Investigation of 3D printed concrete for real-time monitoring of additive manufacturing process

The automation of concrete construction through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods, which can have a tremendous societal impact by globally reducing production costs, increasing construction speed and quality, and significantly enhancing sustainability through the integration of green concrete mixes and the use of strategic geometries. However, a significant challenge hindering its widespread implementation is the high level of uncertainty introduced by the printing process, particularly regarding quality control homogeneity and consistency that stem from variability in layer bonding and the uniqueness of each specific 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 to enable real-time non-destructive evaluation of concrete 3DP by integrating the self-sensing materials as sensing nodes within conventional components to process it with self-sensing properties. In particular, we seek to locally functionalize the material with strain-responsive capabilities through integrated nodes, where the electrical resistance of the functionalized materials can be measured and directly map the strain field evolution within the structure. Three different 3D-printed zig-zagging patterns, consisting of 1, 2, and 3 strip lines, which mimic the pattern used in fabricating foil strain gauges, were investigated as conductive electrode designs to improve strain sensing performance, characterized from a series of the quasi-static and dynamic tests. Results demonstrate that it is possible to integrate 3D-printed self-sensing cementitious materials within and during a 3DP process for real-time and post-print evaluation, thus allowing the monitoring of quality, detecting changes in load paths, and identifying potential defects.