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

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_id	ecaade2024_4
id	ecaade2024_4
authors	Irodotou, Louiza; Gkatzogiannis, Stefanos; Phocas, Marios C.; Tryfonos, George; Christoforou, Eftychios G.
year	2024
title	Application of a Vertical Effective Crank–Slider Approach in Reconfigurable Buildings through Computer-Aided Algorithmic Modelling
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 1, pp. 421–430
doi	https://doi.org/10.52842/conf.ecaade.2024.1.421
summary	Elementary robotics mechanisms based on the effective crank–slider and four–bar kinematics methods have been applied in the past to develop architectural concepts of reconfigurable structures of planar rigid-bar linkages (Phocas et al., 2020; Phocas et al., 2019). The applications referred to planar structural systems interconnected in parallel to provide reconfigurable buildings with rectangular plan section. In enabling structural reconfigurability attributes within the spatial circular section buildings domain, a vertical setup of the basic crank–slider mechanism is proposed in the current paper. The kinematics mechanism is integrated on a column placed at the middle of an axisymmetric circular shaped spatial linkage structure. The definition of target case shapes of the structure is based on a series of numerical geometric analyses that consider certain architectural and construction criteria (i.e., number of structural members, length, system height, span, erectability etc.), as well as structural objectives (i.e., structural behavior improvement against predominant environmental actions) aiming to meet diverse operational requirements and lightweight construction. Computer-aided algorithmic modelling is used to analyze the system's kinematics, in order to provide a solid foundation and enable rapid adaptation for mechanisms that exhibit controlled reconfigurations. The analysis demonstrates the implementation of digital parametric design tools for the investigation of the kinematics of the system at a preliminary design stage, in avoiding thus time-demanding numerical analysis processes. The design process may further provide enhanced interdisciplinary performance-based design outcomes.
keywords	Reconfigurable Structures, Spatial Linkage Structures, Kinematics, Parametric Associative Design
series	eCAADe
email
last changed	2024/11/17 22:05

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

_id	acadia20_142p
id	acadia20_142p
authors	Kilian, Axel
year	2020
title	The Flexing Room
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. 142-147
summary	Robotics has been largely confined to the object category with fewer examples at the scale of buildings. Robotic buildings present unique challenges in communicating intent to the enclosed user. Precedent work in architectural robotics explored the performative dimension, the playful and interactive qualities, and the cognitive challenges of AI systems interacting with people in architecture. The Flexing Room robotic skeleton was installed at MIT at its full designed height for the first time and tested for two weeks in the summer of 2019. The approximately 13-foot-tall structure is comprised of 36 pneumatic actuators and an active bend fiberglass structure. The full height allowed for a wide range of postures the structure could take. Acoustic monitoring through Piezo pickup mics was added that allowed for basic rhythmic responses of the structure to people tapping or otherwise triggering the vibration sensors. Data streams were collected synchronously from Kinect skeleton tracking, piezo pickup mics, camera streams, and posture data. The emphasis in this test period was first to establish reliable hardware operations at full scale and second to record correlated data streams of the sensors installed in the structure together with the actuation triggers and the human poses of the inhabitant. The full-scale installation of hardware was successful and proved the feasibility of the structural and actuation approach previously tested on a one-level setup. The range of postures was increased and more transparent for the occupant. The perception of the structure as space was also improved as the system reached regular ceiling height and formed a clearer architectural scale enclosure. The ambition of communicating through architectural postures has not been achieved yet, but promising directions emerged from the test and data collection
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia20_84
id	acadia20_84
authors	Kirova, Nikol; Markopoulou, Areti
year	2020
title	Pedestrian Flow: Monitoring and Prediction
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. 84-93.
