Current infrastructure is designed and built such that it must simultaneously comply with all possible loads. This leads to overdesigned structures that are inefficient in terms energy and cost. A structure that can self-identify damage, adapt, and learn for future events addresses the emerging field of intelligent infrastructure through inspiration from biology. The required material and embodied energy of structural elements is reduced while maintaining structural integrity with a small increase in operational energy for active control. Tensegrity structures are cable-strut systems held in equilibrium due to self-stress. There is potential for damage tolerance when they are kinematically redundant. This paper presents active control algorithms applied for damage mitigation and service loading of the tensegrity structure. Active control using a path-planning RRT*-connect algorithm and soft-constraint algorithm changes the shape of the tensegrity structure to reduce member stresses and to restrict vertical (downward) displacement caused by a damaged element. Though the effect improves the configuration of the structure, it cannot be fully restored to the design configuration. Effectiveness of the RRT*-connect and soft-constraint algorithms for the half structure depends on the relative change of nodal positions and feasibility to bring the structure back within serviceability limits. Since correction of end-node coordinates can be grouped according to the direction vector to mitigate the configuration of the structure and resulting cable-length changes, case-based reasoning (CBR) is useful to reduce time of execution and to avoid unnecessary cable-length changes. Mitigation techniques are successfully demonstrated for serviceability thresholds for a uniformly distributed load.