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.

Keywords

Artificial IntelligenceEnvironmental SustainabilityGeospatial AnalysisMachine LearningMegastructure ConstructionMulti-Criteria Decision-MakingSite Selection

References

  1. [1] Wang, W.Y., Li, J., Liu, W., et al., 2019. Integrated computational materials engineering for advanced materials: A brief review. Computational Materials Science. 158, 42–48. DOI: https://doi.org/10.1016/j.commatsci.2018.11.001
  2. [2] Wang, W.Y., Tang, B., Lin, D., et al., 2020. A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks. Journal of Materials Research. 35(8), 872–889. DOI: https://doi.org/10.1557/jmr.2020.43
  3. [3] You, Z., Wu, C., 2019. A framework for data-driven informatization of the construction company. Advanced Engineering Informatics. 39, 269–277. DOI: https://doi.org/10.1016/j.aei.2019.02.002
  4. [4] Bilal, M., Oyedele, L.O., Qadir, J., et al., 2016. Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics. 30(3), 500–521. DOI: https://doi.org/10.1016/j.aei.2016.07.001
  5. [5] Jain, A., Babu, K.A., 2025. Reviewing the Green Building Concepts Along With the Applications of AI and IoT for Environmental Monitoring and Designing Sustainable Infrastructures. Journal of Informatics and Mathematical Sciences. 17(1), 123–145. DOI: https://doi.org/10.26713/jims.v17i1.3056
  6. [6] Wu, B., Maalek, R., 2023. Renovation or redevelopment: the case of smart decision-support in aging buildings. Smart Cities. 6(4), 1922–1936. DOI: https://doi.org/10.3390/smartcities6040089
  7. [7] Wang, W.C., Dwijendra, N.K.A., Sayed, B.T., et al., 2023. Internet of things energy consumption optimization in buildings: a step toward sustainability. Sustainability. 15(8), 6475. DOI: https://doi.org/10.3390/su15086475
  8. [8] Sadowski, K., 2021. Implementation of the new European Bauhaus principles as a context for teaching sustainable architecture. Sustainability. 13(19), 10715. DOI: https://doi.org/10.3390/su131910715
  9. [9] Ramesh, M.V., 2019. Integration of participatory approaches, systems, and solutions using IoT and AI for designing smart community: Case studies from India. Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Communities. pp. 4–5. DOI: https://doi.org/10.1145/3364544.3371501
  10. [10] Nadeem, M.W., Goh, H.G., Hussain, M., et al., 2021. Internet of Things for green building management: a survey. In: Role of IoT in Green Energy Systems. IGI Global. pp. 156–170. DOI: https://doi.org/10.4018/978-1-7998-6709-8.CH007
  11. [11] Mersal, A., 2023. The future of the sustainable green architecture through technology. HBRC Journal. 19(1), 33–62. DOI: https://doi.org/10.1080/16874048.2021.1948160
  12. [12] Martínez, I., Zalba, B., Trillo-Lado, R., et al., 2021. Internet of things (IoT) as sustainable development goals (SDG) enabling technology towards smart readiness indicators (SRI) for university buildings. Sustainability. 13(14), 7647. DOI: https://doi.org/10.3390/su13147647
  13. [13] Liu, X., Meng, F., Chen, L., 2022. Research on green building optimization design of smart city based on deep learning. 2022 World Automation Congress (WAC). IEEE. pp. 517–521. DOI: https://doi.org/10.23919/WAC55640.2022.9934445
  14. [14] Krausková, V., Pifko, H., 2021. Use of Artificial Intelligence in the Field of Sustainable Architecture: Current Knowledge. Architecture Papers of the Faculty of Architecture and Design STU. 26(1), 20–29. DOI: https://doi.org/10.2478/alfa-2021-0004
  15. [15] Lam, K.H., To, W.M., Lee, P.K., 2022. Smart Building Management System (SBMS) for commercial buildings—key attributes and usage intentions from building professionals’ perspective. Sustainability. 15(1), 80. DOI: https://doi.org/10.3390/su15010080
  16. [16] Jaafreh, A.B., Al Jaafreh, E.M.B., 2021. Adaption of construction management modelling system through the use of internet of things (IoT). European Scientific Journal. 17(29), 263–279. DOI: https://doi.org/10.19044/esj.2021.v17n29p263
  17. [17] Hodson, E., Vainio, T., Sayún, M.N., et al., 2023. Evaluating social impact of smart city technologies and services: Methods, challenges, future directions. Multimodal Technologies and Interaction. 7(3), 33. DOI: https://doi.org/10.3390/mti7030033
  18. [18] Jain, A., Babu, K.A., 2024. Application of Artificial Intelligence (AI) in the Lifecycle of Sustainable Buildings: An Exhaustive Literature Review. Journal of Informatics and Mathematical Sciences. 16(1), 97–128. DOI: https://doi.org/10.26713/jims.v16i1.2947
