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	acadia17_154
id	acadia17_154
authors	Brown, Nathan; Mueller, Caitlin
year	2017
title	Designing With Data: Moving Beyond The Design Space Catalog
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 154-163
doi	https://doi.org/10.52842/conf.acadia.2017.154
summary	Design space catalogs, which present a collection of different options for selection by human designers, have become commonplace in architecture. Increasingly, these catalogs are rapidly generated using parametric models and informed by simulations that describe energy usage, structural efficiency, daylight availability, views, acoustic properties, and other aspects of building performance. However, by conceiving of computational methods as a means for fostering interactive, collaborative, guided, expert-dependent design processes, many opportunities remain to improve upon the originally static archetype of the design space catalog. This paper presents developments in the areas of interaction, automation, simplification, and visualization that seek to improve on the current catalog model while also describing a vision for effective computer-aided, performance-based design processes in the future.
keywords	design methods; information processing; simulation & optimization; data visualization
series	ACADIA
email
last changed	2022/06/07 07:54

_id	acadia17_138
id	acadia17_138
authors	Berry, Jaclyn; Park, Kat
year	2017
title	A Passive System for Quantifying Indoor Space Utilization
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 138-145
doi	https://doi.org/10.52842/conf.acadia.2017.138
summary	This paper presents the development of a prototype for a new sensing device for anonymously evaluating space utilization, which includes usage factors such as occupancy levels, congregation and circulation patterns. This work builds on existing methods and technology for measuring building performance, human comfort and occupant experience in post-occupancy evaluations as well as pre-design strategic planning. The ability to collect data related to utilization and occupant experience has increased significantly due to the greater accessibility of sensor systems in recent years. As a result, designers are exploring new methods to empirically verify spatial properties that have traditionally been considered more qualitative in nature. With this premise, this study challenges current strategies that rely heavily on manual data collection and survey reports. The proposed sensing device is designed to supplement the traditional manual method with a new layer of automated, unbiased data that is capable of capturing environmental and social qualities of a given space. In a controlled experiment, the authors found that the data collected from the sensing device can be extrapolated to show how layout, spatial interventions or other design factors affect circulation, congregation, productivity, and occupancy in an office setting. In the future, this sensing device could provide designers with real-time feedback about how their designs influence occupants’ experiences, and thus allow the designers to base what are currently intuition-based decisions on reliable data and evidence.
keywords	design methods; information processing; smart buildings; IoT
series	ACADIA
email
last changed	2022/06/07 07:52

_id	acadia17_238
id	acadia17_238
authors	El-Zanfaly, Dina
year	2017
title	A Multisensory Computational Model for Human-Machine Making and Learning
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 238-247
doi	https://doi.org/10.52842/conf.acadia.2017.238
summary	Despite the advancement of digital design and fabrication technologies, design practices still follow Alberti’s hylomorphic model of separating the design phase from the construction phase. This separation hinders creativity and flexibility in reacting to surprises that may arise during the construction phase. These surprises often come as a result of a mismatch between the sophistication allowed by the digital technologies and the designer’s experience using them. These technologies and expertise depend on one human sense, vision, ignoring other senses that could be shaped and used in design and learning. Moreover, pedagogical approaches in the design studio have not yet fully integrated digital technologies as design companions; rather, they have been used primarily as tools for representation and materialization. This research introduces a multisensory computational model for human-machine making and learning. The model is based on a recursive process of embodied, situated, multisensory interaction between the learner, the machines and the thing-in-the-making. This approach depends heavily on computational making, abstracting, and describing the making process. To demonstrate its effectiveness, I present a case study from a course I taught at MIT in which students built full-scale, lightweight structures with embedded electronics. This model creates a loop between design and construction that develops students’ sensory experience and spatial reasoning skills while at the same time enabling them to use digital technologies as design companions. The paper shows that making can be used to teach design while enabling the students to make judgments on their own and to improvise.
keywords	education, society & culture; fabrication
series	ACADIA
email
last changed	2022/06/07 07:55

