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	acadia18_176
id	acadia18_176
authors	Bidgoli, Ardavan; Veloso,Pedro
year	2018
title	DeepCloud. The Application of a Data-driven, Generative Model in Design
doi	https://doi.org/10.52842/conf.acadia.2018.176
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 176-185
summary	Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.
keywords	full paper, design tools software computing + gaming, ai & machine learning, generative design, autoencoders
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

_id	caadria2018_056
id	caadria2018_056
authors	Chirkin, Artem, Pishniy, Maxim and Sender, Arina
year	2018
title	Generilized Visibility-Based Design Evaluation Using GPU
doi	https://doi.org/10.52842/conf.caadria.2018.2.483
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 483-492
summary	Visibility plays an important role in perception and use of an urban design, and thus often becomes a target of design analysis. This work presents a fast method of evaluating various visibility-based design characteristics, such as isovists or insolation exploiting the GPU rendering pipeline and compute shaders. The proposed method employs a two-stage algorithm on each point of interest. First, it projects the visible space around a vantage point onto an equirectangular map. Second, it folds the map using a flexibly defined function into a single value that is associated with the vantage point. Being executed on a grid of points in a 3D scene, it can be visualized as a heat map or utilized by another algorithm for further design analysis. The developed system provides nearly real-time analysis tools for an early-stage design process to a broad audience via web services.
keywords	design analysis; design evaluation; GPU; isovist; insolation
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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. 382-393.
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia18_156
id	acadia18_156
authors	Huang, Weixin; Zheng, Hao
year	2018
title	Architectural Drawings Recognition and Generation through Machine Learning
doi	https://doi.org/10.52842/conf.acadia.2018.156
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 156-165
summary	With the development of information technology, the ideas of programming and mass calculation were introduced into the design field, resulting in the growth of computer- aided design. With the idea of designing by data, we began to manipulate data directly, and interpret data through design works. Machine Learning as a decision making tool has been widely used in many fields. It can be used to analyze large amounts of data and predict future changes. Generative Adversarial Network (GAN) is a model framework in machine learning. It’s specially designed to learn and generate output data with similar or identical characteristics. Pix2pixHD is a modified version of GAN that learns image data in pairs and generates new images based on the input. The author applied pix2pixHD in recognizing and generating architectural drawings, marking rooms with different colors and then generating apartment plans through two convolutional neural networks. Next, in order to understand how these networks work, the author analyzed their framework, and provided an explanation of the three working principles of the networks, convolution layer, residual network layer and deconvolution layer. Lastly, in order to visualize the networks in architectural drawings, the author derived data from different layer and different training epochs, and visualized the findings as gray scale images. It was found that the features of the architectural plan drawings have been gradually learned and stored as parameters in the networks. As the networks get deeper and the training epoch increases, the features in the graph become more concise and clearer. This phenomenon may be inspiring in understanding the designing behavior of humans.
keywords	full paper, design study, generative design, ai + machine learning, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:49

_id	caadria2018_173
id	caadria2018_173
authors	Stouffs, Rudi
year	2018
title	A Triple Graph Grammar Approach to Mapping IFC Models into CityGML Building Models
doi	https://doi.org/10.52842/conf.caadria.2018.2.041
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 41-50
summary	A triple graph grammar approach is adopted as a formal framework for semantic and geometric conversion of IFC models into CityGML Level of Detail 3/4 building models. The triple graph grammar approach supports a semantic mapping from IFC to CityGML, the generation of conversion routines from this mapping, and an incremental approach to achieving a "complete and near-lossless" mapping. The objective of this approach is the development of a methodology and algorithms to automate the conversion of Building Information Models into CityGML building models, capturing both geometric and semantic information as available in the BIM models, in order to create semantically enriched 3D city models that include both exterior and interior structures.
