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	ecaadesigradi2019_034
id	ecaadesigradi2019_034
authors	Chen, Dechen, Luo, Dan, Xu, Weiguo, Luo, Chen, Shen, Liren, Yan, Xia and Wang, Tianjun
year	2019
title	Re-perceive 3D printing with Artificial Intelligence
doi	https://doi.org/10.52842/conf.ecaade.2019.1.443
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 443-450
summary	How can machine learning be combined with intelligent construction, material testing and other related topics to develop a new method of fabrication? This paper presents a set of experiments on the dynamic control of the heat deflection of thermoplastics in searching for a new 3D printing method with the dynamic behaviour of PLA and with a comprehensive workflow utilizing mechanic automation, computer vision, and artificial intelligence. Additionally, this paper will discuss in-depth the performance of different types of neural networks used in the research and conclude with solid data on the potential connection between the structure of neural networks and the dynamic, complex material performance we are attempting to capture.
keywords	3D printing; AI; automation; material; fabrication
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	acadia23_v1_196
id	acadia23_v1_196
authors	Bao, Ding Wen; Yan, Xin; Min Xie, Yi
year	2023
title	Intelligent Form
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 196-201.
summary	InterLoop employs previously developed workflows that enable multi-planar robotic bending of metal tubes with high accuracy and repeatability (Huang and Spaw 2022). The scale and complexity is managed by employing augmented reality (AR) technology in two capacities, fabrication and assembly (Jahn et al. 2018; Jahn, Newnham, and Berg 2022). The AR display overlays part numbers, bending sequences, expected geometry, and robot movements in real time as the robot fabrication is occurring. For assembly purposes, part numbers, centerlines, and their expected positional relationships are projected via quick response (QR) codes spatially tracked by the Microsoft Hololens 2 (Microsoft 2019). This is crucial due to the length and self-similarity of complex multi-planar parts that make them difficult to distinguish and orient correctly. Leveraging augmented reality technology and robotic fabrication uncovers a novel material expression in tubular structures with bundles, knots, and interweaving (Figure 1).
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	cf2019_008
id	cf2019_008
authors	Han, Zhen; Ning Cao, Gang Liu and Wei Yan
year	2019
title	MOPSO for BIM: A Multi-Objective Optimization Tool Using Particle Swarm Optimization Algorithm on a BIMbased Visual Programming Platform
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 39-51
summary	With the increasing applications of computational methods in the field of design optimization, intelligent metaheuristic algorithms are playing a more important role in building performance optimization. To enable the integration of optimization algorithms with Building Information Modeling (BIM), this research implemented the Particle Swarm Optimization (PSO) algorithm on Revit + Dynamo, which is a parametric BIM platform. A MultiObjective PSO (MOPSO) Solver has been developed in Dynamo using MATLAB and C# programming languages. The methodology of the research and the validation studies are presented in the paper. The validation studies prove the effectiveness of the MOPSO Solver for both standard optimization test functions and an optimization example of a simplified building design.
keywords	Particle Swarm Optimization, BIM, multi-objective optimization, visual programming
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	caadria2019_326
id	caadria2019_326
authors	Lai, Po Yan, Kim, Meereh, Choi, Minkyu, Lee, Chae-Seok, Porcellini, Valentin, Yi, Taeha and Lee, Ji-Hyun
year	2019
title	Framework of Judgment System for Smart Home Assistant Utilizing Collective Intelligence Case-Based Reasoning
doi	https://doi.org/10.52842/conf.caadria.2019.1.695
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 695-704
summary	This paper proposes a framework of judgment system for smart home assistant that utilizes Collective Intelligence Case Based Reasoning (CI-CBR). CBR is suitable for the smart home environment with its system adaptability to the changeful user scenarios. However, existing CBR solutions have shown relatively low accuracy in service recommendation. This research therefore aims at enhancing the accuracy by introducing collective intelligence into the recommendation system. Assuming that multiple agents will make better decision than single agent, we adopted a multi-agent approach to generate the most similar case, which represents the optimal recommendation from the case base. This paper describes how our system enables agents adopting different similarity measures come to an agreement about the most similar case by the means of majority voting in the judging process. Our framework of a collective judgment system demonstrates its potentials to improve recommendation accuracy, and further enhance the performance of existing smart home assistants.
