Abstract
Concrete vibration construction sustains high labor intensity, a poor working environment, difficulties in quality control, and other problems. Current research on concrete vibration focuses on monitoring vibration quality, evaluating vibration processes quantitatively, and assessing mechanical vibration of unreinforced mesh concrete (plain concrete). Standardizing concrete vibration under reinforcing steel mesh remains difficult. There is still a lag in the evaluation of the quality of rework and the consumption of human and material resources. To tackle these issues, a vibrating robotic arm system based on automation control technology, machine vision, and kinematic modeling is proposed. Research and simulation tests on intelligent concrete vibration under reinforcing steel mesh aim to enhance construction efficiency and quality. A five-degree-of-freedom robotic arm with a vision module identifies each rebar grid center in the image, extracts the pixel coordinates, and converts them to the mechanical coordinates by the integration of machine vision algorithms. A vibrator point screening algorithm is introduced to determine actual vibrator point locations based on specific insertion spacing, alongside a vibro-module for vertical movement. Real-time assessment of vibration quality is achieved using the YOLOv5 target detection model. Simulation tests confirm the feasibility of automated concrete vibration control under reinforcing steel mesh by a vibrating robot arm system. This research offers a new approach for unmanned vibration technology in concrete under reinforcing steel mesh, supporting future related technological advancements with practical value.
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