PSI - Issue 83

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

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1. Introduction Technological innovations in the construction sector have driven the emergence and adoption of advanced construction methodologies that enhance structural quality and ensure safety performance in the built environment. Additive manufacturing of 3D printing cementitious composites (3DP-CCs) has gained importance in the last two decades, offering substantial gains in productivity, reduction in material consumption and waste, diminished dependence on formwork and manual labour, and expanded geometric versatility for structural components (Soares et al., 2023; Sousa et al., 2024). Extrusion-based 3D concrete printing (3DCP) is advancing as a digital construction method that deposits cementitious material layer-by-layer from a nozzle, enabling automated fabrication of complex geometries and reducing dependence on conventional formwork (Mohan et al., 2021). Achieving reliable 3DCP performance depends strongly on fresh-state engineering, pumpability, extrudability, buildability, and open time, because printable mixes must flow through the system yet quickly gain green strength to sustain subsequent layers (Rahman et al., 2024). Recent systematic and state-of-the-art reviews emphasize that controlling rheology and process parameters is essential for consistent structural quality and for mitigating print-induced defects such as interlayer weakness and anisotropy (Mohan et al., 2021; Rahman et al., 2024). In parallel, self-sensing cementitious composites have emerged as “smart” materials for structural health monitoring, where stress/strain-related changes are detected through electrical signals, most commonly via piezoresistivity enabled by conductive fillers, e.g., carbon black, CNTs, graphene, and carbon fibers. Their sensing response is frequently quantified using fractional change in resistance (FCR) and gauge factor (GF) concepts, which link electrical response to mechanical strain and allow comparison of sensitivity across mixes and loading regimes. However, reported sensing performance is highly dependent on dispersion quality, percolation, moisture/ionic effects, and measurement protocols, which have motivated ongoing efforts to improve repeatability and interpretation of piezoresistive signals (Dang et al., 2021; Li et al., 2025). Despite rapid growth, the intersection of 3D printability and self-sensing functionality introduces coupled constraints that are not fully resolved by treating “printability” and “sensing” as separate topics: printing can align fibers, create interlayer interfaces, and induce anisotropy, which directly affects conductive network formation and thus sensing stability (Sousa et al., 2024; Wang et al., 2022). Recent studies demonstrate the feasibility of 3D-printed self-sensing structural elements and embedded sensing features, but also highlight variability tied to geometry, electrode design, and print-induced heterogeneity, emphasizing the need for clearer design rules and benchmarking (Rahman et al., 2024; Ramachandran et al., 2022). The novelty and value of this review is to develop the promise versus-practicality trade-offs across: printable mix design and rheology windows, conductive filler selection and dispersion strategies, print-process effects on interlayer conductivity and anisotropy, and sensing metrics/testing protocols (FCR and GF), to identify what is genuinely functional for smart infrastructure rather than only “lab demonstrable.” 2. Self-sensing mechanism and materials development 2.1. Self-sensing mechanism Self-sensing cementitious composites function mainly through the piezoresistive effect, meaning that the electrical resistance or resistivity of the composite changes when mechanical stress, strain, or damage alters its internal conductive network. This resistance-based approach has been the dominant self-sensing strategy in cement-based materials since the field emerged in the early 1990s, and it remains the most widely studied route for structural health monitoring applications. Because plain cement paste is primarily an ionic conductor with high moisture sensitivity, conductive admixtures are typically added to improve electrical conductivity, signal stability, and sensing sensitivity. The most common fillers reported in the literature are short carbon fibers (CFs), carbon nanotubes (CNTs), carbon nanofibers (CNFs), carbon black (CB), graphite/graphene-based fillers, and hybrid combinations of these materials (Bekzhanova et al., 2021; Chung, 2020; Lee et al., 2017). At the microscale, the sensing response is governed by changes in contact conduction, tunneling conduction, and crack-induced disruption of conductive pathways inside the cementitious matrix. When the conductive filler