_id	cf2017_137
id	cf2017_137
authors	Ensari, Elif; Kobas, Bilge; Sucuo?lu, Can
year	2017
title	Computational Decision Support for an Airport Complex Roof Design: A Case Study of Evolutionary Optimization for Daylight Provision and Overheating Prevention
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 137-149.
summary	This study focuses on generating geometric design alternatives for an airport roof structure with an evolutionary design method based on optimizing solar heat gain and daylight levels. The method incorporates a parametric 3D model of the building, a multi objective genetic algorithm that was linked with the model to iteratively test for various geometric solutions, a custom module that was developed to simulate solar conditions, and external energy simulation environments that was used to validate the outcomes. The integral outcome was achieved through an iterative workflow of many software tools, and the study is significant in dealing with several space typologies at the same time, taking real-life constraints such as applicability, ease of operation, construction loads into consideration, and satisfying design and aesthetic requirements of the architectural design team.
keywords	Evolutionary algorithms, daylight and energy performance, multi-objective optimization
series	CAAD Futures
email
last changed	2017/12/01 14:37

_id	acadia17_266
id	acadia17_266
authors	Gonzalez Rojas,Paloma
year	2017
title	Space and Motion: Data-Driven Model of 4D Pedestrian Behavior
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 266-273
doi	https://doi.org/10.52842/conf.acadia.2017.266
summary	The understanding of space relies on motion, as we experience space by crossing it in time, space’s fourth dimension. However, architects lack the necessary tools to incorporate people's motion into their design of space. As a consequence, architects fail to connect space with the motion of the people that inhabit their buildings, creating disorienting environments. Further, what if augmentation technology changes how we inhabit space and the static built environment does not fit people anymore? This paper explores the problem of developing a model from people's motion, to inform and augment the architecture design process in the early stages. As an outcome, I have designed a model based on data from human-space interaction obtained through field work. First, relevant behavior was identified and recorded. Second, a metric was extracted from the data and composed by speed, the 4th D dimension as time, and gestures. Third, the original behavior was rebuilt, producing a set of rules. The rules were combined to form the model of human-space interaction. This generalizable model provides a novel approach to designing space based on data from people. Moreover, this paper presents a means of incorporating inhabitants' behavior into digital design. Finally, the model contributes to the advancement of people's motion research for general applications, such as in transport engineering, robotics, and cognitive sciences.
keywords	design methods; information processing; simulation & optimization; data visualization
series	ACADIA
email
last changed	2022/06/07 07:51

