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	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
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
doi	https://doi.org/10.52842/conf.caadria.2018.1.039
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	ecaade2018_176
id	ecaade2018_176
authors	Fisher-Gewirtzman, Dafna and Polak, Nir
year	2018
title	Integrating Crowdsourcing & Gamification in an Automatic Architectural Synthesis Process
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. 439-444
doi	https://doi.org/10.52842/conf.ecaade.2018.1.439
summary	This work covers the methodological approach that is used to gather information from the wisdom of crowd, to be utilized in a machine learning process for the automatic generation of minimal apartment units. The flexibility in the synthesis process enables the generation of apartment units that seem to be random and some are unsuitable for dwelling. Thus, the synthesis process is required to classify units based on their suitability. The classification is deduced from opinions of human participants on previously generated units. As the definition of "suitability" may be subjective, this work offers a crowdsourcing method in order to reach a large number of participants, that as a whole would allow to produce an objective classification. Gaming elements have been adopted to make the crowdsourcing process more intuitive and inviting for external participants.
keywords	crowdsourcing and gamification; urban density; optimization; automated architecture synthesis; minimum apartments; visual openness
series	eCAADe
email
last changed	2022/06/07 07:51

_id	caadria2018_290
id	caadria2018_290
authors	Wang, Zhenyu, Shi, Jia, Yu, Chuanfei and Gao, Guoyuan
year	2018
title	Automatic Design of Main Pedestrian Entrance of Building Site Based on Machine Learning - A Case Study of Museums in China's Urban Environment
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. 227-235
doi	https://doi.org/10.52842/conf.caadria.2018.2.227
summary	The main pedestrian entrance of the building site has a direct influence on the use of the buildings, so the selection of the main pedestrian entrance is very important in the process of architectural design. The correct selection of the main pedestrian entrance of building site depends on the experience of designers and environment data collected by designers, the process is time consuming and inefficient, especially when the building site located in complex urban environment. In order to improve the efficiency of design process, we used online map to collect museums information in China as training samples, and constructing artificial neural networks to predict the direction of the main pedestrian entrance. After the training, we get the prediction model with 79% prediction accuracy. Although the accuracy still need to be improved, it creates a new approach to analysis the main pedestrian entrance of the site and worth further researching.
keywords	Artificial Neural Network (ANN); Main Pedestrian Entrance of Building Site; Automatic Design
series	CAADRIA
email
last changed	2022/06/07 07:58

_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
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.
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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_166
id	acadia18_166
authors	Kvochick, Tyler
year	2018
title	Sneaky Spatial Segmentation. Reading Architectural Drawings with Deep Neural Networks and Without Labeling Data
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. 166-175
doi	https://doi.org/10.52842/conf.acadia.2018.166
summary	Currently, it is nearly impossible for an artificial neural network to generalize a task from very few examples. Humans, however, excel at this. For instance, it is not necessary for a designer to see thousands or millions of unique examples of how to place a given drawing symbol in a way that meets the economic, aesthetic, and performative goals of the project. In fact, the goals can be (and usually are) communicated abstractly in natural language. Machine learning (ML) models, however, do need numerous examples. The methods that we explore here are an attempt to circumvent this in order to make ML models more immediately useful. In this work, we present progress on the application of contemporary ML techniques to the design process in the architecture, engineering, and construction (AEC) industry. We introduce a technique to partially circumvent the data hungriness of neural networks, which is a significant impediment to their application outside of the ML research community. We also show results on the applicability of this technique to real-world drawings and present research that addresses how some fundamental attributes of drawings as images affect the way they are interpreted in deep neural networks. Our primary contribution is a technique to train a neural network to segment real-world architectural drawings after using only generated pseudodrawings.
keywords	full paper, representation + perception, computation, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:51

