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.

To address the challenges in concrete vibration during the construction of concrete-faced rockfill dams, this study proposes a multi-objective-driven lightweight and high-frequency vibrating robotic arm (VRA). The proposed system aims to improve adaptability and performance under harsh site conditions, such as inclined slab surfaces and confined rebar layouts. Based on the geometric structure and task characteristics of the VRA, a multi-objective topology optimization framework was established, integrating compromise programming and average frequency strategies. This method simultaneously achieves mass reduction, stiffness enhancement, and modal frequency improvement to avoid resonance during high-frequency operations. The workspace of the VRA was verified using kinematic modeling and Monte Carlo sampling, and a critical physical posture—where the arm is fully extended horizontally, producing maximum span and joint loads—was identified to extract dynamic load boundaries. Finite element analysis was then conducted under worst-case conditions, and the optimization results were validated by modal analysis and flexibility metrics. The optimized VRA demonstrated substantial improvements in structural performance, reducing overall mass, lowering flexibility, and increasing modal frequencies. The proposed framework provides a transferable approach for designing high-frequency robotic arms in vibration-intensive scenarios, supporting intelligent construction in concrete-faced rockfill dams and similar complex environments.