_id	cf2017_297
id	cf2017_297
authors	He, Yi; Schnabel, Marc Aurel; Chen, Rong; Wang, Ning
year	2017
title	A Comprehensive Application of BIM Modelling for Semi-underground Public Architecture: A Study for Tiantian Square Complex, Wuhan, China
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 297-308.
summary	The paper presents research on how Building Information Modelling (BIM) can be applied comprehensively throughout the design of an architectural project. A practical method based on BIM models that help to deal with multidisciplinary issues by integrating the design information from different sources, collaborators and project stages is formulated by adopting existing available tools. The ‘Tiantian Square’ building project in Wuhan, China combines a subway station with a commercial hug. According to the project’s size and complexity, our study focuses on the multiple cooperation of professionals from different backgrounds, including the departments of architectural design, structure (civil engineering), HVAC (Heating, Ventilation and Air Conditioning), water supply and drainage, and electrics and sustainable design. Our paper presents how the BIM model bridges between various simulation platforms through our technical system and management, including steps of transformation, simplification, analysis, reaction and improvement. Our research has helped to improve the overall efficiency and quality of the project. We generated a successful analysis-design approach for the initial design stages, which does not require in-depth analysis. It is a practical method to immediately evaluate the performance for each design alternative and provide guidelines for design modification. Finally, we discuss how the coordination of different department becomes a crucial factor as we look forward to a more open, communicative and inter-relational design and development process.
keywords	BIM, Subway Complex, Simulation, Semi-Underground Architecture
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	acadia17_284
id	acadia17_284
authors	Hu, Zhengrong; Park, Ju Hong
year	2017
title	HalO [Indoor Positioning Mobile Platform]: A Data-Driven, Indoor-Positioning System With Bluetooth Low Energy Technology To Datafy Indoor Circulation And Classify Social Gathering Patterns For Assisting Post Occupancy Evaluation
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 284-291
doi	https://doi.org/10.52842/conf.acadia.2017.284
summary	Post-Occupancy Evaluation (POE) as an integrated field between architecture and sociology has created practical guidelines for evaluating indoor human behavior within a built environment. This research builds on recent attempts to integrate datafication and machine learning into POE practices that may one day assist Building Information Modeling (BIM) and multi-agent modeling. This research is based on two premises: 1) that the proliferation of Bluetooth Low Energy (BLE) technology allows us to collect a building user’s data cost-effectively and 2) that the growing application of machine learning algorithms allows us to process, analyze and synthesize data efficiently. This study illustrates that the mobile platform HalO can serve as a generic tool for datafication and automation of data analysis of the movement of a building user. In this research, the iOS mobile application HalO, combined with BLE beacons enable building providers (architects, developers, engineers and facility managers etc.) to collect the user’s indoor location data. Triangulation was used to pinpoint the user’s indoor positions, and k-means clustering was applied to classify users into different gathering groups. Through four research procedures—Design Intention Analysis, Data Collection, Data Storage and Data Analysis—the visualized and classified data helps building providers to better evaluate building performance, optimize building operations and improve the accuracy of simulations.
keywords	design methods; information processing; data mining; IoT; AI; machine learning
series	ACADIA
email
last changed	2022/06/07 07:49

_id	caadria2024_87
id	caadria2024_87
authors	Li, Jiongye and Stouffs, Rudi
year	2024
title	Distribution of Carbon Storage and Potential Strategies to Enhance Carbon Sequestration Capacity in Singapore: A Study Based on Machine Learning Simulation and Geospatial Analysis
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 89–98
doi	https://doi.org/10.52842/conf.caadria.2024.2.089
summary	The expansion of urbanization leads to significant changes in land use, consequently affecting carbon storage. This research aims to investigate the carbon loss due to land use alterations and proposes strategies for mitigation. Utilizing existing land use data from 2017 and 2022, along with simulated data for 2025 generated by an ANN model and Cellular Automata, we identified changes in land use. These changes were then correlated with variations in carbon storage, both gains and losses. Our findings reveal a significant loss of 36,859 metric tons of carbon storage from 2017 to 2022. The projection for 2025 estimates a further reduction, reaching a total loss of 83,409 metric tons. By employing the LISA method, we identified that low-carbon storage zones are concentrated in the southeast region of the research site. By overlaying these zones with areas of carbon storage loss, we pinpointed regions severely affected by carbon depletion. Consequently, we propose that mitigation strategies should be imperatively implemented in these identified areas to counteract the trend of carbon storage loss. This approach offers urban planners a solution to identify areas experiencing carbon storage decline. Moreover, our research methodology provides a novel framework for scholars studying similar carbon issues.
keywords	land use and land cover (LULC) changes, simulated LULC, machine learning model, carbon storage changes, GIS
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2018_210
id	caadria2018_210
authors	Lin, Yuqiong, Zheng, Jingyun, Yao, Jiawei and Yuan, Philip F.
year	2018
title	Research on Physical Wind Tunnel and Dynamic Model Based Building Morphology Generation Method
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. 165-174
doi	https://doi.org/10.52842/conf.caadria.2018.2.165
summary	The change of the building morphology directly affects the surrounding environment, while the evaluation of these environment data becomes the main basis for the genetic iterations of the building morphology. Indeed, due to the complexity of the outdoor natural ventilation, multiple factors in the site could be the main reasons for the change of air flow. Thus, the architect is suggested to take the wind environment as the main morphology generation factor in the early stage of the building design. Based on the research results of 2017 DigitalFUTURE Wind Tunnel Visualization Workshop, a novel self-form-finding method in design infancy has been proposed. This method uses Arduino to carry out the dynamic design of the building model, which can not only connect the sensor to monitor the wind environment data, but also contribute the building model to correlate with the wind environment data in real time. The integration of the Arduino platform and the physical wind tunnel can create the possibility of continuous and real-time physical changes, data collection and wind environment simulation, using quantitative environmental factors to control building morphology, and finally achieve the harmony among the building, environment and human.
keywords	Physical wind tunnel; dynamic model; building morphology generation; environmental performance design; wind environment visualization
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ijac201715105
id	ijac201715105
authors	Nahmad Vazque, Alicia and Wassim Jabi
year	2017
title	Investigations in robotic-assisted design: Strategies for symbiotic agencies in material-directed generative design processes
source	International Journal of Architectural Computing vol. 15 - no. 1, 70-86
summary	The research described in this article utilises a phase-changing material, three-dimensional scanning technologies and a six-axis industrial robotic arms as vehicles to enable a novel framework where robotic technology is utilised as an ‘amplifier’ of the design process to realise geometries that derive from both constructive visions and architectural visions through iterative feedback loops between them. The robot in this scenario is not a fabrication tool but the enabler of an environment where the material, robotic and human agencies interact. This article describes the exploratory research for the development of a dialogic design process, sets the framework for its implementation, carries out an evaluation based on designer use and concludes with a set of observations. One of the main findings of this article is that a deeper collaboration that acknowledges the potential of these tools, in a learning-by-design method, can lead to new choreographies for architectural design and fabrication.
keywords	Robotic fabrication, human-machine networks, digital design, agency
series	other
type	normal paper
email
last changed	2019/08/02 08:28