_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_303
id	caadria2018_303
authors	Song, Jae Yeol, Kim, Jin Sung, Kim, Hayan, Choi, Jungsik and Lee, Jin Kook
year	2018
title	Approach to Capturing Design Requirements from the Existing Architectural Documents Using Natural Language Processing Technique
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. 247-254
doi	https://doi.org/10.52842/conf.caadria.2018.2.247
summary	This paper describes an approach to utilizing natural language processing (NLP) to capture design requirements from the natural language-based architectural documents. In various design stage of the architectural process, there are several different kinds of documents describing requirements for buildings. Capturing the design requirements from those documents is based on extracting information of objects, their properties, and relations. Until recently, interpreting and extracting that information from documents are almost done by a manual process. To intelligently automate the conventional process, the computer has to understand the semantics of natural languages. In this regards, this paper suggests an approach to utilizing NLP for semantic analysis which enables the computer to understand the semantics of the given text data. The proposed approach has following steps: 1) extract noun words which mostly represent objects and property data in Korean Building Act; 2) analyze the semantic relations between words, using NLP and deep learning; 3) Based on domain database, translate the noun words in objects and properties data and find out their relations.
keywords	NLP (Natural Language Processing); Deep learning; Design requirements; Korean Building Act; Semantic analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2018_438
id	ecaade2018_438
authors	Das, Subhajit
year	2018
title	Interactive Artificial Life Based Systems, Augmenting Design Generation and Evaluation by Embedding Expert Opinion - A Human Machine dialogue for form finding.
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. 85-94
doi	https://doi.org/10.52842/conf.ecaade.2018.1.085
summary	Evolution of natural life and subsequently selection of life forms is an interesting topic that has been explored multiple times. This area of research and its application has high relevance in evolutionary design and automated design generation. Taking inspiration from Charles Darwin's theory, all biological species were formed by the process of evolution based on natural selection of the fittest (Darwin, n.d.) this paper explains exploratory research showcasing semi-automatic design generation. This is realized by an interactive artificial selection tool, where the designer or the end user makes key decisions steering the propagation and breeding of future design artifacts. This paper, describes two prototypes and their use cases, highlighting interaction based optimal design selection. One of the prototypes explains a 2d organic shape creator using a metaball shape approach, while the other discusses a spatial layout generation technique for conceptual design.
keywords	design generation; implicit surfaces; artificial life; decision making; artificial selection; spatial layout generation
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2018_281
id	caadria2018_281
authors	Lee, Jisun and Lee, Hyunsoo
year	2018
title	Pneumatic Skin with Adaptive Openings - Adaptive Façade with Opening Control Integrated with CFD for Natural Ventilation
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. 143-151
doi	https://doi.org/10.52842/conf.caadria.2018.2.143
summary	The unique integration of geometries and techniques allows the natural organisms to adapt to different environments in creative ways. In this study, a bio-inspired pneumatic facade is presented as a strategy to improve the efficiency of natural ventilation performance by controlling the adaptive openings. The Computational Fluid Dynamics simulation has been conducted to visualize airflows in order to explore how the changing configurations of openings enhance natural ventilation efficiency. The airflows are investigated with changes in wind speed and direction to find out the opening configurations which provide indoor airflows at the comfort level of velocities. As results, it was shown that indoor air velocities were modulated by controlling opening sizes, geometries and positions of the openings, and it was a beneficial strategy to apply the optimized opening configurations implementing automatic control. Also, the air distribution can be enhanced by changing opening configurations in changing conditions of wind speed and direction. An effective methodology for an intelligent façade opening control to encourage natural ventilation is presented in this study to deliver users comfort and efficiency.
keywords	Natural ventilation; airflow simulation; pneumatic facade; Computational Fluid Dynamics
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaade2018_108
id	ecaade2018_108
authors	Luo, Dan, Wang, Jingsong and Xu, Weiguo
year	2018
title	Applied Automatic Machine Learning Process for Material Computation
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. 109-118
doi	https://doi.org/10.52842/conf.ecaade.2018.1.109
summary	Machine learning enables computers to learn without being explicitly programmed. This paper outlines state-of-the-art implementations of machine learning approaches to the study of physical material properties based on Elastomer we developed, which combines with robotic automation and image recognition to generate a computable material model for non-uniform linear Elastomer material. The development of the neural network includes a few preliminary experiments to confirm the feasibility and the influential parameters used to define the final RNN neural network, the study of the inputs and the quality of the testing samples influencing the accuracy of the output model, and the evaluation of the generated material model as well as the method itself. To conclude, this paper expands such methods to the possible architectural implications on other non-uniform materials, such as the performance of wood sheets with different grains and tensile material made from composite materials.
keywords	neural network; robotic; material computation; automation
series	eCAADe
email
last changed	2022/06/07 07:59

