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

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.

Authors

Publication Details

Journal
Urban Planning and Construction
Published
2025-05-24
DOI
https://doi.org/10.55121/upc.v3i1.536
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Anshul Jain, Hridayesh Varma
Urban Planning and Construction
BIM and Construction Integration
article

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

Anshul Jain, Hridayesh Varma
article en

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.

Urban Planning and ConstructionVol. 3(1)
Openalex Percentile: Top 77%
BIM and Construction Integration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.