_id	acadia17_474
id	acadia17_474
authors	Peng, Wenzhe; Zhang, Fan; Nagakura, Takehiko
year	2017
title	Machines’ Perception of Space: Employing 3D Isovist Methods and a Convolutional Neural Network in Architectural Space Classification
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 474- 481
doi	https://doi.org/10.52842/conf.acadia.2017.474
summary	Simple and common architectural elements can be combined to create complex spaces. Different spatial compositions of elements define different spatial boundaries, and each produces a unique local spatial experience to observers inside the space. Therefore an architectural style brings about a distinct spatial experience. While multiple representation methods are practiced in the field of architecture, there lacks a compelling way to capture and identify spatial experiences. Describing an observer’s spatial experiences quantitatively and efficiently is a challenge. In this paper, we propose a method that employs 3D isovist methods and a convolutional neural network (CNN) to achieve recognition of local spatial compositions. The case studies conducted validate that this methodology works well in capturing and identifying local spatial conditions, illustrates the pattern and frequency of their appearance in designs, and indicates peculiar spatial experiences embedded in an architectural style. The case study used small designs by Mies van der Rohe and Aldo van Eyck. The contribution of this paper is threefold. First, it introduces a sampling method based on 3D Isovist that generates a 2D image that can be used to represent a 3D space from a specific observation point. Second, it employs a CNN model to extract features from the sampled images, then classifies their corresponding space. Third, it demonstrates a few case studies where this space classification method is applied to different architectural styles.
keywords	design methods; information processing; AI; machine learning; computer vision; representation
series	ACADIA
email
last changed	2022/06/07 08:00