_id	caadria2018_044
id	caadria2018_044
authors	Inoue, Kazuya, Fukuda, Tomohiro, Cao, Rui and Yabuki, Nobuyoshi
year	2018
title	Tracking Robustness and Green View Index Estimation of Augmented and Diminished Reality for Environmental Design - PhotoAR+DR2017 project
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. 339-348
doi	https://doi.org/10.52842/conf.caadria.2018.1.339
summary	To assess an environmental design, augmented and diminished reality (AR/DR) have a potential to build a consensus more smoothly through the landscape simulation of new design visualization of the items to be assessed, such as the green view index. However, the current system is still considered to be impractical because it does not provide complete user experience. Thus, we aim to improve the robustness of the AR/DR system and to integrate the estimation of the green view index into the AR/DR system on a game engine. Further, we achieve an improved stable tracking by eliminating the outliers of the tracking reference points using the random sample consensus (RANSAC) method and by defining the tracking reference points over an extensive area of the AR/DR display. Additionally, two modules were implemented, among which one module is used to solve the occlusion problem while the other is used to estimate the green view index. The novel integrated AR/DR system with all modules was developed on the game engine. A mock design project was developed in an outdoor environment for simulation purposes, thereby verifying the applicability of the developed system.
keywords	Environmental Design; Augmented Reality (AR); Diminished Reality (DR); Green View Index; Segmentation
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia18_216
id	acadia18_216
authors	Ahrens, Chandler; Chamberlain, Roger; Mitchell, Scott; Barnstorff, Adam
year	2018
title	Catoptric Surface
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. 216-225
doi	https://doi.org/10.52842/conf.acadia.2018.216
summary	The Catoptric Surface research project explores methods of reflecting daylight through a building envelope to form an image-based pattern of light on the interior environment. This research investigates the generation of atmospheric effects from daylighting projected onto architectural surfaces within a built environment in an attempt to amplify or reduce spatial perception. The mapping of variable organizations of light onto existing or new surfaces creates a condition where the perception of space does not rely on form alone. This condition creates a visual effect of a formless atmosphere and affects the way people use the space. Often the desired quantity and quality of daylight varies due to factors such as physiological differences due to age or the types of tasks people perform (Lechner 2009). Yet the dominant mode of thought toward the use of daylighting tends to promote a homogeneous environment, in that the resulting lighting level is the same throughout a space. This research project questions the desire for uniform lighting levels in favor of variegated and heterogeneous conditions. The main objective of this research is the production of a unique facade system that is capable of dynamically redirecting daylight to key locations deep within a building. Mirrors in a vertical array are individually adjusted via stepper motors in order to reflect more or less intense daylight into the interior space according to sun position and an image-based map. The image-based approach provides a way to specifically target lighting conditions, atmospheric effects, and the perception of space.
keywords	full paper, non-production robotics, representation + perception, performance + simulation, building technologies
series	ACADIA
type	paper
email
last changed	2022/06/07 07:54

