PSI - Issue 83

Samia M. Mohamed et al. / Procedia Structural Integrity 83 (2026) 63–71

66

dispersion deteriorates, agglomeration increases, or the matrix loses mechanical integrity. Across studies, the most common conductive systems are CF, MWCNTs, CNFs, CB, graphite/graphene-related fillers, and hybrid combinations of micro- and nano-carbon fillers. These fillers are not functionally similar, CF often contributes crack bridging and toughness, CNT/CNF systems typically reduce percolation threshold and improve sensitivity, while particulate fillers such as CB or graphite can strongly reduce resistivity but may require careful dosage control to avoid excessive strength loss or unstable dispersion (Dogra et al., 2025; Donnini et al., 2018a; Taheri et al., 2022). A consistent observation reported across the literature is that the optimum sensing performance of cementitious composites is typically achieved near the percolation threshold of the conductive network, rather than at the maximum filler dosage. At this condition, the conductive phase forms a sufficiently connected network to enable stable electrical conduction, while avoiding excessive agglomeration or disruption of the cementitious matrix (Dinesh et al., 2023; Lee et al., 2017b; Zhao et al., 2024). For example, Zhao et al., demonstrated that in graphite-modified cement composites, the incorporation of 0.6 vol.% CF into a matrix containing 20 wt.% graphite powder resulted in a peak compressive strength of 43.01 MPa, a maximum resistivity change of − 76.8%, and a strain sensitivity factor of 1300 under cyclic compression (Zhao et al., 2024). In another graphite-based self-sensing cement composite, the highest reported stress sensitivity (1.35) was obtained at 5% graphite powder, whereas increasing the graphite content to 25% reduced electrical resistivity by 43.9% but resulted in a reduction in mechanical strength (Dinesh et al., 2023). Collectively, these findings indicate that the performance of self-sensing cementitious composites is governed by a delicate balance between conductive network formation and matrix integrity, highlighting that maximizing filler content does not necessarily lead to optimal sensing behavior. Beyond filler type and dosage, materials processing and dispersion strategies play a critical role in determining the stability and reliability of the sensing response. Techniques such as ultrasonic dispersion, surfactant assisted mixing, and hybrid filler systems are commonly employed to improve the homogeneity of the conductive network and minimize particle agglomeration. For instance, a CNT-modified UHPFRC system reported that incorporating 0.3 wt.% CNT or higher enabled crack-related self-sensing under compressive loading, while the same dosage allowed the pre-peak flexural response to be effectively captured through resistivity variation. In a more recent study focusing on embeddable sensing nodes, mixtures containing 0.2% reduced graphene oxide (rGO) and 2.5% carbon black (CB) exhibited the most stable piezoresistive behavior, with a reported fractional change in resistance (FCR) approaching 400% during reinforced concrete beam monitoring. Furthermore, red-mud-based self-sensing mortars incorporating carbon black nanoparticles showed that increasing red mud replacement from 25% to 100% led to higher viscosity and yield stress, accompanied by a 60% reduction in the consistency index, while simultaneously improving compressive strength by up to 80% and increasing the gauge factor by approximately 23%. These findings demonstrate that the development of self-sensing cementitious materials should not be considered solely from the perspective of conductive filler content, but must also account for dispersion methodology, matrix composition, and fresh-state rheological characteristics, which collectively influence the formation and stability of the conductive network (Dogra et al., 2025; Oliveira et al., 2025; You et al., 2017). Table 1 summarizes representative studies on SS-CCs, including both conventionally cast materials and extrusion-based 3D-printed systems. The comparison highlights the matrix composition, type and dosage of conductive fillers, fresh-state or printing characteristics, mechanical properties, and sensing performance reported in the literature. Carbon-based conductive additives, such as carbon fibers (CF), CNTs, graphene-derived materials (GO and rGO), CB, and graphite, are the most commonly adopted fillers for establishing conductive networks within cementitious matrices. As shown in the table, the sensing capability of these materials is strongly influenced by filler type, dosage, and dispersion method, as well as by matrix composition and processing route. In particular, several studies demonstrate that hybrid conductive systems and optimized filler contents can significantly enhance piezoresistive sensitivity while maintaining acceptable mechanical performance. In addition, extrusion-based 3D printing introduces further complexities related to rheology, interlayer bonding, and print-induced anisotropy, which may affect both structural behavior and sensing reliability. Overall, the compiled studies illustrate the progress achieved in developing multifunctional cementitious materials capable of structural load bearing and self-sensing, while also highlighting the limited number of investigations that simultaneously address mix design, printability, mechanical performance, and sensing behavior within the same experimental framework.