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
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. 327–336
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	cf2017_415
id	cf2017_415
authors	Tschetwertak, Julia; Schneider, Sven; Hollberg, Alexander; Donath, Dirk; Ruth, Jürgen
year	2017
title	A Matter of Sequence: Investigating the Impact of the Order of Design Decisions in Multi-Stage Design Processes
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, p. 415.
summary	The design as a process is not a new topic in architecture, yet some theories are widely unexplored, such as the multi-stage decision-making (MD) process. This design method provides multiple solutions for one design problem and is characterized by design stages. By adding new building components in every stage, multiple solutions are created for each design solution from the previous stage. If the MD process is to be applied in architectural practice, fundamental and theoretical knowledge about it becomes necessary. This paper investigates the impact of sequence of design stages on the design solutions in the MD process. A basic case study provides the necessary data for comparing different sequences and gaining fundamental knowledge of the MD process. The study contains a parametric model for building generation, a parametric Life Cycle Assessment tool and an optimization mechanism based on Evolutionary Algorithms.
keywords	Multi-stage decision-making process, Design process, Life Cycle Performance, Design Automation
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	acadia17_28
id	acadia17_28
authors	Aguiar, Rita; Cardoso, Carmo; Leit?o,António
year	2017
title	Algorithmic Design and Analysis Fusing Disciplines
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 28-37
doi	https://doi.org/10.52842/conf.acadia.2017.028
summary	In the past, there has been a rapid evolution in computational tools to represent and analyze architectural designs. Analysis tools can be used in all stages of the design process, but they are often only used in the final stages, where it might be too late to impact the design. This is due to the considerable time and effort typically needed to produce the analytical models required by the analysis tools. A possible solution would be to convert the digital architectural models into analytical ones, but unfortunately, this often results in errors and frequently the analytical models need to be built almost from scratch. These issues discourage architects from doing a performance-oriented exploration of their designs in the early stages of a project. To overcome these issues, we propose Algorithmic Design and Analysis, a method for analysis that is based on adapting and extending an algorithmic-based design representation so that the modeling operations can generate the elements of the analytical model containing solely the information required by the analysis tool. Using this method, the same algorithm that produces the digital architectural model can also automatically generate analytical models for different types of analysis. Using the proposed method, there is no information loss and architects do not need additional work to perform the analysis. This encourages architects to explore several design alternatives while taking into account the design’s performance. Moreover, when architects know the set of design variations they wish to analyze beforehand, they can easily automate the analysis process.
keywords	design methods; information processing; simulation & optimization; BIM; generative system
series	ACADIA
email
last changed	2022/06/07 07:54

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia17_164
id	acadia17_164
authors	Brugnaro, Giulio; Hanna, Sean
year	2017
title	Adaptive Robotic Training Methods for Subtractive Manufacturing
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 164-169
doi	https://doi.org/10.52842/conf.acadia.2017.164
summary	This paper presents the initial developments of a method to train an adaptive robotic system for subtractive manufacturing with timber, based on sensor feedback, machine-learning procedures and material explorations. The methods were evaluated in a series of tests where the trained networks were successfully used to predict fabrication parameters for simple cutting operations with chisels and gouges. The results suggest potential benefits for non-standard fabrication methods and a more effective use of material affordances.
keywords	design methods; information processing; construction; robotics; ai & machine learning; digital craft; manual craft
series	ACADIA
email
last changed	2022/06/07 07:52

_id	ijac201715106
id	ijac201715106
authors	Cardoso Llach, Daniel; Ardavan Bidgoli and Shokofeh Darbari
year	2017
title	Assisted automation: Three learning experiences in architectural robotics
source	International Journal of Architectural Computing vol. 15 - no. 1, 87-102
summary	Fueled by long-standing dreams of both material efficiency and aesthetic liberation, robots have become part of mainstream architectural discourses, raising the question: How may we nurture an ethos of visual, tactile, and spatial exploration in technologies that epitomize the legacies of industrial automation—for example, the pursuit of managerial efficiency, control, and an ever-finer subdivision of labor? Reviewing and extending a growing body of research on architectural robotics pedagogy, and bridging a constructionist tradition of design education with recent studies of science and technology, this article offers both a conceptual framework and concrete strategies to incorporate robots into architectural design education in ways that foster a spirit of exploration and discovery, which is key to learning creative design. Through reflective accounts of three learning experiences, we introduce the notions “assisted automation” and “robotic embodiment” as devices to enrich current approaches to robot–human design, highlighting situated and embodied aspects of designing with robotic machines.
keywords	Design education, architectural robotics, computational design, robot–human collaboration, studies of science and technology
series	other
type	normal paper
email
last changed	2019/08/02 08:28

