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	ecaade2020_017
id	ecaade2020_017
authors	Chan, Yick Hin Edwin and Spaeth, A. Benjamin
year	2020
title	Architectural Visualisation with Conditional Generative Adversarial Networks (cGAN). - What machines read in architectural sketches.
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 299-308
doi	https://doi.org/10.52842/conf.ecaade.2020.2.299
summary	As a form of visual reasoning, sketching is a human cognitive activity instrumental to architectural design. In the process of sketching, abstract sketches invoke new mental imageries and subsequently lead to new sketches. This iterative transformation is repeated until the final design emerges. Artificial Intelligence and Deep Neural Networks have been developed to imitate human cognitive processes. Amongst these networks, the Conditional Generative Adversarial Network (cGAN) has been developed for image-to-image translation and is able to generate realistic images from abstract sketches. To mimic the cyclic process of abstracting and imaging in architectural concept design, a Cyclic-cGAN that consists of two cGANs is proposed in this paper. The first cGAN transforms sketches to images, while the second from images to sketches. The training of the Cyclic-cGAN is presented and its performance illustrated by using two sketches from well-known architects, and two from architecture students. The results show that the proposed Cyclic-cGAN can emulate architects' mode of visual reasoning through sketching. This novel approach of utilising deep neural networks may open the door for further development of Artificial Intelligence in assisting architects in conceptual design.
keywords	visual cognition; design computation; machine learning; artificial intelligence
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2020_342
id	caadria2020_342
authors	Han, Yoojin and Lee, Hyunsoo
year	2020
title	A Deep Learning Approach for Brand Store Image and Positioning - Auto-generation of Brand Positioning Maps Using Image Classification
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 689-696
doi	https://doi.org/10.52842/conf.caadria.2020.2.689
summary	This paper presents a deep learning approach to measuring brand store image and generating positioning maps. The rise of signature brand stores can be explained in terms of brand identity. Store design and architecture have been highlighted as effective communicators of brand identity and position but, in terms of spatial environment, have been studied solely using qualitative approaches. This study adopted a deep learning-based image classification model as an alternative methodology for measuring brand image and positioning, which are conventionally considered highly subjective. The results demonstrate that a consistent, coherent, and strong brand identity can be trained and recognized using deep learning technology. A brand positioning map can also be created based on predicted scores derived by deep learning. This paper also suggests wider uses for this approach to branding and architectural design.
keywords	Deep Learning; Image Classification; Brand Identity; Brand Positioning Map; Brand Store Design
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ecaade2020_093
id	ecaade2020_093
authors	Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	An Academy of Spatial Agents - Generating spatial configurations with deep reinforcement learning
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 191-200
doi	https://doi.org/10.52842/conf.ecaade.2020.2.191
summary	Agent-based models rely on decentralized decision making instantiated in the interactions between agents and the environment. In the context of generative design, agent-based models can enable decentralized geometric modelling, provide partial information about the generative process, and enable fine-grained interaction. However, the existing agent-based models originate from non-architectural problems and it is not straight-forward to adapt them for spatial design. To address this, we introduce a method to create custom spatial agents that can satisfy architectural requirements and support fine-grained interaction using multi-agent deep reinforcement learning (MADRL). We focus on a proof of concept where agents control spatial partitions and interact in an environment (represented as a grid) to satisfy custom goals (shape, area, adjacency, etc.). This approach uses double deep Q-network (DDQN) combined with a dynamic convolutional neural-network (DCNN). We report an experiment where trained agents generalize their knowledge to different settings, consistently explore good spatial configurations, and quickly recover from perturbations in the action selection.
keywords	space planning; agent-based model; interactive generative systems; artificial intelligence; multi-agent deep reinforcement learning
series	eCAADe
email
last changed	2022/06/07 07:58

