Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	ecaade2024_167
id	ecaade2024_167
authors	Alammar, Ammar; Alymani, Abdulrahman; Jabi, Wassim
year	2024
title	Building Energy Efficiency Estimations with Random Forest for Single and Multi-Zones
doi	https://doi.org/10.52842/conf.ecaade.2024.2.365
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 365–374
summary	Surrogate models (SM) present an opportunity for rapid assessment of a building's performance, surpassing the pace of simulation-based methods. Setting up a simulation for a single concept involves defining numerous parameters, disrupting the architect's creative flow due to extended simulation run times. Therefore, this research explores integrating building energy analysis with advanced machine learning techniques to predict heating and cooling loads (KWh/m2) for single and multi-zones in buildings. To generate the dataset, the study adopts a parametric generative workflow, building upon Chou and Bui's (2014) methodology. This dataset encompasses multiple building forms, each with unique topological connections and attributes, ensuring a thorough analysis across varied building scenarios. These scenarios undergo thermal simulation to generate data for machine learning analysis. The study primarily utilizes Random Forest (RF) as a new technique to estimate the heating and cooling loads in buildings, a critical factor in building energy efficiency. Following that, A random search approach is utilized to optimize the hyperparameters, enhancing the robustness and accuracy of the machine learning models employed later in the research. The RF algorithms demonstrate high performance in predicting heating and cooling loads (KWh/m2), contributing to enhanced building energy efficiency. The study underscores the potential of machine learning in optimizing building designs for energy efficiency.
keywords	Heating and Cooling loads, Topology, Machine learning, Random Forest
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2024_47
id	ecaade2024_47
authors	Alymani, Abdulrahman Ahmed A
year	2024
title	Integrating Artificial Intelligence Rendering Tools in Design: Integrating AI as teaching methods in architectural education
doi	https://doi.org/10.52842/conf.ecaade.2024.2.629
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 629–638
summary	This paper introduces an innovative teaching approach for architectural design studios, emphasizing the integration of AI-rendering tools to enhance student learning and creativity. The method begins with conventional site analysis, followed by an in-depth study of a micro-home case study to deepen understanding. Students’ progress from traditional 2D plans to conceptual 3D massing, facing challenges in integrating case studies into their designs. To address this, an AI-rendering engine is incorporated, allowing students to add intricate details and apply various case studies directly onto their 3D models. This visual approach aids understanding and application of architectural concepts. The paper discusses how this approach helps students overcome integration challenges and fosters creative exploration. Findings suggest that this method enriches architectural education, offering a new dimension to design studio learning.
keywords	Architectural Pedagogy, AI-Rendering Tools, Architecture Precedents, Architecture Case Study, Design Studios
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2020_193
id	ecaade2020_193
authors	Alymani, Abdulrahman, Jabi, Wassim and Corcoran, Padraig
year	2020
title	Machine Learning Methods for Clustering Architectural Precedents - Classifying the relationship between building and ground
doi	https://doi.org/10.52842/conf.ecaade.2020.1.643
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 643-652
summary	Every time an object is built, it creates a relationship with the ground. Architects have a full responsibility to design the building by taking the ground into consideration. In the field of architecture, using data mining to identify any unusual patterns or emergent architectural trends is a nascent area that has yet to be fully explored. Clustering techniques are an essential tool in this process for organising large datasets. In this paper, we propose a novel proof-of-concept workflow that enables a machine learning computer system to cluster aspects of an architect's building design style with respect to how the buildings in question relate to the ground. The experimental workflow in this paper consists of two stages. In the first stage, we use a database system to collect, organise and store several significant architectural precedents. The second stage examines the most well-known unsupervised learning algorithm clustering techniques which are: K-Means, K-Modes and Gaussian Mixture Models. Our experiments demonstrated that the K-means clustering algorithm method achieves a level of accuracy that is higher than other clustering methods. This research points to the potential of AI in helping designers identify the typological and topological characteristics of architectural solutions and place them within the most relevant architectural canons
keywords	Machine Learning; Building and Ground Relationship; Clustering Algorithms; K-means cluster Algorithms
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia23_v3_179
id	acadia23_v3_179
authors	Jabi, Wassim; Leon, David Andres; Alymani, Abdulrahman; Behzad, Selda Pourali; Salamoun, Michelle
year	2023
title	Exploring Building Topology Through Graph Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	Graph theory offers a powerful method for analyzing complex networks and relationships. When combined with machine learning, graph theory can provide valuable insights into the data generated by 3D models. This workshop integrated advanced spatial modeling and analysis with artificial intelligence, highlighting the importance of technological advancements in shaping the future of architecture and design. It introduced participants to novel workflows that link parametric 3D modeling with concepts of topology, graph theory, and graph machine learning. We used Topologicpy, an advanced spatial modeling and analysis software library designed for Architecture, Engineering, and Construction, paired with DGL, a powerful machine learning library that provides tools for implementing and optimizing graph neural networks (Figure 1). In essence, this process blends cutting-edge technologies and architectural principles that will shape the future of design. Participants learned how to use these workflows to convert 3D models into graphs, analyze their properties, and perform classification and regression tasks. Participants also explored how to create synthetic datasets based on generative and parametric workflows, and build and optimize graph neural networks for specific tasks.
series	ACADIA
type	workshop
last changed	2024/04/17 14:00

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