_id	caadria2018_086
id	caadria2018_086
authors	Castelo Branco, Renata and Leit?o, António
year	2018
title	Algorithmic Architectural Visualization
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
doi	https://doi.org/10.52842/conf.caadria.2018.2.557
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	acadia18_386
id	acadia18_386
authors	Chen, Canhui; Burry, Jane
year	2018
title	(Re)calibrating Construction Simplicity and Design Complexity
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. 386-393
doi	https://doi.org/10.52842/conf.acadia.2018.386
summary	Construction simplicity is crucial to cost control, however design complexity is often necessary in order to meet particular spatial performance criteria. This paper presents a case study of a semi-enclosed meeting pod that has a brief that must contend with the seemingly contradictory conditions of the necessary geometric complexities imperative to improved acoustic performance and cost control in construction. A series of deep oculi are introduced as architectural elements to link the pod interior to the outside environment. Their reveals also introduce sound reflection and scattering, which contribute to the main acoustic goal of improved speech privacy. Represented as a three-dimensional funnel like shape, the reveal to each opening is unique in size, depth and angle. Traditionally, the manufacturing of such bespoke architectural elements in many cases resulted in lengthy and costly manufacturing processes. This paper investigates how the complex oculi shape variations can be manufactured using one universal mold. A workflow using mathematical and computational operations, a standardized fabrication approach and customization through tooling results in a high precision digital process to create particular calculated geometries, recalibrated at each stage to account for the paradoxical inexactitudes and inevitable tolerances.
keywords	work in progress,tolerance, developable surface, form finding, construction simplicity, material behavior
series	ACADIA
type	paper
email
last changed	2022/06/07 07:55

_id	caadria2018_245
id	caadria2018_245
authors	Chowdhury, Shuva and Schnabel, Marc Aurel
year	2018
title	An Algorithmic Methodology to Predict Urban Form - An Instrument for Urban Design
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. 401-410
doi	https://doi.org/10.52842/conf.caadria.2018.2.401
summary	We question the recent practices of conventional and participatory urban design approaches and offer a middle approach by exploring computational design tools in the design system. On the one hand, the top-down urban planning approaches investigate urban form as a holistic matter which only can be calibrated by urban professionals. These approaches are not able to offer enough information to the end users to predict the urban form. On the other hand, the bottom-up urban design approaches cannot visualise predicted urban scenarios, and most often the design decisions stay as general assumptions. We developed and tested a parametric design platform combines both approaches where all the stakeholders can participate and visualise multiple urban scenarios in real-time feedback. Parametric design along with CIM modelling system has influenced urban designers for a new endeavour in urban design. This paper presents a methodology to generate and visualise urban form. We present a novel decision-making platform that combines city level and local neighbourhood data to aid participatory urban design decisions. The platform allows for stakeholder collaboration and engagement in complex urban design processes.
keywords	knowledge-based system; algorithmic methodology ; design decision tool; urban form;
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2018_399
id	ecaade2018_399
authors	Cutellic, Pierre
year	2018
title	UCHRON - An Event-Based Generative Design Software Implementing Fast Discriminative Cognitive Responses from Visual ERP BCI
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. 131-138
doi	https://doi.org/10.52842/conf.ecaade.2018.2.131
summary	This research aims at investigating BCI technologies in the broad scope of CAAD applications exploiting early visual cognition in computational design. More precisely, this paper will describe the investigation of key BCI and ML components for the implementation and development of a software supporting this research : Uchron. It will be organised as follows. Firstly, it will introduce the pursued interest and contribution that visual-ERP EEG based BCI application for Generative Design may provide through a synthetic review of precedents and BCI technology. Secondly, selected BCI components will be described and a methodology will be presented to provide an appropriate framework for a CAAD software approach. This section main focus is on the processing component of the BCI. It distinguishes two key aspects of discrimination and generation in its design and proposes a new model based on GAN for modulated adversarial design. Emphasis will be made on the explicit use of inference loops integrating fast human cognitive responses and its individual capitalisation through time in order to reflect towards the generation of design and architectural features.
keywords	Human Computer Interaction; Neurodesign; Generative Design; Design Computing and Cognition; Machine Learning
series	eCAADe
email
last changed	2022/06/07 07:56