keywords	BIM; CityGML; conversion; semantic; automated
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2018_w03
id	ecaade2018_w03
authors	Dorta, Tomás, Beaudry Marchand, Emmanuel and Sopher, Hadas
year	2018
title	Co-Design in HYVE-3D - Representational Ecosystem, Design Conversations and Knowledge Construction Activities
doi	https://doi.org/10.52842/conf.ecaade.2018.1.053
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. 53-56
summary	The aim of this workshop is to introduce participants to the co-design approach using a Social VR system (without headsets): Hyve-3D (Hybrid Virtual Environment 3D). The system affords simultaneous multi-user co-design (local and remote) using 3D sketches (exportable as vectors) and imported 3D textured geometries, photogrammetry models and point-clouds. Participants will be trained to use the suitable representational ecosystem and the verbal protocols specific for co-design as a particular kind of collaborative design where each will be simultaneously ideating ad-hoc projects instead of cooperating (where individual designs are put together in a later stage).
series	eCAADe
email
last changed	2022/06/07 07:55

_id	ecaade2018_145
id	ecaade2018_145
authors	Fukuda, Tomohiro, Zhu, Yuehan and Yabuki, Nobuyoshi
year	2018
title	Point Cloud Stream on Spatial Mixed Reality - Toward Telepresence in Architectural Field
doi	https://doi.org/10.52842/conf.ecaade.2018.2.727
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 727-734
summary	In remote meetings that involve the study of buildings and cities, sharing three-dimensional (3D) virtual spatial of buildings and cities is just as necessary as sharing the appearances and voices of meeting participants. Because of this, system development and pilot projects have attempted to share 3D virtual models via the internet in real-time but is still insufficient compared with face-to-face meeting. Therefore, this research explores the applicability of a spatial mixed reality (MR) system that displays point cloud streams to realize 3D remote meeting in architecture and urban fields. MR is a new technology that enables 3D presentations of various information, combining the physical and virtual worlds. One MR method is telepresence, which is expected to give people a way to communicate remotely as if face to face in a realistic way. We first developed a MR system named PcsMR (Point cloud stream on mixed reality) to display point cloud streams. The PcsMR system's operation consists of generating and transferring a point cloud stream and then rendering a point cloud stream using MR. The PcsMR acquired the point cloud stream in real-time using Kinect for Windows v2 and transferred it to Microsoft HoloLens, which uses optical see-through MR. Then we constructed two prototypes based on PcsMR and carried out pilot projects. Through observing the experiments, application possibilities for architecture and urban fields are found in meetings and communications that share real-time 3D objects and include the movement of remote participants and objects. The proposed method was evaluated feasible and effective.
keywords	Telepresence; Mixed reality; Point cloud stream; Remote meeting; Real time
series	eCAADe
email
last changed	2022/06/07 07:50

_id	sigradi2018_1619
id	sigradi2018_1619
authors	Agirbas, Asli
year	2018
title	Creating Non-standard Spaces via 3D Modeling and Simulation: A Case Study
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 1051-1058
summary	Especially in the film industry, architectural spaces away from Euclidean geometry are brought to foreground. The best environment in which such spaces can be designed, is undoubtedly the 3D modeling environment. In this study, an experimental study was carried out on the creation of alternative spaces with undergraduate architectural students. Via using 3D modeling and various simulation techniques in the Maya software, students created spaces, which were away from the traditional architectural spaces. Thus, in addition to learning the 3D modeling software, architectural students learned to use animation and simulation as a part of design, not just as a presentation tool, and opening up new horizons for non-standard spaces was provided.
keywords	3D Modeling; Simulation; Animation; CAAD; Maya; Non-standard spaces
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	ijac201816406
id	ijac201816406
authors	As, Imdat; Siddharth Pal and Prithwish Basu
year	2018
title	Artificial intelligence in architecture: Generating conceptual design via deep learning
source	International Journal of Architectural Computing vol. 16 - no. 4, 306-327
summary	Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph- based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.