  19. [19] Benavente-Peces, C., Ibadah, N., 2020. ICT technologies, techniques and applications to improve energy efficiency in smart buildings. SENSORNETS. pp. 121–128. DOI: https://doi.org/10.5220/0009000601210128
  20. [20] Benavente-Peces, C., 2019. On the energy efficiency in the next generation of smart buildings—Supporting technologies and techniques. Energies. 12(22), 4399. DOI: https://doi.org/10.3390/en12224399
  21. [21] Aung, T., Liana, S.R., Htet, A., et al., 2023. Implementing green facades: A step towards sustainable smart buildings. Journal of Smart Cities and Society. (Preprint), 1–11. DOI: https://doi.org/10.3233/scs-230014
  22. [22] Adel Ismail, A., Momen Afifi, M., 2019. Smart eco home: towards sustainable community within relevant strategic integrated intelligence of architecture. Engineering Research Journal. 163, 80–105. DOI: https://doi.org/10.21608/erj.2019.122501
  23. [23] Aliero, M.S., Asif, M., Ghani, I., et al., 2022. Systematic review analysis on smart building: Challenges and opportunities. Sustainability. 14(5), 3009. DOI: https://doi.org/10.3390/su14053009
  24. [24] Almalki, F.A., Alsamhi, S.H., Sahal, R., et al., 2023. Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities. Mobile Networks and Applications. 28(1), 178–202. DOI: https://doi.org/10.1007/s11036-021-01790-w
  25. [25] Abbas, E., 2022. Towards a new scenario for sustainable coastal tourism “The role of the Internet of Things (IoT) application for Smart Sustainable Destination”. IOP Conference Series: Earth and Environmental Science. 1113(1), 012028. DOI: https://doi.org/10.1088/1755-1315/1113/1/012028
  26. [26] Abbasi, S., Hasan, R.Z., 2017. Review the new system and its impact on intelligent buildings and sustainable architecture. Journal of Fundamental and Applied Sciences. 9(1S), 500–510. DOI: https://doi.org/10.4314/JFAS.V9I1S.708
  27. [27] Abd Alhaleem, F.A.A., Abbas, A.M., Bogdady, M., 2021. Applications of smart technologies in buildings and their role in achieving sustainable architecture. Journal of Al-Azhar University Engineering Sector. 16(59), 375–394. Available from: https://journals.ekb.eg/article_166715_9424dcaadc6988374904a04bdfa50502.pdf (cited 5 March 2025).
  28. [28] Rane, N., Choudhary, S., Rane, J., 2023. Artificial Intelligence (AI) and Internet of Things (IoT) ‐ based sensors for monitoring and controlling in architecture, engineering, and construction: applications, challenges, and opportunities. Social Science Research Network. DOI: https://doi.org/10.2139/ssrn.4642197
  29. [29] Almusaed, A., Yitmen, I., Almssad, A., 2023. Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future‐Built Environments. Sustainability. 15(18), 13464. DOI: https://doi.org/10.3390/su151813464
  30. [30] Liang, C.J., Le, T.H., Ham, Y., et al., 2024. Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry. Automation in Construction. 162, 105369. https://arxiv.org/pdf/2310.05414
  31. [31] Arroyo, P., Schöttle, A., Christensen, R., 2022. A Shared Responsibility: Ethical and Social Dilemmas of Using AI in the AEC Industry. In: Lean Construction 4.0. Routledge. pp. 68–81. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003150930-7
  32. [32] Emaminejad, N., Akhavian, R., 2022. Trustworthy AI and robotics: Implications for the AEC industry. Automation in Construction. 139, 104298. DOI: https://doi.org/10.1016/j.autcon.2022.104298
  33. [33] Shamreeva, A., Doroschkin, A., 2021. Analysis of the influencing factors for the practical application of BIM in combination with AI in Germany. In: CRC Press eBooks. pp. 536–543. DOI: https://doi.org/10.1201/9781003191476-72
  34. [34] An, Y., Li, H., Su, T., et al., 2021. Determining Uncertainties in AI Applications in AEC Sector and their Corresponding Mitigation Strategies. Automation in Construction. 131, 103883. DOI: https://doi.org/10.1016/j.autcon.2021.103883
  35. [35] Panagoulia, E., Rakha, T., 2023. Data Reliability in BIM and Performance Analytics: A Survey of Contemporary AECO practice. Journal of Architectural Engineering. 29(2). DOI: https://doi.org/10.1061/jaeied.aeeng‐1483

How to Cite

Anshul Jain, Hridayesh Varma. Artificial Intelligence Based Site Selection for the Construction of Megastructures in Urban areas. Urban Planning and Construction. 24 May 2025. https://doi.org/10.55121/upc.v3i1.536
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