_id	caadria2018_292
id	caadria2018_292
authors	Eid Mohamed, Basem, ElKaftangui, Mohamed and Zureikat, Rana
year	2018
title	{In}Formed Panels - Towards Rethinking the Precast Concrete Industry in the UAE
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. 287-296
doi	https://doi.org/10.52842/conf.caadria.2018.1.287
summary	The convergence of digital design and fabrication technologies have offered architects and designers the means by which to develop customized architectural artifacts, ones that goes beyond the standards of "one size fits all". Such applications have been applied extensively in various architectural practices, and specifically in the realm of industrialized building production, given that they present a suitable model. Although unrecognized within standard precast concrete production, current research acknowledges the need for advanced computer applications for shifting the industry into a digitized process. This paper represent a critical phase of an ongoing research endeavor that aims at rethinking the precast concrete production in the UAE, and MENA region for housing typologies. The project explores possibilities of a new protocol that is focused from design to production, relying on performative design strategies, and possible optimized for large format 3D printing of concrete elements. The aim is to develop an integrated façade panels system that is tailored for design and production; an approach that goes beyond current industry practices.
keywords	Precast Concrete; Industrialized Construction; Evolutionary Design; Optimization
series	CAADRIA
email
last changed	2022/06/07 07:55

_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
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
doi	https://doi.org/10.52842/conf.caadria.2018.1.081
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_161
id	caadria2018_161
authors	Huang, Xiaoran, White, Marcus and Burry, Mark
year	2018
title	Design Globally, Immerse Locally - A Synthetic Design Approach by Integrating Agent Based Modelling with Virtual Reality
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. 473-482
doi	https://doi.org/10.52842/conf.caadria.2018.1.473
summary	The last three decades have witnessed the explosion of technology and its impact on the architecture discipline which has drastically changed the methods of design. New techniques such as Agent-based modeling (ABM) and Virtual Reality (VR) have been widely implemented in architectural and urban design domains, yet the potential integration between these two methods remains arguably unexploited. The investigation in this paper aims to probe the following questions: How can architects and urban designers be informed more comprehensively by melding ABM and VR techniques at the preliminary/conceptual design stage? Which platform is considered more appropriate to facilitate a user-friendly system and reduces the steep learning curve? And what are the potential benefits of this approach in architectural education, particularly for the design studio environment? With those questions, we proposed a prototype in Unity, a multi-platform development tool that originated from the game industry, to simulate and visualize pedestrian behaviors in urban environments with immersive design experience and tested it in a scenario-based case study. This approach has also been further tested in an architectural design studio, demonstrating its technical feasibility as well as the potential contributions to the pedagogy.
keywords	Agent based modelling; Virtual Reality; Urban Design
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	acadia18_118
id	acadia18_118
authors	Kalantari, Saleh; Contreras-Vidal, Jose Luis; Smith, Joshua Stanton; Cruz-Garza, Jesus; Banner, Pamela
year	2018
title	Evaluating Educational Settings through Biometric Data and Virtual Response Testing
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. 118-125
doi	https://doi.org/10.52842/conf.acadia.2018.118
summary	The physical design of the learning environment has been shown to contribute significantly to student performance and educational outcomes. However, the existing literature on this topic relies primarily on generalized observations rather than on rigorous empirical testing. Broad trends in environmental impacts have been noted, but there is a lack of detailed evidence about how specific design variables can affect learning performance. The goal of this study was to apply a new approach in examining classroom design innovations. We developed a protocol to evaluate the effectiveness of classroom designs by measuring the physical responses of study participants as they interacted with different designs using a virtual reality platform. Our hypothesis was that virtual “test runs” can help designers to identify potential problems and successes in their work prior to its being physically constructed. The results of our initial pilot study indicated that this approach could yield important results about human responses to classroom design, and that the virtual environment seemed to be a reliable testing substitute when compared against real classroom environments. In addition to leading toward practical conclusions about specific classroom design variables, this project provides a new kind of research method and toolset to test the potential human impacts of a wide variety of architectural innovations.
keywords	work in progress, signal processing, eeg, virtual reality, big data, learning performance
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

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