_id	caadria2020_446
id	caadria2020_446
authors	Cho, Dahngyu, Kim, Jinsung, Shin, Eunseo, Choi, Jungsik and Lee, Jin-Kook
year	2020
title	Recognizing Architectural Objects in Floor-plan Drawings Using Deep-learning Style-transfer Algorithms
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 717-725
doi	https://doi.org/10.52842/conf.caadria.2020.2.717
summary	This paper describes an approach of recognizing floor plans by assorting essential objects of the plan using deep-learning based style transfer algorithms. Previously, the recognition of floor plans in the design and remodeling phase was labor-intensive, requiring expert-dependent and manual interpretation. For a computer to take in the imaged architectural plan information, the symbols in the plan must be understood. However, the computer has difficulty in extracting information directly from the preexisting plans due to the different conditions of the plans. The goal is to change the preexisting plans to an integrated format to improve the readability by transferring their style into a comprehensible way using Conditional Generative Adversarial Networks (cGAN). About 100-floor plans were used for the dataset which was previously constructed by the Ministry of Land, Infrastructure, and Transport of Korea. The proposed approach has such two steps: (1) to define the important objects contained in the floor plan which needs to be extracted and (2) to use the defined objects as training input data for the cGAN style transfer model. In this paper, wall, door, and window objects were selected as the target for extraction. The preexisting floor plans would be segmented into each part, altered into a consistent format which would then contribute to automatically extracting information for further utilization.
keywords	Architectural objects; floor plan recognition; deep-learning; style-transfer
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2019_134
id	cdrf2019_134
authors	Zhen Han, Wei Yan, and Gang Liu
year	2020
title	A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_13
summary	In recent years, generative design methods are widely used to guide urban or architectural design. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The agent arranges one building in the site at one time in a training episode according to the observation. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. The test results have proven that the method is a potentially effective way for assisting urban design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_196
id	ecaade2020_196
authors	Paiva Ponzio, Angelica, Prazeres Veloso de Souza, Leonardo, Mateus Schulz, Victor and Lasso, Cindy
year	2020
title	Digital Understandings in the Construction of Knowledge - Report of experiences in contemporary architectural design teaching
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. 675-684
doi	https://doi.org/10.52842/conf.ecaade.2020.1.675
summary	As part of an ongoing research on the study of digital tools envisioning innovation in the design process, this article intends to demonstrate how analogical and digital design thinking techniques can improve and expand the range of creative methodologies in the context of an undergraduate architectural design studio. The approach presented builds on the improvement of a theoretical-didactic model during three strategies, each aiming at different steps of the design process. The first one explores analog design thinking techniques on the initial concept decisions, the following demonstrates the joint use of parametric and BIM tools as an alternative resource for generating complex forms, and the last one presents BIM technology as a pedagogical instrument for learning a constructive system. Thus, besides presenting the methods, instruments, products, and results generated, this paper will also discuss the gains and difficulties faced, appointing a new approach to undergo in the future.
keywords	Digital Design process; Architectural design teaching; Design thinking; Parametricism
series	eCAADe
email
last changed	2022/06/07 08:00

