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<p>Autonomous robotic application of shotcrete requires not only precise actuation and control, but also a minimum understanding of the sprayed-concrete placement process. However, modeling shotcrete is inherently complex due to its dynamic, multiphase, and stochastic nature. To address this challenge, we present a real-time simulation model based on physics-informed particle systems that captures the key characteristics of shotcrete spraying and accumulation. The model accounts for total rebound, cohesive failure (material detachment), and interactions with reinforcement elements commonly found in construction scenarios. This approach enables a flexible and computationally efficient simulation model that provides visual feedback to the user and structured data output (i.e., height-field) suitable for downstream analysis. Material distribution on a receiving target surface and the total rebound were systematically analyzed and compared with empirical data found in the literature, demonstrating a strong correlation between simulation outcome and experimental observations. This model establishes a foundation for the integration of artificial intelligence (AI) technologies and hybrid, simulation-based planning and control methods—such as Digital Twins—to enhance robotic shotcrete performance and autonomous decision-making.</p>

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Physics-based particle system modeling of shotcrete process for robotic placement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Mohammad Reza Yazdi Samadi,
  • Ralf Waspe,
  • Christian Schlette

摘要

Autonomous robotic application of shotcrete requires not only precise actuation and control, but also a minimum understanding of the sprayed-concrete placement process. However, modeling shotcrete is inherently complex due to its dynamic, multiphase, and stochastic nature. To address this challenge, we present a real-time simulation model based on physics-informed particle systems that captures the key characteristics of shotcrete spraying and accumulation. The model accounts for total rebound, cohesive failure (material detachment), and interactions with reinforcement elements commonly found in construction scenarios. This approach enables a flexible and computationally efficient simulation model that provides visual feedback to the user and structured data output (i.e., height-field) suitable for downstream analysis. Material distribution on a receiving target surface and the total rebound were systematically analyzed and compared with empirical data found in the literature, demonstrating a strong correlation between simulation outcome and experimental observations. This model establishes a foundation for the integration of artificial intelligence (AI) technologies and hybrid, simulation-based planning and control methods—such as Digital Twins—to enhance robotic shotcrete performance and autonomous decision-making.