Self-learning Buildings: integrating Artificial Intelligence to create a building that can adapt to future challenges

Author:

Maksoud Aref,Al-Beer Hayder Basel,Mushtaha Emad,Yahia Moohammed Wasim

Abstract

Abstract Adaptability is a crucial quality in nature, and Artificial Intelligence (AI) provides leverage for adaptability in Architecture. In this paper, AI is integrated to create Self-learning buildings that can adapt to future challenges. The aim of this study is to make buildings that collect data from their environment through sensors and adapt themselves according to these data. The approach followed in this study is divided into different phases. Phase 1 starts by making an extensive research on the use of AI in Architecture. The data that was gathered from that research in phase 1 was used as guidelines to design the building in phase 2. The design of the building that is in phase 2 follows a parametric approach with the help of machine learning in the form of computational design tools. An algorithm was designed with Rhino modeling & Grasshopper Scripting to generate forms that not only biomimicks the Coral Growth process but also adapt that form to the selected site of the project. Phase 3 shows the selection process for the generated experimental studies. Multiple analyses were made such as sunlight, radiation, and shadow analysis to select the best performing form in terms of energy use. In phase 4, the form is developed to increase the building’s performance. In phase 5, performance analyses are done to prove that resultant form is a climate or environmentally responsive form which have high levels of adaptability. The analysis showed that the radiation exposure of this building is between 200 and 300 kWh/m². The shadow analysis shows the building form provides a shadow length of 8 hours. The analyses proves that the building’s form reduces its energy use thus makes it adaptable. In the last phase, an AI engine system is used to predict the future expansion of the building. Integrating technology in the architecture of future buildings provides adaptable buildings and helps save some of the energy used by buildings and thus build a sustainable planet.

Publisher

IOP Publishing

Subject

General Engineering

Reference30 articles.

1. 63906 Vision based self learning mobile robot using machine learning algorithms(Robotics and Mechatronics);Choi;The Proceedings of the Asian Conference on Multibody Dynamics,2010

2. Editorial: Artificial Intelligence and Human Movement in Industries and Creation;Dimitropoulos;Frontiers in Robotics and AI,2021

3. The New Design Considerations in the Residential Buildings’ Interiors at the Post-Corona (COVID-19) Era;A Hendy;Journal of Advanced Research in Dynamical and Control Systems,2020

4. Biomanufacturing the Future: Biodigital Architecture & Genetics;Estévez;Procedia Manufacturing,2017

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