Piezoresistive Sensing Performance of Smart Layer in Multi-Material 3D-Printed Reinforced Cementitious Beams
Year: 2026
Type: article
Abstract: 3D concrete printing (3DP) enables automated construction with reduced material waste and enhanced geometric flexibility. However, its structural performance remains sensitive to anisotropy, mix design, and printing parameters, thereby complicating quality control. Self-sensing cementitious materials provide a promising approach by enabling intrinsic strain monitoring during fabrication and service. In this study, a hybrid multi-material printing strategy was developed using a conductive cement-based mix incorporating graphite (G), milled carbon microfibers (MCMF), and chopped carbon microfibers (CCMF), alongside a plain cement-based matrix. Based on percolation analysis, an optimal composition of 2 wt.% G, 0.25 wt.% MCMF, and 0.0625 wt.% CCMF was selected. Reinforced beam specimens were fabricated with the conductive material embedded in either the tensile (bottom) or compressive (top) region, combined with two internal architectures: diagonal infill and solid-base configuration. Four configurations were defined: Pattern 1 (bottom/diagonal), Pattern 2 (bottom/solid-base), Pattern 3 (top/diagonal), and Pattern 4 (top/solid-base). Cyclic three-point bending tests with spatially distributed electrical measurements were conducted to evaluate the electromechanical response in the elastic range. Specimens with the conductive layer located in the tensile region (Patterns 1 and 2) consistently exhibited higher gauge factors than those in the compressive region (Patterns 3 and 4). Pattern 2 exhibited the best sensing performance, with an average gauge factor of 556 and SNR of 31. Across all configurations, SNR decreased with increasing electrode spacing, with reductions of up to 31.0%, demonstrating the effect of current path length on sensing performance.
Source: Sensors
DOI: 10.3390/s26103204
Institutions: Iowa State University, University of Perugia
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7161623227