AI driven Foundation and Material Recommendation for Sustainable Construction

Authors

  • Adhil M
  • Adithyan M S
  • Ajithkumar A. B.
  • Anna Lesly
  • Asha Joseph

Keywords:

Foundation, Material Selection, AI model, Sustainability Introduction

Abstract

In the realm of sustainable architecture and civil engineering, optimizing foundational and material components is pivotal for achieving structural stability and environmental efficiency. An innovative AI-driven parametric design tool tailored to address these crucial aspects by leveraging real-time environmental data and material properties. The tool aims to enhance decision-making, reduce carbon footprint, and ensure cost-effective construction practices. The Foundation Model plays a vital role by utilizing comprehensive soil data, including specific gravity, and moisture content, collected from multiple borehole samples. Processed through SQLite, this model provides precise foundation type recommendations, ensuring stability and cost efficiency under diverse geological conditions. The Material Model evaluates essential material properties such as thermal conductivity, water absorption, permeability, and durability. It emphasizes selecting sustainable building materials tailored to different climate zones, promoting energy efficiency and reducing environmental impact. By focusing on these two core components, the project demonstrates the potential of AI in transforming traditional construction methodologies. This offers a forward-thinking approach to material and foundation optimization, contributing to the advancement of sustainable architecture. Through data-driven insights and automation, it paves the way for more resilient and eco-friendly building designs

References

Anil K Berwal and Manisha Yadav. “Retrofit Strategy in Existing Building for Implementing Energy Conservation Building Code in India: ECBC”. In: European Journal of Engineering and Technology Research 6.5 (2021), pp. 134–140.

Saidjon Kamolov. “Machine learning methods in civil engineering: a systematic review”. In: Annals of Mathematics and Computer Science 21 (2024), pp. 181–191.

Richard J De Dear and Gail S Brager. “Thermal comfort in naturally ventilated buildings: revisions to ASHRAE Standard 55”. In: Energy and buildings 34.6 (2002), pp. 549–561.

Rana Elnaklah, Ian Walker, and Sukumar Natarajan. “Moving to a green building: Indoor environment quality, thermal comfort and health”. In: Building and Environment 191 (2021), p. 107592.

Anurag Aman Kaushal et al. “Thermal comfort in Indian naturally ventilated buildings: A comprehensive review”. In: Energy and Buildings (2024), p. 113923.

Nourredine Kebaili et al. “Satisfaction and representations of raw earth habitat: a case study of Auroville, India”. In: Journal of Asian Architecture and Building Engineering (2023), pp. 1–23.

G Kiran Kumar, S Saboor, and TP Ashok Babu. “Experimental and theoretical studies of window glazing materials of green energy building in Indian climatic zones”. In: Energy Procedia 109 (2017), pp. 306–313.

Sofia Real et al. “Thermal performance of compressed earth blocks stabilised with thermoactivated recycled cement”. In: Energy and Buildings 314 (2024), p. 114288

Mustika Sari et al. “Machine learning model for green building design prediction”. In: IAES International Journal of Artificial Intelligence 11.4 (2022), p. 1525.

Ji-Woo Kim et al. “Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures”. In: Journal of Imaging 10.4 (2024), p. 93.

Tanveer Ahmad et al. “Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities”. In: Journal of Cleaner Production 289 (2021), p. 125834.

Shakti Suman et al. “Slope stability analysis using artificial intelligence techniques”. In: Natural Hazards 84 (2016), pp. 727–748.

Published

30-08-2025

How to Cite

Adhil M, Adithyan M S, Ajithkumar A. B., Anna Lesly, & Asha Joseph. (2025). AI driven Foundation and Material Recommendation for Sustainable Construction. Journal of Applied Science, Engineering, Technology and Management, 3(2), 15–18. Retrieved from https://jasetm.fisat.ac.in/index.php/jasetm/article/view/49

Issue

Vol. 3 No. 2 (2025): Journal of Applied Science, Engineering, Technology and Management

Section

Articles

License

Copyright (c) 2025 Adhil M, Adithyan M S , Ajithkumar A. B. , Anna Lesly, Asha Joseph

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after the acceptance, with an acknowledgement of its initial publication in this journal, as it can lead to productive exchanges, as well as earlier and greater citation of published work.