keywords	Architectural design, conceptual design, deep learning, artificial intelligence, generative design
series	journal
email
last changed	2019/08/07 14:04

_id	caadria2018_086
id	caadria2018_086
authors	Castelo Branco, Renata and Leit?o, António
year	2018
title	Algorithmic Architectural Visualization
doi	https://doi.org/10.52842/conf.caadria.2018.2.557
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 557-566
summary	Digitally-generated visualizations, such as renders or movies, are, nowadays, commonly used as representation methods for architectural creations. This occurs not only in final stages of the process, with the goal of selling the product's image, but also in midst creation process to express concepts and ideas. Presently, the spread of parametric and algorithmic approaches to design creates a problem for visualization, as it enables the almost effortless change of 3D models, thus requiring repeated visualization efforts to keep up with the changes applied to the design. To solve this, we propose extending the algorithmic design approach to also include the high-level description of architectural image creation. The methodology, Algorithmic Architectural Visualization (AAV), also contemplates the required preparation settings for the visualization process, and includes possible visualization productions inspired by film techniques.
keywords	Algorithmic Design; Architectural Visualization; Render; Film Grammar
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2018_118
id	caadria2018_118
authors	Chen, Zi-Ru, Liao, Chien-Jung and Chu, Chih-Hsing
year	2018
title	An Assembly Guidance System of Tou Kung Based on Augmented Reality
doi	https://doi.org/10.52842/conf.caadria.2018.1.349
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 349-358
summary	Tou kung represent Chinese architecture. Due to the difficulty of learning from ancient books, some develop 3D assembly models. Still, there are limits while using 2D images for assembly instructions. The purpose of this study is to explore whether the application of AR technology can guide the process of tou kung assembly and address the recognition gap between paper illustrations and the physical assembly process. The method is to observes the user's tou kung assembly behavior and performance. Then the study proposed an dynamic simulation AR guidance system to help people not only understand the structure, but also the culture behind to reach the goal of education promotion.
keywords	Augmented Reality; Tou-Kung; assembly
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	caadria2018_301
id	caadria2018_301
authors	Fereos, Pavlos, Tsiliakos, Marios and Jaschke, Clara
year	2018
title	Spaceship Architecture - A Sci-Fi Pedagogical Approach to Design Computation
doi	https://doi.org/10.52842/conf.caadria.2018.1.081
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 81-90
summary	The analysis of make-belief drawings and models of Sci-Fi spaceships and architecture, leaves architects usually in absence of interior, material or program information. The spatial depth of sci-fi digital or physical models is virtually non-existent and unresolved. This discrepancy within sci-fi scenarios inspired the development of an integrated teaching methodology within design studios, with the academic objective to utilize computational methods for analysis, reproduction and eventually composition, while assessing its capacity to achieve a successful assimilation of design computation in the curriculum. The Spaceship Architecture Design Studio at University of Innsbruck's Institute for Experimental Architecture.hochbau follows a procedural approach in which the design objective is not predefined. Yet, it aims to be 'outside of this world' as a sci-fi architectural quality-enriched result of our reality, via a design oriented course with immersive computational strategies.
keywords	pedagogy; computation; sci-fi; academia; teaching
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2018_270
id	caadria2018_270
authors	Houda, Maryam and Reinhardt, Dagmar
year	2018
title	Structural Optimisation for 3D Printing Bespoke Geometries
doi	https://doi.org/10.52842/conf.caadria.2018.1.235
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 235-244
summary	Current advances in 3D printing technology enable novel design explorations with the potential to inform printing deposition through generative scripting and structural performance analysis. This paper presents ongoing research that involves three scales of operation; a global geometry for multi-skin cellular mesh densities; localised skin-porosity detailing, and material structural optimisation. Centering on a chair as a test case scenario, the research explores the affordances of a serialised, multi-material 3D printing process in the context of digital instruction, customisation, and material efficiency. The paper discusses two case studies with consecutive optimisation, and outlines the benefits and limitations of 3D printing for structural optimisation and multi-material grading in the additive process.
keywords	3D Printing; Bespoke Complexity; Digital Instruction; Mass Customisation; Multi-Material Grading; Robotic Deposition; Structural Optimisation
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ecaade2018_315
id	ecaade2018_315
authors	Koehler, Daniel, Abo Saleh, Sheghaf, Li, Hua, Ye, Chuwei, Zhou, Yaonaijia and Navasaityte, Rasa
year	2018
title	Mereologies - Combinatorial Design and the Description of Urban Form.
