The majority of self-sensing studies conducted in current literature uses small-scale specimens without steel reinforcement which might be misleading for damage monitoring in actual structures. To account for this, herein, the main emphasis was placed on self-sensing sensing evaluation of large-scale (100 x 150 x 1000 mm(3) [3.94 x 5.91 x 39.4 in.(3)] width x height x length) reinforced mortar beam elements tested under four-point bending. For the purposes of self-sensing, either chopped carbon fibers (CFs) or multi-walled carbon nanotubes (CNTs) were used in beams to achieve desirable electrical properties. Assessment of self-sensing was made by tracking the fractional changes in electrical resistivity (FCER) with respect to midspan beam displacement under flexural loading. Results related to electrical properties were recorded from brass electrodes embedded in specimens in fresh state using a resistivity meter using alternating current. Self-sensing results of large-scale beams were also backed by mechanical/structural characterization. Experimental findings suggest that use of CF and CNT in beam elements is significantly effective in modifying the overall failure types for the given reinforcement configuration. Proposed measurement setup is successful in capturing the flexural self-sensing data regardless of the type of carbon-based material. At low levels of damage, for both CF- and CNT-bearing beams, self-sensing and damage occurrence measured by the level of beam deformation are well-fit to each other, although this is clearer for specimens with CF However; at high levels of damage, most probably due to rupturing of individual fibers, clearer abrupt changes in FCER results of CF-bearing beams are monitored, although this is not the case for beams with CNT. Taking into account the production cost, performance, and easier mixability of different carbon-based materials within the relatively dense cementitious systems of beam elements, use of CF seems to be more advantageous than the use of CNT for an efficient self-sensing assessment of real-time structural elements.