doi	https://doi.org/10.52842/conf.acadia.2020.1.084
summary	The worldwide lockdowns during the first wave of the COVID-19 pandemic had an immense effect on the public space. The events brought up an opportunity to redesign mobility plans, streets, and sidewalks, making cities more resilient and adaptable. This paper builds on previous research of the authors that focused on the development of a graphene-based sensing material system applied to a smart pavement and utilized to obtain pedestrian spatiotemporal data. The necessary steps for gradual integration of the material system within the urban fabric are introduced as milestones toward predictive modeling and dynamic mobility reconfiguration. Based on the capacity of the smart pavement, the current research presents how data acquired through an agent-based pedestrian simulation is used to gain insight into mobility patterns. A range of maps representing pedestrian density, flow, and distancing are generated to visualize the simulated behavioral patterns. The methodology is used to identify areas with high density and, thus, high risk of transmitting airborne diseases. The insights gained are used to identify streets where additional space for pedestrians is needed to allow safe use of the public space. It is proposed that this is done by creating a dynamic mobility plan where temporal pedestrianization takes place at certain times of the day with minimal disruption of road traffic. Although this paper focuses mainly on the agent-based pedestrian simulation, the method can be used with real-time data acquired by the sensing material system for informed decision-making following otherwise-unpredictable pedestrian behavior. Finally, the simulated data is used within a predictive modeling framework to identify further steps for each agent; this is used as a proof-of-concept through which more insights can be gained with additional exploration.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_38
id	acadia20_38
authors	Mueller, Stephen
year	2020
title	Irradiated Shade
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. 38-46.
doi	https://doi.org/10.52842/conf.acadia.2020.1.038
summary	The paper details computational mapping and modeling techniques from an ongoing design research project titled Irradiated Shade, which endeavors to develop and calibrate a computational toolset to uncover, represent, and design for the unseen dangers of ultraviolet radiation, a growing yet underexplored threat to cities, buildings, and the bodies that inhabit them. While increased shade in public spaces has been advocated as a strategy for “mitigation [of] climate change” (Kapelos and Patterson 2014), it is not a panacea to the threat. Even in apparent shade, the body is still exposed to harmful, ambient, or “scattered” UVB radiation. The study region is a binational metroplex, a territory in which significant atmospheric pollution and the effects of climate change (reduced cloud cover and more “still days” of stagnant air) amplify the “scatter” of ultraviolet wavelengths and UV exposure within shade, which exacerbates urban conditions of shade as an “index of inequality” (Bloch 2019) and threatens public health. Exposure to indirect radiation correlates to the amount of sky visible from the position of an observer (Gies and Mackay 2004). The overall size of a shade structure, as well as the design of openings along its sides, can greatly impact the UV protection factor (UPF) (Turnbull and Parisi 2005). Shade, therefore, is more complex than ubiquitous urban and architectural “sun” and “shadow studies” are capable of representing, as such analyses flatten the three-dimensional nature of radiation exposure and are “blind” to the ultraviolet spectrum. “Safe shade” is contingent on the nuances of the surrounding built environment, and designers must be empowered to observe and respond to a wider context than current representational tools allow.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

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

_id	acadia20_160
id	acadia20_160
authors	Sun, Yunjuan; Jiang, Lei; Zheng, Hao
year	2020
title	A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data
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. 160-168.