doi	https://doi.org/10.52842/conf.ecaade.2018.2.085
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 85-94
summary	This paper discusses the ability to apply machine learning to the combinatorial design-assembly at the scale of a building to urban form. Connecting the historical lines of discrete automata in computer science and formal studies in architecture this research contributes to the field of additive material assemblies, aggregative architecture and their possible upscaling to urban design. The following case studies are a preparation to apply deep-learning on the computational descriptions of urban form. Departing from the game Go as a testbed for the development of deep-learning applications, an equivalent platform can be designed for architectural assembly. By this, the form of a building is defined via the overlap between separate building parts. Building on part-relations, this research uses mereology as a term for a set of recursive assembly strategies, integrated into the design aspects of the building parts. The models developed by research by design are formally described and tested under a digital simulation environment. The shown case study shows the process of how to transform geometrical elements to architectural parts based merely on their compositional aspects either in horizontal or three-dimensional arrangements.
keywords	Urban Form; Discrete Automata ; Combinatorics; Part-Relations; Mereology; Aggregative Architecture
series	eCAADe
email
last changed	2022/06/07 07:51

_id	caadria2018_268
id	caadria2018_268
authors	Lim, Joie, Janssen, Patrick and Stouffs, Rudi
year	2018
title	Automated Generation of BIM Models from 2D CAD Drawings
doi	https://doi.org/10.52842/conf.caadria.2018.2.061
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 61-70
summary	Existing buildings are often lacking BIM models. This paper proposes a method to semi-automate the generation of BIM models from 2D CAD drawings. The method has two parts: the first part, 2D CAD drawing preparation, involves cleaning the drawings to obtain simplified 2D input geometry and the second, 3D BIM model generation, involves generating and extracting parameters to generate 3D BIM components. This research focuses on the semi-automation of the second part. The the model is generated storey by storey, with each building element type being processed. A demonstration was carried out for a case-study building. The main modelling strategies used by the method are described and key challenges are discussed.
keywords	BIM; CAD drawings; conversion; generation; Grasshopper
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2018_050
id	caadria2018_050
authors	Lo, Tian Tian and Schnabel, Marc Aurel
year	2018
title	Virtual & Augmented Studio Environment (VASE) - Developing the Virtual Reality Eco-System for Design Studios
doi	https://doi.org/10.52842/conf.caadria.2018.1.443
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 443-452
summary	Virtual Reality (VR) is being revived in major disciplines, including architecture. VR is no longer only employed for basic operations, such as construction of 3D models, dynamic renderings, closed-loop interaction, inside-out perspective and enhance sensory feedback. This paper explains how over the past twenty years technologies and software have evolved that a new eco-system for design processes have risen. This paper discusses how students made full use of both software and equipment in the whole design process; from ideas exploration to site analysis to form generation to design realization. Students have been exposed to a whole range of digital software tools in the beginning. As most of them were already familiar with modelling software, they have in particular been introduced to animation software, game engines and even 3D documentation software such as photogrammetry. Most importantly, they were led to IVE. The paper points out the benefits of adopting such methodology and the difficulties faced by the students at the various stages of the design process.
keywords	Design Studio; Virtual Reality; Software and Equipment; Design Exchange
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2018_083
id	caadria2018_083
authors	Luo, Dan, Wang, Jinsong and Xu, Weiguo
year	2018
title	Robotic Automatic Generation of Performance Model for Non-Uniform Linear Material via Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2018.1.039
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 39-48
summary	In the following research, a systematic approach is developed to generate an experiment-based performance model that computes and customizes properties of non-uniform linear materials to accommodate the form of designated curve under bending and natural force. In this case, the test subject is an elastomer strip of non-uniform sections. A novel solution is provided to obtain sufficient training data required for deep learning with an automatic material testing mechanism combining robotic arm automation and image recognition. The collected training data are fed into a deep combination of neural networks to generate a material performance model. Unlike most traditional performance models that are only able to simulate the final form from the properties and initial conditions of the given materials, the trained neural network offers a two-way performance model that is also able to compute appropriate material properties of non-uniform materials from target curves. This network achieves complex forms with minimal and effective programmed materials with complicated nonlinear properties and behaving under natural forces.
