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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Reformat results as: short short into frame detailed detailed into frame

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

_id	ecaade2020_432
id	ecaade2020_432
authors	Fragkia, Vasiliki and Worre Foged, Isak
year	2020
title	Methods for the Prediction and Specification of Functionally Graded Multi-Grain Responsive Timber Composites
doi	https://doi.org/10.52842/conf.ecaade.2020.2.585
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. 585-594
summary	The paper presents design-integrated methods for high-resolution specification and prediction of functionally graded wood-based thermal responsive composites, using machine learning. The objective is the development of new circular design workflow, employing robotic fabrication, in order to predict fabrication files linked to material performance and design requirements, focused on application for intrinsic responsive and adaptive architectural surfaces. Through an experimental case study, the paper explores how machine learning can form a predictive design framework where low-resolution data can solve material systems at high resolution. The experimental computational and prototyping studies show that the presented image-based machine learning method can be adopted and adapted across various stages and scales of architectural design and fabrication. This in turn allows for a design-per-requirement approach that optimizes material distribution and promotes material economy.
keywords	material specification; responsive timber composites; machine learning; robotic fabrication; building envelopes
series	eCAADe
email
last changed	2022/06/07 07:50

_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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
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
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

_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
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
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
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
doi	https://doi.org/10.52842/conf.ecaade.2020.2.079
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
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	acadia20_236p
id	acadia20_236p
authors	Anton, Ana; Jipa, Andrei; Reiter, Lex; Dillenburger, Benjamin
year	2020
title	Fast Complexity
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 236-241
summary	The concrete industry is responsible for 8% of the global CO2 emissions. Therefore, using concrete in more complex and optimized shapes can have a significant benefit to the environment. Digital fabrication with concrete aims to overcome the geometric limitations of standardized formworks and thereby reduce the ecological footprint of the building industry. One of the most significant material economy potentials is in structural slabs because they represent 85% of the weight of multi-story concrete structures. To address this opportunity, Fast Complexity proposes an automated fabrication process for highly optimized slabs with ornamented soffits. The method combines reusable 3D-printed formwork (3DPF) and 3D concrete printing (3DCP). 3DPF uses binder-jetting, a process with submillimetre resolution. A polyester coating is applied to ensure reusability and smooth concrete surfaces otherwise not achievable with 3DCP alone. 3DPF is selectively used only where high-quality finishing is necessary, while all other surfaces are fabricated formwork-free with 3DCP. The 3DCP process was developed interdisciplinary at ETH Zürich and employs a two-component material system consisting of Portland cement mortar and calcium aluminate cement accelerator paste. This fabrication process provides a seamless transition from digital casting to 3DCP in a continuous automated process. Fast Complexity selectively uses two complementary additive manufacturing methods, optimizing the fabrication speed. In this regard, the prototype exhibits two different surface qualities, reflecting the specific resolutions of the two digital processes. 3DCP inherits the fine resolution of the 3DPF strictly for the smooth, visible surfaces of the soffit, for which aesthetics are essential. In contrast, the hidden parts of the slab use the coarse resolution specific to the 3DCP process, not requiring any formwork and implicitly achieving faster fabrication. In the context of an increased interest in construction additive manufacturing, Fast Complexity explicitly addresses the low resolution, lack of geometric freedom, and limited reinforcement options typical to layered extrusion 3DCP, as well as the limited customizability in concrete technology.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ecaade2020_089
id	ecaade2020_089
authors	Ardic, Sabiha Irem, Kirdar, Gulce and Lima, Angela Barros
year	2020
title	An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case - Measuring popularity based on POIs, accessibility and perceptual quality parameters
doi	https://doi.org/10.52842/conf.ecaade.2020.2.309
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. 309-318
summary	The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity - population change, accessibility by walk - density of POIs, and the feeling of safety - social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.