doi	https://doi.org/10.52842/conf.acadia.2020.2.160
summary	The growing popularity of machine learning has provided new opportunities to predict certain behaviors precisely by utilizing big data. In this research, we use an image-based neural network to explore the relationship between the built environment and the activity of bicyclists in that environment. The generative model can produce heat maps that can be used to predict quantitatively the cycling and running activity in a given area, and then use urban design to enhance urban vitality in that area. In the machine learning model, the input image is a plan view of the built environment, and the output image is a heat map showing certain activities in the corresponding area. After it is trained, the model yields output (the predicted heat map) at an acceptable level of accuracy. The heat map shows the levels and conditions of the subject activity in different sections of the built environment. Thus, the predicted results can help identify where regional vitality can be improved. Using this method, designers can not only predict the behavioral heat distribution but also examine the different interactions between behaviors and aspects of the environment. The extent to which factors might influence behaviors is also studied by generating a heat map of the modified plan. In addition to the potential applications of this approach, its limitations and areas for improvement are also proposed.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_148p
id	acadia20_148p
authors	Vansice, Kyle; Attraya, Rahul; Culligan, Ryan; Johnson, Benton; Sondergaard, Asbjorn; Peters, Nate
year	2020
title	Stereoform Slab
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. 148-153
summary	Stereoform Slab is both a pavilion and a prototype - an exhibition for the 2019 Chicago Architectural Biennial. It is an experiment in how digital form-finding and robotics can be leveraged to rethink the future of concrete construction. Stereoform Slab examines the role of one of the most ubiquitous horizontal elements in the city - the concrete slab, also the most common element in contemporary construction. Using smarter forming systems - in this case, a ruled-surface-derived, robotic hotwire process - the Stereoform Slab prototype proved that the amount of material used and waste generated could be minimized without increasing construction complexity, by about 20% over a conventional system. Stereoform also extends the conventional concrete span (column spacing), specifically in Chicago, from 30’ to 45’. In developing a concrete forming system that affords added flexibility without increasing construction costs, it is possible to reduce embodied carbon significantly. The method allows reducing carbon in buildings that aren’t typically the subject of advanced architectural design or rigorous optimization – conventional buildings that compose a majority of our built environment, and its respective contributions to global carbon emissions. Stereoform is the result of a multi-objective design optimization process. Optimal materialization, according to the compressive/tensile physics present in beam design, was balanced against the fabrication constraints of a singularly ruled-surface, which enables fast form-making using robotic hotwire cutting. SOM and Autodesk collaborated to mirror the approach developed to optimize Stereoform slab as a pavilion, to the building scale, using the multi-objective optimization platform Refinery. Project Refinery allowed the team to create a hyper-responsive system design that could adapt to any number of varying programmatic conditions and loading patterns. The development of this approach is a crucial step in making optimization techniques flexible enough to balance the number of competing parameters in the design process available and accessible to a broader design audience within architecture and engineering.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia20_94
id	acadia20_94
authors	Yoo, Wonjae; Kim, Hyoungsub; Shin, Minjae; J.Clayton, Mark
year	2020
title	BIM-Based Automatic Contact Tracing System Using Wi-Fi
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. 94-101.
doi	https://doi.org/10.52842/conf.acadia.2020.1.094
summary	This study presents a BIM-based automatic contact tracing method using a stations-oriented indoor localization (SOIL) system. The SOIL system integrates BIM models and existing network infrastructure (i.e., Wi-Fi), using a clustering method to generate roomlevel occupancy schedules. In this study, we improve the accuracy of the SOIL system by including more detailed Wi-Fi signal travel sources, such as reflection, refraction, and diffraction. The results of field measurements in an educational building show that the SOIL system was able to produce room-level occupant location information with a 95.6% level of accuracy. This outcome is 2.6% more accurate than what was found in a previous study. We also describe an implementation of the SOIL system for conducting contact tracing in large buildings. When an individual is confirmed to have COVID-19, public health professionals can use this system to quickly generate information regarding possible contacts. The greatest strength of this SOIL implementation is that it has wide applicability in largescale buildings, without the need for additional sensing devices. Additional tests using buildings with multiple floors are required to further explore the robustness of the system.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_208
id	acadia20_208
authors	Zheng, Hao; Wang, Xinyu; Qi, Zehua; Sun, Shixuan; Akbarzadeh, Masoud
year	2020
title	Generating and Optimizing a Funicular Arch Floor Structure
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. 208-217.