_id	acadia20_282
id	acadia20_282
authors	Steinfeld, Kyle
year	2020
title	Drawn, Together
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 282-289.
doi	https://doi.org/10.52842/conf.acadia.2020.1.282
summary	Changes in the media through which design proceeds are often associated with the emergence of novel design practices and new subjectivities. While the dynamic between design tools and design practices is complex and nondeterministic, there are moments when rapid development in one of these areas catalyzes changes in the other. The nascent integration of machine learning (ML) processes into computer-aided design suggests that we are in just such a moment. It is in this context that an undergraduate research studio was conducted at UC Berkeley in the spring of 2020. By introducing novice students to a set of experimental tools (Steinfeld 2020) and processes based on ML techniques, this studio seeks to uncover those original practices or new subjectivities that might thereby arise. We describe here a series of small design projects that examine the applicability of such tools to early-stage architectural design. Specifically, we document the integration of several conditional text-generation models and conditional image-generation models into undergraduate architectural design pedagogy, and evaluate their use as “creative provocateurs” at the start of a design. After surveying the resulting student work and documenting the studio experience, we conclude that the approach taken here suggests promising new modalities of design authorship, and we offer reflections that may serve as a useful guide for the more widespread adoption of machine-augmented design tools in architectural practice.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_045
id	caadria2020_045
authors	Zheng, Hao and Ren, Yue
year	2020
title	Machine Learning Neural Networks Construction and Analysis in Vectorized Design Drawings
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 707-716
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
summary	Machine Learning, a recently prevalent research domain in data prediction and analysis, has been widely used in a variety of fields. In the design field, especially for architectural design, a machine learning method to learn and generate design data as pixelized images has been developed in previous researches. However, proceeding pixelized image data will cause the problems of precision loss and calculation waste, since the geometric architectural design data is efficiently stored and presented as vectorized CAD files. Thus, in this article, the author developed a specific machine learning neural network to learn and predict design drawings as vectorized data, speeding up the learning and predicting process, while improving the accuracy. First, two necessary geometric tests have been successfully done, which shows the central concept of neural network construct. Then, a design rule prediction model was built to demonstrate the methods to optimize the neural network and data structure. Lastly, a generation model based on human-made design data was constructed, which can be used to predict and generate the bedroom furniture positions by inputting the boundary data of the room, door, and window.
keywords	Machine Learning; Artificial Intelligence; Generative Design; Geometric Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 228-236.
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_402
id	caadria2020_402
authors	Ezzat, Mohammed
year	2020
title	A Framework for a Comprehensive Conceptualization of Urban Constructs - SpatialNet and SpatialFeaturesNet for computer-aided creative urban design
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 111-120
doi	https://doi.org/10.52842/conf.caadria.2020.2.111
summary	Analogy is thought to be foundational for designing and for design creativity. Nonetheless, practicing analogical reasoning needs a knowledge-base. The paper proposes a framework for constructing a knowledge-base of urban constructs that builds on an ontology of urbanism. The framework is composed of two modules that are responsible for representing either the concepts or the features of any urban constructs' materialization. The concepts are represented as a knowledge graph (KG) named SpatialNet, while the physical features are represented by a deep neural network (DNN) called SpatialFeaturesNet. For structuring SpatialNet, as a KG that comprehensively conceptualizes spatial qualities, deep learning applied to natural language processing (NLP) is employed. The comprehensive concepts of SpatialNet are firstly discovered using semantic analyses of nine English lingual corpora and then structured using the urban ontology. The goal of the framework is to map the spatial features to the plethora of their matching concepts. The granularity Ã nd the coherence of the proposed framework is expected to sustain or substitute other known analogical, knowledge-based, inspirational design approaches such as case-based reasoning (CBR) and its analogical application on architectural design (CBD).
keywords	Domain-specific knowledge graph of urban qualities; Deep neural network for structuring KG; Natural language processing and comprehensive understanding of urban constructs; Urban cognition and design creativity; Case-based reasoning (CBR) and case-based design (CBD)
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ijac202018101
id	ijac202018101
authors	Karakiewicz, Justyna
year	2020
title	Design is real, complex, inclusive, emergent and evil
source	International Journal of Architectural Computing vol. 18 - no. 1, 5-19
summary	Can computers make our designs more intelligent and better informed? This is the implication of the theme of the special issue. Architectural design is often thought of as the design of the object, and design models of architecture seek to explicate this process. As an architect, however, I cannot subscribe to that view. In this particular article, I will explore how computational approaches have illuminated and expanded my work to enable the interaction of these themes across scores of projects. Underpinning the projects are foundational concepts: design is real, complex, inclusive, emergent and evil. Design is grounded in reality and facts, that we can derive design outcomes from a deep and unblemished understanding of the world around us. It is not a stylistic escape. Reality is complex. Architectural design has sought to simplify. This was inescapable when projects are so large yet need to be communicated succinctly. ‘Less is more’ justified this approach. In town planning, this is evident in the tool of zoning. Parse the problem and then address each piece. What we do is part of a larger effort. The field of architecture seeks distinction. Design theories want to distinguish and elevate architecture. But if design is complex and it is real, then it is tied to messy realism. Designing has to become accessible to other realms of knowledge. Designing is the seeking of opportunity. For many, design is simply finding the answer – think of Herbert Simon’s statement that design is problem solving. Design reveals opportunities, and these emergent conditions are to be grasped. As designers, our decisions have implications. We know now that what we build has future implications in ways that are profound. When we define design as problem solving, we ignore the truth that design is problem making.
keywords	Design, panarchy, CAS, complexity, Digital Project, Galapagos
series	journal
email
last changed	2020/11/02 13:34

