Cement-based composites are among the most widely used materials in modern infrastructure, yet their inherent brittleness and durability concerns inevitably pose challenges to their long-term performance. To overcome these limitations, smart cementitious composites with self-sensing functionality have attracted increasing interest. However, conventional approaches relying on functional additives often encounter agglomeration issues, leading to reduced sensing and mechanical properties. This study proposes an innovative self-sensing approach by embedding geometry-based conductive polymer reinforcements (GCPRs) to form continuous and stable conductive networks, rather than relying on randomly dispersed conductive fillers. A rigorously validated electromechanical model is developed to investigate the self-sensing behavior of the composites. The results reveal linear and nonlinear correlations between fractional change in resistivity (FCR) and stress for cementitious matrices without and with conductive fillers, respectively. Compression tests demonstrate stable piezoresistive behavior in GCPR embedded cementitious composites, achieving absolute FCR values of up to 37.3% under cyclic loading with 2% strength reduction. Geometry tailoring further enables tunable self-sensing performance; reducing the strut diameter increases the absolute FCR by approximately 70%, while modifying the unit cell arrangement enhances it by nearly 22%. The proposed systems exhibit markedly enhanced self-sensing performance, achieving a maximum stress sensitivity of and a gauge factor of 2084, representing an approximately threefold improvement over most reported filler-based systems while maintaining compressive strength. The study elucidates the underlying stress-dependent charge transport mechanisms and highlights the tunability of GCPRs in optimizing self-sensing performance, providing a new pathway for intelligent infrastructure systems. • Geometry-based conductive polymer reinforcement enables self-sensing composites. • An electromechanical model captures piezoresistive behavior in the composites. • The developed composites exhibit strong and stable self-sensing under cyclic loads. • Self-sensing performance is tunable by modifying the geometry of the reinforcement. • The electromechanical mechanisms in the proposed composites are elucidated.
Li et al. (Tue,) studied this question.