doi	https://doi.org/10.52842/conf.acadia.2020.2.208
summary	In this paper, we propose a geometry-based generative design method to generate and optimize a floor structure with funicular building members. This method challenges the antiquated column system, which has been used for more than a century. By inputting the floor plan with the positions of columns, designers can generate a variety of funicular supporting structures, expanding the choice of floor structure designs beyond simply columns and beams and encouraging the creation of architectural spaces with more diverse design elements. We further apply machine learning techniques (artificial neural networks) to evaluate and optimize the structural performance and constructability of the funicular structure, thus finding the optimal solutions within the almost infinite solution space. To achieve this, a machine learning model is trained and used as a fast evaluator to help the evolutionary algorithm find the optimal designs. This interdisciplinary method combines computer science and structural design, providing flexible design choices for generating floor structures.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_108p
id	acadia20_108p
authors	Akbarzadeh, Masoud; Ghomi, Ali Tabatabaie; Bolhassani, Mohammad; Akbari, Mostafa; Seyedahmadian, Alireza; Papalexiou, Konstantinos
year	2020
title	Saltatur
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. 108-113.
summary	The Saltatur (Dancer in Latin) demonstrates innovative research in the design and fabrication of a prefab structure consisting of spatial concrete nodes assembled in a compression-only configuration. The compression-only body is kept in equilibrium using the post-tensioning steel rods at the top and the bottom of the structure, supporting an ultra-thin glass structure on its top. A node-based assembly was considered as a method of construction. An innovative detailing was developed that allows locking each member in its exact location in the body, obviating the need for a particular assembly sequence. A bespoke steel connection transfers the tensile forces between the concrete members effectively. Achieving a high level of efficiency in utilizing concrete for spatial systems requires a robust and powerful structural design and fabrication approach that has been meticulously exhibited in this project. The structural form of the project was developed using a three-dimensional geometry-based structural design method known as 3D Graphic Statics with precise control over the magnitude of the lateral forces in the system. The entire concrete body of the structure is held in compression by the tension ties at the top and bottom of the structure with no horizontal reactions at the supports. This particular internal distribution of forces in the form of the compression-only body reduces the bending moment in the system and, therefore, the required mass to span such a distance.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia20_456
id	acadia20_456
authors	Alali, Jiries; Negar Kalantar, Dr.; Borhani, Alireza
year	2020
title	Casting on a Dump
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. 456-463.
doi	https://doi.org/10.52842/conf.acadia.2020.1.456
summary	“Casting on a dump” focuses on finding accessible, low-tech fabrication methodologies that allow for the construction of parametrically designed nonstandard modular cast panels. Such an approach adopts a computational design framework using a single low-tech and low-energy fabrication device to create nonrepetitive volumetric panels cast in situ. The design input for these panels is derived from design preferences and environmental control data. The technique expands upon easy to fabricate and cast methods, targeting less-developed logistical settings worldwide, and thus responding to imminent needs related to climate, available resources, and the economy.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_499
id	ecaade2020_499
authors	Ashour, Ziad and Yan, Wei
year	2020
title	BIM-Powered Augmented Reality for Advancing Human-Building Interaction
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. 169-178
doi	https://doi.org/10.52842/conf.ecaade.2020.1.169
summary	The shift from computer-aided design (CAD) to building information modeling (BIM) has made the adoption of augmented reality (AR) promising in the field of architecture, engineering and construction. Despite the potential of AR in this field, the industry and professionals have still not fully adopted it due to registration and tracking limitations and visual occlusions in dynamic environments. We propose our first prototype (BIMxAR), which utilizes existing buildings' semantically rich BIM models and contextually aligns geometrical and non-geometrical information with the physical buildings. The proposed prototype aims to solve registration and tracking issues in dynamic environments by utilizing tracking and motion sensors already available in many mobile phones and tablets. The experiment results indicate that the system can support BIM and physical building registration in outdoor and part of indoor environments, but cannot maintain accurate alignment indoor when relying only on a device's motion sensors. Therefore, additional computer vision and AI (deep learning) functions need to be integrated into the system to enhance AR model registration in the future.
keywords	Augmented Reality; BIM; BIM-enabled AR; GPS; Human-Building Interactions; Education
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia20_66
id	acadia20_66
authors	Aviv, Dorit; Wang, Zherui; Meggers, Forrest; Ida, Aletheia
year	2020
title	Surface Generation of Radiatively-Cooled Building Skin for Desert Climate
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. 66-73.