_id	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 669-678
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2020_404
id	ecaade2020_404
authors	Singh, Manav Mahan, Schneider-Marin, Patricia, Harter, Hannes, Lang, Werner and Geyer, Philipp
year	2020
title	Applying Deep Learning and Databases for Energy-efficient Architectural Design - Abstract
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 79-87
doi	https://doi.org/10.52842/conf.ecaade.2020.2.079
summary	The reduction of energy consumption of buildings requires consideration in early design phases. However, modelling and computation time required for dynamic energy simulations makes them inappropriate in the early phases. This paper presents a performance prediction approach for these phases that is embedded in a multi-level-of-development modelling approach. First, parametric pre-trained modular deep learning components are embedded in the building elements. The energy performance is predicted by composing these components. Second, embodied energy assessment is performed by extracting the information from a database. A calculation module queries the database and calculates the embodied energy. Both, embodied and operational, energy are assembled to predict lifecycle energy demand. The method has been implemented prototypically in a digital modelling environment Revit. A case study serves to demonstrate the application process, the user interaction and the information flows. It shows energy prediction in early design phases to enhance the environmental performance of the building.
keywords	BIM; Operational Energy; Embodied Energy; Life-cycle Energy Demand; Early Design Phases
series	eCAADe
email
last changed	2022/06/07 07:56

_id	caadria2020_203
id	caadria2020_203
authors	Xiao, Yahan, Chen, Sen, Ikeda, Yasushi and Hotta, Kensuke
year	2020
title	Automatic Recognition and Segmentation of Architectural Elements from 2D Drawings by Convolutional Neural Network
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 843-852
doi	https://doi.org/10.52842/conf.caadria.2020.1.843
summary	The BIM modeling process is the most time-consuming aspect. This paper studies the possibility of applying the recognition and segmentation of architectural components by deep learning to assist automatic BIM modeling. The research has two parts: the first one is dataset preparing, that images with the labeled architectural components from an original CAD drawing are made for the network training, and second is training and testing, that a mature network which has been trained in hundreds of labeled images is used to make predictions. The utilization of the current study results is discussed and the optimization method as well.
keywords	BIM; CAD drawings; Recognition and Segmentation; Convolutional Neural Network; Computer vision
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ijac202321102
id	ijac202321102
authors	Özerol, Gizem; Semra Arslan Selçuk
year	2023
title	Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 1, pp. 23–41
summary	Abstract Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers.
keywords	Artificial intelligence, machine learning, deep learning, architectural research, bibliometric analysis
series	journal
last changed	2024/04/17 14:30

_id	ecaade2020_253
id	ecaade2020_253
authors	Buš, Peter
year	2020
title	User-driven Configurable Architectural Assemblies - Towards artificial intelligence-embedded responsive environments
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 483-490
doi	https://doi.org/10.52842/conf.ecaade.2020.2.483
summary	The paper theoretically elaborates the idea of individual users' customisation activities to create and configure responsive spatial scenarios by means of reconfigurable interactive adaptive assemblies. It reflects Gordon Pask's concept of human and device interaction based on its unpredictable notion speculating a potential to be enhanced by artificial intelligence learning approach of an assembly linked with human activator's participative inputs. Such a link of artificial intelligence, human agency and interactive assembly capable to generate its own spatial configurations by itself and users' stimuli may lead to a new understanding of humans' role in the creation of spatial scenarios. The occupants take the prime role in the evolution of spatial conditions in this respect. The paper aims to position an interaction between the human agents and artificial devices as a participatory and responsive design act to facilitate creative potential of participants as unique individuals without pre-specified or pre-programmed goal set by the designer. Such an approach will pave a way towards true autonomy of responsive built environments, determined by an individual human agent and behaviour of the spatial assemblies to create authentic responsive built forms in a digital and physical space.
keywords	deployable systems; responsive assemblies; embedded intelligence; Learning-to-Design-and-Assembly method; Conversation Theory
series	eCAADe
email
last changed	2022/06/07 07:54

