journal article·2024-07Open access

Influence of materials and nozzle geometry on spray and placement behavior of wet-mix shotcrete

Xiongxin Wang, Md. Mashfiqul Islam, Qian Zhang

Abstract

Shotcrete finds extensive applications in civil engineering, from repair and rehabilitation to new constructions. Nevertheless, shotcrete construction frequently suffers from placement issues, such as rebound and overshooting. These challenges can result in reduced operational efficiency, increased material wastage, diminished construction quality, and potential workplace hazards. It is worth noting that these placement issues are intrinsically linked to the spray characteristics of shotcrete. These characteristics are contingent upon various factors, including the material's flowability and viscosity, as well as the nozzle geometry. This research experimentally characterized the spray behavior of wet-mix shotcrete. High-speed cameras and image analysis techniques were utilized to capture and characterize wet-mix shotcrete on a full-scale spraying setup. The study experimentally determined the key placement performance parameters such as placement, rebound, and final build-up thickness and correlates the placement performance of shotcrete with the spray characteristics. The findings reveal that wider spray opening angles, smaller droplet sizes, and higher velocities contribute to improved material deposition, reduced rebound, and increased build-up thickness. The study also assesses the impact of nozzle geometry and rheology of the shotcrete mixture. Increased flowability led to slightly better spray behavior and placement. Higher material viscosity produced a more consistent spray which enhanced shotcrete performance. Multi-air jet guns, where compressed air directly contacts droplets, can enhance shotcrete spray efficiency.
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Research context

OpenAlex topics: Innovations in Concrete and Construction Materials · Innovative concrete reinforcement materials · Engineering and Material Science Research

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

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