doi	https://doi.org/10.52842/conf.acadia.2020.1.066
summary	A radiatively cooled translucent building skin is developed for desert climates, constructed out of pockets of high heat-capacity liquids. The liquids are contained by a wavelength-selective membrane enclosure, which is transmissive in the infrared range of electromagnetic radiation but reflective in the shortwave range, and therefore prevents overheating from solar radiation and at the same time allows for passive cooling through exposure of its thermal mass to the desert sky. To assess the relationship between the form and performance of this envelope design, we develop a feedback loop between computational simulations, analytical models, and physical tests. We conduct a series of simulations and bench-scale experiments to determine the thermal behavior of the proposed skin and its cooling potential. Several materials are considered for their thermal storage capacity. Hydrogel cast into membrane enclosures is tested in real climate conditions. Slurry phase change materials (PCM) are also considered for their additional heat storage capacity. Challenges of membrane welding patterns and nonuniform expansion of the membrane due to the weight of the enclosed liquid are examined in both digital simulations and physical experiments. A workflow is proposed between the radiation analysis based on climate data, the formfinding simulations of the elastic membrane under the liquid weight, and the thermal storage capacity of the overall skin.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2024.2.079
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_198p
id	acadia20_198p
authors	Birkeland, Jennifer; Scelsa, Jonathan A.
year	2020
title	Live L’oeil – Through the Looking Ceiling
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. 198-201
summary	Following the proliferation of linear perspective during the Renaissance, the hegemony of the vantage point was often problematically used to signify the patron’s dominance. During the mannerist era, we witnessed the creation of elaborate rooms, painted in architectural linear perspective establishing the illusionary space of faraway lands - a measure of optic imperialism wherein the conquests of the west played out in the domestic decoration of the elite later provided to the public as a societal spectacle in the form of the panorama. Within these architectural illusions, or Quadratura as they were named in Italy, lies the most notable and justifiable critique of design by vantage point, the question ‘which vantage point is privileged?’ History not surprisingly reveals that the typical vantage point was most problematically centered at one and three-quarter meters above the ground – coincident with five centimeters below the average height of a human European male. The design of architectural form through view or spatial image has arguably perpetuated this act of optic bias. This project addresses this problematic practice of design by vantage point by utilizing motion sensors to liberate the virtual space of a canonic example of quadrature from its confines within a singular vantage point. The authors digitally modeled the projective space of Andrea Pozzo’s vision for the Church of Sant’Ignazio di Loyola in Rome, scaled and fit to a gallery space outfitted with a canvas to inform a ceiling plane. Anamorphic images of the virtual heavenly space, as seen through the canvas ceiling picture plane, were created from the digital model and encoded to the individual moments in the room. Individuals who moved through the gallery were followed by the illusion of the heavenly space, creating a live l’oeil distortion.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	acadia20_638
id	acadia20_638
authors	Claypool, Mollie; Jimenez Garcia, Manuel; Retsin, Gilles; Jaschke, Clara; Saey, Kevin
year	2020
title	Discrete Automation
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. 638-647.
doi	https://doi.org/10.52842/conf.acadia.2020.1.638
summary	Globally, the built environment is inequitable. And while construction automation is often heralded as the solution to labor shortages and the housing crisis, such methods tend to focus on technology, neglecting the wider socioeconomic contexts. Automated Architecture (AUAR), a spinoff of AUAR Labs at The Bartlett School of Architecture, UCL, asserts that a values-centered, decentralized approach to automation centered around local communities can begin to address this material hegemony. The paper introduces and discusses AUAR’s platform-based framework, Discrete Automation, which subverts the status quo of automation that excludes those who are already disadvantaged into an inclusive network capable of providing solutions to both the automation gap and the assembly problem. Through both the wider context of existing modular housing platforms and issues of the current use of automated technologies in architectural production, Discrete Automation is discussed through the example of Block Type A, a discrete timber building system, which in conjunction with its combinatorial app constitutes the base of a community-led housing platform developed by AUAR. Built case studies are introduced alongside a discussion of the applied methodologies and an outlook on the platform’s potential for scalability in an equitable, sustainable manner.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_688
id	acadia20_688
authors	del Campo, Matias; Carlson, Alexandra; Manninger, Sandra
year	2020
title	3D Graph Convolutional Neural Networks in Architecture Design
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. 688-696.
