Method and System for Determining an Optimized Cable Routing Plan Using AI and Machine Learning
Abstract: This paper presents a conceptual framework for an AI - driven method and system for determining an optimized cable routing plan based on a given schematic design. The system utilizes machine learning models to assess environmental constraints, validate predefined routing parameters, and generate efficient routing paths with minimal interference. A 3D model of the infrastructure is provided by the user, allowing the AI to analyze available pathways and recommend the most cost - effective and time - efficient routes. The approach ensures reduced cable length, fewer bends, minimized interference, and enhanced routing efficiency. The proposed methodology is particularly relevant for industrial and commercial cabling infrastructures, offering automation, adaptability, and scalability. Future research directions include real - time optimization using IoT sensors and AR - based visualization for practical implementation.
Keywords: AI - driven cable routing, Machine Learning, 3D modeling, Real - time optimization, Cost - effectiveness, IoT - enabled monitoring, Infrastructure planning, Automated routing systems, Deep learning algorithms, Smart infrastructure
How to Cite?: Vamshi Krishna Kurrapati, "Method and System for Determining an Optimized Cable Routing Plan Using AI and Machine Learning", Volume 14 Issue 3, March 2025, International Journal of Science and Research (IJSR), Pages: 1496-1501, https://www.ijsr.net/getabstract.php?paperid=SR25320195324, DOI: https://dx.doi.org/10.21275/SR25320195324