keywords	Material performance model; Deep Learning; Robotic automation; Material computation; Neural network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaaderis2023_45
id	ecaaderis2023_45
authors	Morton, David, Ahmed, Tarek MF and Humphery, Richard
year	2023
title	BIM and Teaching in Architecture: Current thinking and approaches
source	De Luca, F, Lykouras, I and Wurzer, G (eds.), Proceedings of the 9th eCAADe Regional International Symposium, TalTech, 15 - 16 June 2023, pp. 105–115
summary	Increasing use of BIM has represented a continuing shift in traditional assumptions on how we navigate the design process. BIM is affording the student the ability to gain a greater understanding of their design ideas via the exploration of scale, spatial organisation and structure, amongst many other design layers, in increasing levels of detail, at the same point in the design process. Architectural education is at a delayed tipping point where architectural students are increasingly looking towards BIM to streamline their design process drawn by the production of realistic visualisation, but with a lack of knowledge and skill in its application. With a lack of guidance and understanding around the application of BIM, the use of BIM in this manner overlooks the potential of BIM to construct and test virtual simulations of proposed schemes, to support design enquiry. A historical concern for the pedagogy constructed around the students’ design process is the application of methods and techniques that support the progression through the design process, (Ambrose, 2014; dash mei & Safari, 2018). This study examines the design process of architectural students and the interaction between analogue and digital methods used in design. These primary modes of communication, offer the opportunity to query the roles and rules of traditional architectural conventions around ‘problem finding’ and ‘problem solving’, challenging the ‘traditional’ design process examined by pioneers like Bruner (1966) and Schon (1987). These approaches are distilled from the findings of the study and presented as guidance to those teaching in architectural aBIMemia to align pedagogic goals to methods of abstraction in this new era of design education reconsidering digital methods in design.
keywords	BIM, BIM, Design Process, Architecture, Learning
series	eCAADe
email
last changed	2024/02/05 14:28

_id	caadria2018_062
id	caadria2018_062
authors	Narengerel, Amartuvshin, Hong, Sukjoo, Lee, Chae-Seok and Lee, Ji-Hyun
year	2018
title	FBSMAP: The Spatial Representation Method for Intelligent Semantic Service in Indoor Environment
doi	https://doi.org/10.52842/conf.caadria.2018.2.587
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 587-596
summary	In order to provide intelligent services in complex and diverse indoor environments, it is necessary to understand spatial features of indoor objects: furniture and items. Function-Behavior-Structure Map (FBSMAP), which is a novel indoor representation method that focuses on space functionality for intelligent semantic services, is introduced in this study. The three steps of FBSMAP are defining spatial components, constructing semantic map for indoor environment, and securing spatial features. This novel implementation method is implemented and examined on 3D house models.
keywords	Indoor Representation Method; Semantic Space; Spatial Subdivision; IndoorGML; Furniture Semantics
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	sigradi2018_1364
id	sigradi2018_1364
authors	Nunes de Vasconcelos, Guilherme; de Sousa Van Stralen, Mateus; Menezes, Alexandre; Gontijo Ramos, Fernando Murilo
year	2018
title	Perceive to learn to perceive: an experience with virtual reality devices for architecture design learning
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 985-990
summary	This work investigates the potential use of low-cost virtual reality (VR) devices in architectural education to improve spatial perception of undergraduate architecture students. The experiment involved a gradual approach into the design process, starting with an intervention on a physical space, its bidimensional representation, 3d modelling and immersion in VR. After the immersion, students answered a questionnaire with open and closed-questions about their experience, and their evaluation of the use of VR in the designing. The findings point to the use of VR as a means to explore, perceive and reflect on decisions, allowing students a better understanding of designing.
keywords	Virtual reality; Architectural design; Architecture teaching; Representation; Low-cost devices
series	SIGRADI
email
last changed	2021/03/28 19:59

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