journal article·2019-05Open access

An approach to develop printable strain hardening cementitious composites

Stefan Chaves Figueiredo, Claudia Romero Rodríguez, Zeeshan Y. Ahmed, D.H. Bos, Yading Xu, Theo M. Salet, Oğuzhan Çopuroğlu, Erik Schlangen, Freek P. Bos

Materials & Design· Volume 169

Abstract

New additive manufacturing methods for cementitious materials hold a high potential to increase automation in the construction industry. However, these methods require new materials to be developed that meet performance requirements related to specific characteristics of the manufacturing process. The appropriate characterization methods of these materials are still a matter of debate. This study proposes a rheology investigation to systematically develop a printable strain hardening cementitious composite mix design. Two known mixtures were employed and the influence of several parameters, such as the water-to-solid ratio, fibre volume percentage and employment of chemical admixtures, were investigated using a ram extruder and Benbow-Bridgwater equation. Through printing trials, rheology parameters as the initial bulk and shear yield stress were correlated with variables commonly employed to assess printing quality of cementitious materials. The rheology properties measured were used to predict the number of layers a developed mixture could support. Selected mixtures had their mechanical performance assessed through four-point bending, uni-axial tensile and compressive strength tests, to confirm that strain hardening behaviour was obtained. It was concluded that the presented experimental and theoretical framework are promising tools, as the bulk yield stress seems to predict buildability, while shear yield stress may indicate a threshold for pumpability.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Innovations in Concrete and Construction Materials · Additive Manufacturing and 3D Printing Technologies · Concrete and Cement Materials Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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