keywords	Collective Intelligence; Case Based Reasoning; Smart home; Service recommendation; Multi-agent system
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2019_093
id	caadria2019_093
authors	Shahsavari, Fatemeh, Koosha, Rasool and Yan, Wei
year	2019
title	Uncertainty and Sensitivity Analysis Using Building Information Modeling - (An Energy Analysis Test Case)
doi	https://doi.org/10.52842/conf.caadria.2019.1.615
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 615-624
summary	Building design decision-making is associated with uncertainties due to variations over time and unpredictable parameters. There is a growing demand for probabilistic methods, i.e., uncertainty and sensitivity analyses to handle the uncertainties in building design. This research intends to encourage the application of Building Information Modeling (BIM) for addressing design uncertainties affecting building energy performance. The mapping between BIM (Revit and Dynamo) and a customized model-based energy analysis tool in Excel is investigated to translate architectural models to energy models and conduct the probabilistic analyses. The application of this method is demonstrated with a test case of a hypothetical residential unit in College Station, Texas, USA. Input variables in this example are the thermal properties of building elements, and the two simulation outputs are annual heating and cooling energy consumption, and deviation from comfort temperature. The results indicate the probability distribution of simulation outputs and the importance factor of each design input. This method deals with uncertainties and provides a more reliable and robust basis for design decision-making.
keywords	building design decision-making ; Building Information Modeling (BIM); Parametric design; Uncertainty and sensitivity analysis; Building performance analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2019_411
id	caadria2019_411
authors	Yan, Liang, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2019
title	Intergrating UAV Development Technology with Augmented Reality toward Landscape Tele-Simulation
doi	https://doi.org/10.52842/conf.caadria.2019.1.423
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 423-432
summary	Augmented reality (AR) is an emerging landscape simulation technology being used in the construction industry to reduce losses in subsequent projects by reviewing the landscape before a building is completed. However, since AR projects virtual models into the real world through portable devices, the designer's review perspective and the number of people able to participate in the review process is limited. Therefore, a system that combines AR and unmanned aerial vehicle (UAV) development with telecommunications technology was designed and prototyped to use the UAV camera as the source of the video stream of AR. This frees the designer's review perspective through ground control and allows remote communication with off-site people, thus allowing more users site access and improving system usability. This paper details the construction of the integrated system, including the integrating of different development languages, environments, and mutual calls used, the AR and UAV development modules, the construction process of the telecommunication protocol, and mutual data interoperability.
keywords	Landscape simulation; tele-simulation; Markerless Augmented Reality (AR); Unmanned Aerial Vehicle (UAV); telecommunication
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	cf2019_062
id	cf2019_062
authors	Yousif, Shermeen ;and Wei Yan
year	2019
title	Shape Clustering Using K-Medoids in Architectural Form Finding
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 503
summary	As the number of design candidates in generative systems is often high, there is a need for an articulation mechanism that assists designers in exploring the generated design set. This research aims to condense the solution set yet enhance heterogeneity in generative design systems. Specifically, this work accomplishes the following: (1) introduces a new design articulation approach, a KMedoids Shape Clustering (KM-SC) method that is capable of grouping a dataset of shapes with similitude in one cluster and retrieving a representative for each cluster, and (2) incorporate the developed clustering method in architectural form finding. The articulated (condensed) set of shapes can be presented to designers to assist in their decision making. The research methods include formulating an algorithmic set with the implementation of K-Medoids and other algorithms. The results, visualized and discussed in the paper, show accurate clustering in comparison with the expected reference clustering sets.
keywords	Generative design systems, clustering, form finding, K-Medoids
series	CAAD Futures
email
last changed	2019/07/29 14:18

_id	acadia19_60
id	acadia19_60
authors	Yousif, Shermeen; Yan, Wei
year	2019
title	Application of an Automatic Shape Clustering Method
doi	https://doi.org/10.52842/conf.acadia.2019.060
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 60-69
summary	Despite their prevalence and extensive applications, generative and design optimization systems lack effective organizational methods of the excessive number of design options they produce, which is problematic for designers’ interaction. Ideally, a diverse and organized set of designs can mediate successful designers’ evaluation and exploration of the design space. Cluster analysis, a big-data management strategy, offers a solution. Yet, there is a need for investigating appropriate methods for applying cluster-analysis to a dataset of geometric shapes. Therefore, we have recently developed and published a new approach, the Shape Clustering using K-Medoids (SC-KM) method as an articulation mechanism in generative systems. The method involves shape description, shape difference measure calculation, and implementation of the K-Medoids clustering algorithm. The focus of this work is on incorporating the method into a generative system with parametric building shape generation and design optimization. The method organizes a dataset of shapes into clusters where shapes within the cluster share similarities yet differ from other clusters, and each cluster is signified by one representative shape. The SC-KM method contributes to an organized design presentation and facilitates designers’ examination of their designs’ geometric qualities.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:57

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