_id	cdrf2019_159
id	cdrf2019_159
authors	Hang Zhang and Ye Huang
year	2020
title	Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_15
summary	In the past few years, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field such as building facades generation or floor plans generation, etc. However, due to the relatively slow development of 3D machine learning algorithms, 3D architecture form exploration through machine learning is still a difficult issue for architects. As a result, most of these applications are confined to the level of 2D. Based on the state-of-the-art 2D image generation algorithm, also the method of spatial sequence rules, this article proposes a brand-new strategy of encoding, decoding, and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. This method could provide some innovative design possibilities that generate the latent 3D forms between several different architectural styles. Benefited from the 2D network advantages and the image amplification network nested outside the benchmark network, we have significantly expanded the resolution of training results when compared with the existing form-finding algorithm and related achievements in recent years
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_156
id	ecaade2020_156
authors	Hemmerling, Marco and Maris, Simon
year	2020
title	INTERCOM - A platform for collaborative design processes
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 173-180
doi	https://doi.org/10.52842/conf.ecaade.2020.2.173
summary	The INTERCOM project propounds a cloud-based collaboration platform for digital planning processes in architecture. The concept is based on an openBIM approach and ensures open access for all partners involved. At its core it provides IFC-based and model-related online tools for planning, communication and collaboration. The interaction with the model and the exchange with other project partners takes place in real-time via a model-related chat and BCF exports. In addition, the integration of e-learning modules (e.g. video tutorials, wikis, project documents) encourages problem solving through further education. Especially the integration of communication and collaboration tools is supposed to enhance the decision making throughout the design process and become a key factor for a successful and coordinated BIM process. Primarily INTERCOM has been developed as a prototype for teaching BIM in interdisciplinary teams. Subsequently, the application can also be adopted for professional practice. The paper evaluates previous experiences from BIM cloud teaching and discusses the conception and development of the proposed collaborative platform.
keywords	architecture curriculum; didactics; building information modeling (BIM); collaborative design process; common data environment (CDE)
series	eCAADe
email
last changed	2022/06/07 07:49

_id	ecaade2018_103
id	ecaade2018_103
authors	Kepczynska-Walczak, Anetta
year	2018
title	Building Information Modelling for 2020+ Realm - Contemporary practice and future perspectives
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 271-280
doi	https://doi.org/10.52842/conf.ecaade.2018.1.271
summary	The paper discusses the future possible trajectories of information technologies applied to Architecture, Engineering and Construction (AEC) domain. Specifically, it focuses on Building Information Modelling (BIM) being a key subject in the context of understanding the challenge of computing for a better tomorrow. In this respect it presents Polish situation as one of the European Union countries aiming at implementing BIM on the national level. What is more, it reveals findings derived from experience of teaching BIM and from questionnaires prepared for BIM learners. A comparative study of two types of representatives, viz. architecture students and experienced professionals, both acquiring BIM skills, has been conducted. The results show different approach and key obstacles associated with teaching, learning and comprehending BIM. Furthermore, on the one hand the study reveals discrepancy between research, academic experiments and everyday practice. On the other hand it emphasises specific characteristics of this domain enhanced with dynamic pace of change in technology, leading to conclusions that BIM should be placed on lifelong learning trajectory. Despite numerous obstacles the adoption of BIM is facing it concludes that it has arguments and potential to become 2020+ realm.
keywords	Building Information Modelling; BIM; Lifelong Learning; architectural practice
series	eCAADe
email
last changed	2022/06/07 07:52

_id	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 601–610
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget, as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly, through early predictions of the structure’s material, and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

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