keywords	big data approach; neighborhood analysis; popularity; point of interest (POI); accessibility; perceptual quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2020_076
id	ecaade2020_076
authors	Bai, Nan, Azadi, Shervin, Nourian, Pirouz and Pereira Roders, Ana
year	2020
title	Decision-Making as a Social Choice Game - Gamifying an urban redevelopment process in search for consensus
doi	https://doi.org/10.52842/conf.ecaade.2020.2.555
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. 555-564
summary	The paper reports the formulation, the design, and the results of a serious game developed for structuring negotiations concerning the redevelopment of a university campus with various stakeholders. The main aim of this research was to formulate the redevelopment planning problem as an abstract and discrete decision-making problem involving multiple actions, multiple actors with preconceived gains and losses with respect to the comprising actions, and decisions as combinations of actions. Using fictitious and yet realistic scenarios and stakeholders as simulation, the results evidence how different levels of democratic participation and different modes of moderation can affect reaching a consensus and present in a mathematical characterisation of a consensus as a state of equilibrium. The small set of actions and actors enabled a chance to compute a theoretically optimal state of consensus, where the efficiency and the effectiveness of different modes of moderation and participatory rights could be observed and analysed.
keywords	Serious Game; Consensus Building; Democratization; Game Theory; Social Decision
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia20_202p
id	acadia20_202p
authors	Battaglia, Christopher A.; Verian, Kho; Miller, Martin F.
year	2020
title	DE:Stress Pavilion
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 202-207
summary	Print-Cast Concrete investigates concrete 3D printing utilizing robotically fabricated recyclable green sand molds for the fabrication of thin shell architecture. The presented process expedites the production of doubly curved concrete geometries by replacing traditional formwork casting or horizontal corbeling with spatial concrete arching by developing a three-dimensional extrusion path for deposition. Creating robust non-zero Gaussian curvature in concrete, this method increases fabrication speed for mass customized elements eliminating two-part mold casting by combining robotic 3D printing and extrusion casting. Through the casting component of this method, concrete 3D prints have greater resolution along the edge condition resulting in tighter assembly tolerances between multiple aggregated components. Print-Cast Concrete was developed to produce a full-scale architectural installation commissioned for Exhibit Columbus 2019. The concrete 3D printed compression shell spanned 12 meters in length, 5 meters in width, and 3 meters in height and consisted of 110 bespoke panels ranging in weight of 45 kg to 160 kg per panel. Geometrical constraints were determined by the bounding box of compressed sand mold blanks and tooling parameters of both CNC milling and concrete extrusion. Using this construction method, the project was able to be assembled and disassembled within the timeframe of the temporary outdoor exhibit, produce <1% of waste mortar material in fabrication, and utilize 60% less material to construct than cast-in-place construction. Using the sand mold to contain geometric edge conditions, the Print-Cast technique allows for precise aggregation tolerances. To increase the pavilions resistance to shear forces, interlocking nesting geometries are integrated into each edge condition of the panels with .785 radians of the undercut. Over extruding strategically during the printing process casts the undulating surface with accuracy. When nested together, the edge condition informs both the construction logic of the panel’s placement and orientation for the concrete panelized shell.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ecaade2024_222
id	ecaade2024_222
authors	Bindreiter, Stefan; Sisman, Yosun; Forster, Julia
year	2024
title	Visualise Energy Saving Potentials in Settlement Development: By linking transport and energy simulation models for municipal planning
doi	https://doi.org/10.52842/conf.ecaade.2024.2.079
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 79–88
summary	To achieve Sustainable Development Goals, in addition to the switch to sustainable energy sources and energy-efficient buildings, transport offers a major lever for reducing energy consumption and greenhouse gases. The increasing demand for emission-free mobility (e.g. through electromobility) but also heat pumps has a direct impact on the electricity consumption of buildings and settlements. It is still difficult to simulate the effects and interactions of different measures as sector coupling concepts require comprehensible tools for ex ante evaluation of planning measures at the community level and the linking of domain-specific models (energy, transport). Using the municipality of Bruck an der Leitha (Austria) as an example, a digital twin based on an open data model (Bednar et al., 2020) is created for the development of methods, which can be used to simulate measures to improve the settlement structure within the municipality. Forecast models for mobility (Schmaus, 2019; Ritz, 2019) and the building stock are developed or applied and linked via the open data model to be able to run through development scenarios and variants. The forecasting and visualisation options created in the project form the basis for the ex-ante evaluation of measures and policies on the way to a Positive-Energy-District. By identifying and collecting missing data, data gaps are filled for the simulation of precise models in the specific study area. A digital, interactive 3D model is created to examine the forecast results and the different scenarios.
