Artificial Intelligence Based Site Selection for the Construction of Megastructures in Urban areas

Authors

  • Anshul Jain *

    Department of Civil Engineering, Sagar Institute of Research & Technology, Bhopal 462026, India
  • Hridayesh Varma

    Department of Civil Engineering, Sagar Institute of Research & Technology, Bhopal 462026, India

DOI:

https://doi.org/10.55121/upc.v3i1.536

Keywords:

Artificial Intelligence , Megastructure Construction, Site Selection , Machine Learning , Geospatial Analysis , Multi-Criteria Decision-Making, Environmental Sustainability

Abstract

The construction of megastructures, such as skyscrapers, dams, and large-scale urban developments, demands precise site selection to ensure structural integrity, economic viability, and environmental sustainability. Traditional site selection methods rely heavily on manual surveys and expert judgment, which are time-consuming and prone to human error. Artificial Intelligence (AI) offers transformative potential to enhance the efficiency and accuracy of site selection by integrating vast datasets, predictive modeling, and optimization algorithms. This research paper explores the application of AI-based techniques, including machine learning, geospatial analysis, and multi-criteria decision-making, in selecting optimal sites for megastructure construction. The study proposes a novel AI-driven framework that combines environmental, geotechnical, socio-economic, and logistical factors to evaluate potential sites. Through a simulated case study, the framework demonstrates superior performance in identifying sites that minimize environmental impact, reduce costs, and maximize structural stability compared to conventional methods. The findings underscore AI’s capacity to revolutionize site selection processes, offering actionable insights for engineers, urban planners, and policymakers. The construction of megastructures demands precise site selection for structural integrity, economic viability, and environmental sustainability. Traditional methods are time-consuming and error-prone. This research explores an AI-based framework using machine learning, geospatial analysis, and multi-criteria decision making to optimize site selection. A simulated case study demonstrates superior performance, reducing site-selection time by 80%, cutting projected costs by 15% ($500M savings), and lowering environmental impact by 30% (carbon footprint) compared to conventional methods. The findings highlight AI’s potential to revolutionize megastructure site selection.

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