Introduction: With the rapid development in computational design, both architectural design and representation processes have witnessed a revolutionary change from the analog to the digital medium, opening new doors for adaptability in the architectural design process by leveraging nature concepts in design. The computational design approach starts with the mathematical model definition based on numerical relations and equations, thus, replacing the standard visual representation. Purpose of the study: We aimed to integrate computational design technologies to create self-learning buildings that could adapt to environmental challenges and adjust accordingly by collecting data from the surrounding environment via the implementation of sensors. Methods: We started with extensive research on state-of-the-art computational design in architecture, followed by the design implementation and the implementation of the architectural design of a building. The design followed a parametric approach to design and strategies. An algorithm was developed with Grasshopper Scripting to generate documents that mimic the growth process of cellular bone structures and adapt that form to a selected project site. To ensure that the generated form is adaptable, we performed multiple analyses, such as sunlight, radiation, and shadow analysis, before selecting the form and finishing its development. The results show that an environmentally responsive form that extends from the surrounding environment is characterized by high levels of adaptability. Results: In the course of the study, the effectiveness of computational design technologies in architecture was established.

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.

Adaptability is a crucial quality in nature, and Artificial Intelligence (AI) provides leverage for adaptability in Architecture. This paper integrates AI to create self-learning buildings that can adapt to future challenges. The aim is to make buildings that collect data from their environment through sensors and adjust according to these data. The approach followed in this paper starts making an extensive research on the use of AI in Architecture. The data gathered from that research was then used to design this building. The design of this building follows a parametric approach with the help of machine learning in the form of computational design tools. An algorithm was designed with Grasshopper Scripting to generate documents that biomimetics the Coral Growth process and adapt that form to the selected site of the project. To ensure that the generated form is adaptable, multiple analyses were made, such as sunlight, radiation, and shadow analysis, before selecting the form and finishing the form development. The result was an environmentally responsive form that extends from the site's surroundings, which have high levels of adaptability. 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.