keywords	visualisation, decision support, sector coupling, holistic spatial energy models for municipal planning, (energy) saving potentials in settlement development
series	eCAADe
email
last changed	2024/11/17 22:05

_id	acadia20_526
id	acadia20_526
authors	Bruce, Mackenzie; Clune, Gabrielle; Culligan, Ryan; Vansice, Kyle; Attraya, Rahul; McGee, Wes; Yan Ng, Tsz
year	2020
title	FORM{less}
doi	https://doi.org/10.52842/conf.acadia.2020.1.526
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. 526-535
summary	Form{less} focuses on the creation of complex thin-shell concrete forms using robotically thermoformed plastic molds. Typically, similar molds would be created using the vacuum forming process, producing direct replications of the pattern. Creating molds with this process is not only time- and material-intensive but also costly if customization is involved. Thin-shell concrete forms often require a labor-intensive process of manually finishing the open-face surface. The devised process of thermoforming two nested molds allows the concrete to be cast in between, with finished surfaces on both sides. Molds made with polyethylene terephthalate glycol (PETG) allow the formwork to be reused and recycled. The research and fabrication work include the development of heating elements and the creation of the robotic process for forming the PETG. The PETG is manipulated via a robotic arm, with a custom magnetic end effector. The integration of robotics not only enables precision for manufacturing but also allows for replicability with unrestricted threedimensional deformation. The repeatable process allows for rapid prototyping and geometric customization. Design options are then simulated computationally using SuperMatterTools, enabling further design exploration of this process without the need for extensive physical prototyping. This research aims to develop a process that allows for the creation of complex geometries while reducing the amount of material waste used for concrete casting. The novelty of the process created by dynamically forming PETG allows for quick production of formwork that is both customizable and replicable. This method of creating double-sided building components is simulated at various scales of implementation.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	cdrf2019_57
id	cdrf2019_57
authors	Caitlyn Parry and Sean Guy
year	2020
title	Recycling Construction Waste Material with the Use of AR
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_6
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper aims to present a methodology for reusing and recycling scrap timber from building sites using augmented reality and flexible digital models. The project we present describes a process that enables existing material to be reused and repurposed such that the designed model is updated by the digital inventory of digitised offcuts/waste elements.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_564
id	acadia20_564
authors	Cutajar, Sacha; Costalonga Martins, Vanessa; van der Hoven, Christo; Baszyñski, Piotr; Dahy, Hanaa
year	2020
title	Towards Modular Natural Fiber-Reinforced Polymer Architecture
doi	https://doi.org/10.52842/conf.acadia.2020.1.564
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. 564-573.
summary	Driven by the ecological crisis looming over the 21st century, the construction sector must urgently seek alternative design solutions to current building practices. In the wake of emergent digital technologies and novel material strategies, this research proposes a lightweight architectural solution using natural fiber-reinforced polymers (NFRP), which elicit interest for their inherent renewability as compared to high-performance yarns. Two associated fabrication techniques are deployed: tailored fiber placement (TFP) and coreless filament winding (CFW), both favored for their additive efficiencies granted by strategic material placement. A hypothesis is formed, postulating that their combination can leverage the standalone complexities of molds and frames by integrating them as active structural elements. Consequently, the TFP enables the creation of a 2D stiffness-controlled preform to be bent into a permanent scaffold for winding rigid 3D fiber bodies via CFW. A proof of concept is generated via the small-scale prototyping and testing of a stool, with results yielding a design of 1 kg capable of carrying 100 times its weight. Laying the groundwork for a scaled-up architectural proposal, the prototype instigates alterations to the process, most notably the favoring of a modular global design and lapped preform technique. The research concludes with a discussion on the resulting techno-implications for automation, deployment, material life cycle, and aesthetics, rekindling optimism towards future sustainable practices.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_290
id	ecaade2020_290
authors	Elesawy, Amr Alaaeldin, Signer, Mario, Seshadri, Bharath and Schlueter, Arno
year	2020
title	Aerial Photogrammetry in Remote Locations - A workflow for using 3D point cloud data in building energy modeling
doi	https://doi.org/10.52842/conf.ecaade.2020.1.723
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. 723-732
summary	Building energy modelling (BEM) results are highly affected by the surrounding environment, due to the impact of solar radiation on the site. Hence, modelling the context is a crucial step in the design process. This is challenging when access to the geometrical data of the built and natural environment is unavailable as in remote villages. The acquisition of accurate data through conventional surveying proves to be costly and time consuming, especially in areas with a steep and complex terrain. Photogrammetry using drone-captured aerial images has emerged as an innovative solution to facilitate surveying and modeling. Nevertheless, the workflow of translating the photogrammetry output from data points to surfaces readable by BEM tools proves to be tedious and unclear. This paper presents a streamlined and reproducible approach for constructing accurate building models from photogrammetric data points to use for architectural design and energy analysis in early design stage projects.