doi	https://doi.org/10.52842/conf.acadia.2020.1.688
summary	The nature of the architectural design process can be described along the lines of the following representational devices: the plan and the model. Plans can be considered one of the oldest methods to represent spatial and aesthetic information in an abstract, 2D space. However, to be used in the design process of 3D architectural solutions, these representations are inherently limited by the loss of rich information that occurs when compressing the three-dimensional world into a two-dimensional representation. During the first Digital Turn (Carpo 2013), the sheer amount and availability of models increased dramatically, as it became viable to create vast amounts of model variations to explore project alternatives among a much larger range of different physical and creative dimensions. 3D models show how the design object appears in real life, and can include a wider array of object information that is more easily understandable by nonexperts, as exemplified in techniques such as building information modeling and parametric modeling. Therefore, the ground condition of this paper considers that the inherent nature of architectural design and sensibility lies in the negotiation of 3D space coupled with the organization of voids and spatial components resulting in spatial sequences based on programmatic relationships, resulting in an assemblage (DeLanda 2016). These conditions constitute objects representing a material culture (the built environment) embedded in a symbolic and aesthetic culture (DeLanda 2016) that is created by the designer and captures their sensibilities.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_272
id	acadia20_272
authors	del Campo, Matias; Carlson, Alexandra; Manninger, Sandra
year	2020
title	How Machines Learn to Plan
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. 272-281.
doi	https://doi.org/10.52842/conf.acadia.2020.1.272
summary	This paper strives to interrogate the abilities of machine vision techniques based on a family of deep neural networks, called generative adversarial neural networks (GANs), to devise alternative planning solutions. The basis for these processes is a large database of existing planning solutions. For the experimental setup of this paper, these plans were divided into two separate learning classes: Modern and Baroque. The proposed algorithmic technique leverages the large amount of structural and symbolic information that is inherent to the design of planning solutions throughout history to generate novel unseen plans. In this area of inquiry, aspects of culture such as creativity, agency, and authorship are discussed, as neural networks can conceive solutions currently alien to designers. These can range from alien morphologies to advanced programmatic solutions. This paper is primarily interested in interrogating the second existing but uncharted territory.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2023_99
id	ecaade2023_99
authors	Dervishaj, Arlind, Fonsati, Arianna, Hernández Vargas, José and Gudmundsson, Kjartan
year	2023
title	Modelling Precast Concrete for a Circular Economy in the Built Environment
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 177–186
doi	https://doi.org/10.52842/conf.ecaade.2023.2.177
summary	In recent years, there has been a growing interest in adopting circular approaches in the built environment, specifically reusing existing buildings or their components in new projects. To achieve this, drawings, laser scanning, photogrammetry and other techniques are used to capture data on buildings and their materials. Although previous studies have explored scan-to-BIM workflows, automation of 2D drawings to 3D models, and machine learning for identifying building components and materials, a significant gap remains in refining this data into the right level of information required for digital twins, to share information and for digital collaboration in designing for reuse. To address this gap, this paper proposes digital guidelines for reusing precast concrete based on the level of information need (LOIN) standard EN 17412-1:2020 and examines several CAD and BIM modelling strategies. These guidelines can be used to prepare digital templates that become digital twins of existing elements, develop information requirements for use cases, and facilitate data integration and sharing for a circular built environment.
keywords	building information modelling (BIM), circular construction, reuse, concrete
series	eCAADe
email
last changed	2023/12/10 10:49

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