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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.08415 |
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| _version_ | 1866911309582303232 |
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| author | Park, Sunggyu |
| author_facet | Park, Sunggyu |
| contents | The integration of AI into CAD systems transforms how engineers plan and develop infrastructure projects involving water and power transportation across industrial and remote landscapes. This paper discusses how AI-driven CAD systems improve the efficient, effective, and sustainable design of infrastructure by embedding automation, predictive modeling, and real-time data analytics. This study examines how AI-supported toolsets can enhance design workflows, minimize human error, and optimize resource allocation for projects in underdeveloped environments. It also addresses technical and organizational challenges to AI adoption, including data silos, interoperability issues, and workforce adaptation. The findings demonstrate that AI-powered CAD enables faster project delivery, enhanced design precision, and increased resilience to environmental and logistical constraints. AI helps connect CAD, GIS, and IoT technologies to develop self-learning, adaptive design systems that are needed to meet the increasing global demand for sustainable infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08415 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integration of AI-Driven CAD Systems in Designing Water and Power Transportation Infrastructure for Industrial and Remote Landscape Applications Park, Sunggyu Systems and Control The integration of AI into CAD systems transforms how engineers plan and develop infrastructure projects involving water and power transportation across industrial and remote landscapes. This paper discusses how AI-driven CAD systems improve the efficient, effective, and sustainable design of infrastructure by embedding automation, predictive modeling, and real-time data analytics. This study examines how AI-supported toolsets can enhance design workflows, minimize human error, and optimize resource allocation for projects in underdeveloped environments. It also addresses technical and organizational challenges to AI adoption, including data silos, interoperability issues, and workforce adaptation. The findings demonstrate that AI-powered CAD enables faster project delivery, enhanced design precision, and increased resilience to environmental and logistical constraints. AI helps connect CAD, GIS, and IoT technologies to develop self-learning, adaptive design systems that are needed to meet the increasing global demand for sustainable infrastructure. |
| title | Integration of AI-Driven CAD Systems in Designing Water and Power Transportation Infrastructure for Industrial and Remote Landscape Applications |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.08415 |