Real-Time Simulation and Optimization of High-Voltage Cable Construction Process Using Digital Twin and Deep Reinforcement Learning

The construction of high-voltage cable systems involves complex processes that require precise planning, monitoring, and optimization to ensure efficiency and reliability. This study introduces a real-time simulation and optimization framework for the high-voltage cable construction process, leveraging predictive modeling and optimization algorithms. The proposed framework integrates physical and virtual environments to create a dynamic and interactive simulation system. By utilizing real-time data acquisition, advanced predictive models, and optimization techniques, the framework enables continuous monitoring, scenario analysis, and adaptive decision-making during construction. Key challenges such as resource allocation, risk assessment, and schedule optimization are addressed through these computational methods, ensuring improved construction quality and reduced project delays. Experimental results demonstrate the framework's capability to enhance process efficiency, minimize risks, and provide valuable insights for construction management. This work highlights the potential of predictive modeling and optimization algorithms in transforming high-voltage cable construction practices.