keywords	Building Energy Modeling; Photogrammetry; 3D Point Clouds; Low-energy architecture; Multidisciplinary design; Education
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2020_361
id	caadria2020_361
authors	Geht, Alexander, Weizmann, Michael, Grobman, Yasha Jacob and Tarazi, Ezri
year	2020
title	Horizontal Forming in Additive Manufacturing: Design and Architecture Perspective
doi	https://doi.org/10.52842/conf.caadria.2020.1.203
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. 203-212
summary	Extrusion based three-dimensional additive manufacturing technology forms objects by driving the material through a nozzle depositing a linear structure through vector-building blocks called roads. In a common 3-axis system, the roads are stacked layer upon layer for forming the final object. However, forming overhanging geometry in this way requires additional support structures increasing material usage and effective printing time. The paper presents a novel Horizontal forming (HF) approach and method for forming overhanging geometry, HF is a new extrusion-based AM approach that allows rapid and stable forming of horizontal structures without additional support in 3-axis systems. This approach can provide new design and manufacturing possibilities for extrusion AM, with emphasis on medium and large-scale AM. HF can affect the outcome's aesthetic and mechanical properties. Moreover, it can significantly accelerate the production process and reduce material waste. The present paper maps the influence of various parameters employed in the HF method, providing a deeper understanding of the printing process. Additionally, it explores and demonstrates the potential functional and aesthetic characteristics that can be achieved with HF for industrial design and architectural products.
keywords	Additive manufacturing; Support; Horizontal forming (HF); Extrusion-based system; Fused granulate forming (FGF)
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	acadia20_300
id	acadia20_300
authors	H Arnardottir, Thora; Dade-Robertson, Martyn; Mitrani, Helen; Zhang, Meng; Christgen, Beate
year	2020
title	Turbulent Casting
doi	https://doi.org/10.52842/conf.acadia.2020.1.300
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. 300-309.
summary	There has been a growing interest in living materials and fabrication processes including the use of bacteria, algae, fungi, and yeast to offer sustainable alternatives to industrial materials synthesis. Microbially induced calcium carbonate precipitation (MICP) is a biomineralization process that has been widely researched to solve engineering problems such as concrete cracking and to strengthen soils. MICP can also be used as an alternative to cement in the fabrication of building materials and, because of the unique process of living fabrication, if we see bacteria as our design collaborators, new types of fabrication and processes may be possible. The process of biomineralization is inherently different from traditional fabrication processes that use casting or molding. Its properties are influenced by the active bacterial processes that are connected to the casting environment. Understanding and working with interrelated factors enables a novel casting approach and the exploration of a range of form types and materials of variable consistencies and structure. We report on an experiment with partial control of mineralization through the design of different experimental vessels to direct and influence the cementation process of sand. In order to capture the form of the calcification in these experiments, we have analyzed the results using three-dimensional imaging and a technique that excavates the most friable material from the cast in stages. The resulting scans are used to reconstruct the cementation timeline. This reveals a hidden fabrication/growth process. These experiments offer a different perspective on form finding in material fabrication.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_444
id	caadria2020_444
authors	Higgs, Baptiste and Doherty, Ben
year	2020
title	Sanitary Sanity: Evaluating Privacy Preserving Machine Learning Methods for Post-occupancy Evaluation
doi	https://doi.org/10.52842/conf.caadria.2020.2.697
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. 697-706
summary	Traditional post-occupancy evaluation (POE) of building performance has typically privileged physical building attributes over human behavioural data. This is due to a lack of capability and is especially the case for private spaces such as Sanitary Facilities (SFs). A privacy-preserving sensor-based system using Machine Learning (ML) was previously developed, however it was limited to basic body position classification. Yet, SF usage behaviour can be significantly more complex. This research accordingly builds on the aforementioned work to expand behavioural classifications using a sensor-based ML system. Specifically, the case study uses a GridEYE thermal sensor array, which is trained on a cubicle location within a workplace SF. A variety of ML algorithms are then evaluated on their behaviour-classifying ability. A detailed analysis of behaviour-classification performance is then provided. A system with greater fidelity is thus demonstrated, albeit hampered by imprecise behaviour definitions. Regardless, this contributes to the capability of the broader field of research that is investigating Evidence Based Design (EBD) by extending the ability to examine human behaviour, especially in private spaces. This further contributes to the growing body of work surrounding SF provision.