_id	ecaade2018_165
id	ecaade2018_165
authors	Fisher-Gewirtzman, Dafna and Bruchim, Elad
year	2018
title	Considering Variant Movement Velocities on the 3D Dynamic Visibility Analysis (DVA) - Simulating the perception of urban users: pedestrians, cyclists and car drivers.
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. 569-576
doi	https://doi.org/10.52842/conf.ecaade.2018.2.569
summary	The objective of this research project is to simulate and evaluate the effect of movement velocity and cognitive abilities on the visual perception of three groups of urban users: pedestrians, cyclists and car drivers.The simulation and analysis is based on the 3D Dynamic Visual Analysis (DVA) (Fisher-Gewirtzman, 2017). This visibility analysis model was developed in the Rhinoceros and Grasshopper software environments and is based on the conceptual model presented in Fisher-Gewirtzman (2016): a 3D Line of Sight (LOS) visibility analysis, taking into account the integrated effect of the 3D geometry of the environment and the variant elements of the view (such as the sky, trees and vegetation, buildings and building types, roads, water etc.). In this paper, the current advancement of the existing model considers the visual perception of human users employing three types of movement in the urban environment--pedestrians, cyclists and drivers--is explored.We expect this research project to exemplify the contribution of such a quantification and evaluation model to evaluating existing urban structures, and for supporting future human perception-based urban design processes.
keywords	visibility analysis and simulation; predicting perception of space; movement in the urban environment; pedestrians; cyclists; car drivers
series	eCAADe
email
last changed	2022/06/07 07:51

_id	sigradi2017_064
id	sigradi2017_064
authors	Fonseca Motta, Silvio Romero; Ana Clara Moura Mourão, Ana Clara Moura Mourão, Suellen Roquete Ribeiro, Julia Marion Florencio Kato
year	2017
title	Simulation of Scenarios and Urban Analysis Using Parametric Modeling and Genetic Algorithm Based on Multicriteria Analysis
source	SIGraDi 2017 [Proceedings of the 21th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-227-439-5] Chile, Concepción 22 - 24 November 2017, pp.434-440
summary	The present paper surveys a method of changing the adequacy level of variables in multicriteria analysis (MCA) using parametric modeling. The aim is to simulate if-then scenarios to support resilience designs. The case study is a MCA for Pampulha region, Belo Horizonte, Brazil. The parametric model was developed in Grasshopper software, and defines, by knowledge-driven, a set of weight for an increased environmental quality which generates an index of suitability for each territorial unit. The if-then simulation changes the level of adequacy of 3 variables using a genetic algorithm, which calculates new distribution patterns for the MCA adequacy level.
keywords	Multicriteria analysis; Parametric modeling; Genetic algorithm; Urban analysis; Scenario simulation.
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	ecaade2017_jgo
id	ecaade2017_jgo
authors	Gero, John S.
year	2017
title	Cognitive Design Computing
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 37-40
doi	https://doi.org/10.52842/conf.ecaade.2017.1.037
summary	This talk describes the foundational concepts of cognitive design computing and then presents some examples. Cognitive computing is concerned with modeling human cognition computationally and using that model as the foundation for constructing computer models of design activities. Human cognition is based on perception, learning and adaptation. Here we present human cognition in terms of situated cognition - cognition involving interaction with an environment. The talk briefly introduces a set of principles for cognitive design computing founded on the three concepts of interaction, constructive memory and situatedness. It then presents two examples of applications of this approach.
series	eCAADe
email
last changed	2022/06/07 07:51

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