Artificial intelligence tunnel construction comprehensive is a M.Tech project topic for Environmental Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Artificial intelligence tunnel construction comprehensive Project Details
| Abstract |
This research looks at how to evaluate and organize AI methods used in tunnel construction, especially for assessing environmental risks, how soil and structures interact, and reducing groundwater effects. Using bibliometric tools like CiteSpace together with machine‑learning frameworks, the study reviews and classifies important research topics found in papers from 2011 to 2023. It points out main areas such as optimizing Tunnel Boring Machine (TBM) performance, describing rock and soil behavior, and monitoring environmental hazards in real time. The work especially focuses on adding predictive models that forecast ground settlement, groundwater seepage, and underground pollution during digging. By studying neural‑network algorithms, computer‑vision methods, and decision‑making systems, the project gives detailed
guidance for building strong risk‑assessment frameworks. The organized review becomes a technical base for using AI solutions to reduce geological and environmental hazards in complex underground projects, helping make engineering safer and more sustainable.
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| Reference Paper |
Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics |
| Domain |
Environmental Engineering |
| Sub-Domain |
Pollution Control / Soil & Groundwater / Risk Assessment |
| PDF Download |
Download / View PDF |
| Get Help |
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