7 disruptions and to establish a consequent critical state. We assign different levels on links' 8 capacities and search for probability distributions over those levels that would result in a critical 9 state. The critical state is modeled as the one which effects in poor network performance and 10 comparably equal expected path costs, that makes it hard for a user to identify the better path (i.e. 11 it induces maximum indifference on expected utilities of alternative paths). We formulate the 12 critical state link disruption problem as a minimax optimization problem and solve it using a 13 coevolutionary algorithm. We evaluate network performance in terms of expected system travel 14 time. The formulation is exemplified by finding expected system travel time at the critical state 15 on test networks. It is also shown how the performance at critical state can be used to measure 16 network robustness.