keywords	EBD; Data; Internet of Things; Machine Learning; Post Occupancy Evaluation
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	cdrf2019_217
id	cdrf2019_217
authors	Jinghua Song and Sirui Sun
year	2020
title	Research on Architectural Form Optimization Method Based on Environmental Performance-Driven Design
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_21
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In the context of contemporary environment and society, the architectural form optimization based on Environmental performance-driven design is a method by using environmental performance data to optimize the architectural form. Its value lies in dealing with the interaction between architecture and environment, and developing architecture with environmental sustainability. This thesis summarizes the similarities and differences between performance-driven form design and traditional bionic form design. The traditional bionic design separates the bionic object from its complex living environment, and its simple imitation tends to fall into the local rather than the global optimum. However, performancedriven design is different from bionic design. It advocates environmental factors as a driving factor rather than a confrontational factor. It is a systematic global optimal method for studying architectural form. This paper puts forward the specific architectural form optimization simulation process based on the performance-driven thought. Taking the multilayer parking building design of the riparian zone on the south bank of Chongqing as an example, the parametric design method is used to obtain architectural optimization form adapted to the environment.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_154p
id	acadia20_154p
authors	Josephson, Alex; Friedman, Jonathan; Salance, Benjamin; Vasyliv, Ivan; Melnichuk, Tim
year	2020
title	Gusto: Rationalizing Computational Masonry Design
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 154-159
summary	Gusto 501 is a multi-level Infill Building on the footprint of an old car garage. Surrounded by an overpass and former factories, the restaurant and event spaces take the form of a ‘Hyper garage’ as a nod to its urban context. The interior is punctuated with standard terracotta blocks formed to create an intricate play of shadows during the day and embedded with LEDs to provide atmospheric illumination at night. The client's vision, our narrative, and the program demanded an innovative use of the primal material: terracotta. The scale of the project required the use of 3,700 blocks. Within the array wrapped around a 50ft tall interior volume, each block needed to be formed and sequenced uniquely to maintain structural integrity and interface with building systems, and express the sculptural qualities our team had designed. Standard approaches to the masonry could not achieve the effects our team was striving for - we had to develop our ground-up process to manufacture and install mass-customized masonry. The design process involved an algorithmic approach to a series of cuts and geometric manipulations to the blocks that allowed for near-endless combinations/configurations to create a dynamic interior facade system. Partisans, partnering with a terracotta block manufacturer, a local mason, and a masonry engineer, pursued simplifying production using wire cutter systems. Digital and physical mock-ups were then used to create a robust library of parameterized design criteria that optimized corbelling, grout thickness, weight, and fabrication complexity. Working sets of drawings were automated through a fully integrated BIM model, simplifying and speeding up installation. The challenge of marrying these processes with the physical realities of installation required another level of collaboration that included the masons themselves and the electricians who would eventually combine lighting systems into the sculpted block array.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	caadria2020_375
id	caadria2020_375
authors	Kalo, Ammar, Tracy, Kenneth and Tam, Mark
year	2020
title	Robotic Sand Carving - Machining Techniques Derived from a Traditional Balinese Craft
doi	https://doi.org/10.52842/conf.caadria.2020.2.443
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. 443-452
summary	This paper presents research aimed at translating Ukiran Pasir Melela, traditional Balinese sand carving, into a new robotic-enabled framework for rapidly carving stiff but uncured cement sand blocks to create free-form and architecturally scalable unique volumetric elements. The research aims to reconsider vernacular materials and craft through their integration robotic manufacturing processes and how this activity can provide localized, low energy manufacturing solutions for building in the Anthropocene.Balinese sand carving shows potential advantages over current, and rather environmentally damaging, machining process primarily using soft materials state to make deep, smooth cuts into material with little torque. Transferring this manual and low-impact craft to robotic-enabled fabrication leverages heuristic knowledge developed over decades and opens possibilities for expanding and transforming these capabilities to increase the variability of potential future applications.
keywords	Robotic Fabrication; Computational Design; Traditional Craft
series	CAADRIA
email
last changed	2022/06/07 07:52

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