Embedment of 3D Printed Self-Sensing Composites for Smart Cementitious Components

TL;DR

This paper explores using 3D-printed self-sensing composites to monitor the quality and performance of cementitious structures during and after printing.

Contribution

The study introduces hybrid 3D-printed self-sensing composites for real-time structural monitoring in cementitious components.

Findings

The three-strip design showed the highest sensitivity in strain sensing.

The two-strip design provided the best signal quality for static and dynamic measurements.

Hybrid manufacturing enables monitoring of print quality and defect detection.

Abstract

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well as the uniqueness of each printed component. Building upon our prior work in developing 3D-printable self-sensing cementitious materials by incorporating graphite powder and carbon microfibers into a cementitious matrix to enhance its piezoresistive properties, this study aims at enabling condition assessment of cementitious 3DP by integrating the self-sensing materials as sensing nodes within conventional components. Three different 3D-printed strip patterns, consisting of one, two, and three strip lines that mimic the pattern used in fabricating foil strain gauges were investigated as conductive electrode designs to impart strain sensing capabilities, and characterized from a series of quasi-static and dynamic tests. Results demonstrate that the three-strip design yielded the highest sensitivity (λstat of 669, λdyn of 630), whereas the two-strip design produced the highest signal quality (SNRstat = 9.5 dB, SNRdyn = 10.8 dB). These findings confirm the feasibility of integrating 3D-printed self-sensing cementitious materials through hybrid manufacturing, enabling monitoring of print quality, detection of load path changes, and identification of potential defects.

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