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	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	ijac202119313
id	ijac202119313
authors	Saldana Ochoa, Karla; Ohlbrock, Patrick Ole; D’Acunto, Pierluigi; Moosavi, Vahid
year	2021
title	Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligence
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 466–490
summary	This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection, and regeneration) that allow to create multiple design options and to navigate in the design space according to objective and subjective criteria defined by the human designer. Through the interaction between human and machine intelligence, the machine can learn the nonlinear correlation between the design inputs and the design outputs preferred by the human designer and generate new options by itself. In addition, the machine can provide insights into the structural performance of the generated structural forms. Within the proposed framework, three main algorithms are used: Combinatorial Equilibrium Modeling for generating of structural forms in static equilibrium as design options, Self-Organizing Map for clustering the generated design options, and Gradient-Boosted Trees for classifying the design options. These algorithms are combined with the ability of human designers to evaluate non-quantifiable aspects of the design. To test the proposed framework in a real-world design scenario, the design of a stadium roof is presented as a case study.
keywords	Structural design, machine learning, topology, graphic statics, form-finding, Combinatorial Equilibrium Modeling, Self-Organizing Map, Gradient-Boosted Trees
series	journal
email
last changed	2024/04/17 14:29

_id	ascaad2021_021
id	ascaad2021_021
authors	Albassel, Mohamed; Mustafa Waly
year	2021
title	Applying Machine Learning to Enhance the Implementation of Egyptian Fire and Life Safety Code in Mega Projects
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 7-22
summary	Machine Learning has become a significant research area in architecture; it can be used to retrieve valuable information for available data used to predict future instances. the purpose of this research was to develop an automated workflow to enhance the implementation of The Egyptian fire & life safety (FLS) code in mega projects and reduce the time wasted on the traditional process of rooms’ uses, occupant load, and egress capacity calculations to increase productivity by applying Supervised Machine Learning based on classification techniques through data mining and building datasets from previous projects, and explore the methods of preparation and analyzing data (text cleanup- tokenization- filtering- stemming-labeling). Then, provide an algorithm for classification rules using C# and python in integration with BIM tools such as Revit-Dynamo to calculate cumulative occupant load based on factors which are mentioned in the Egyptian FLS code, determine classification and uses of rooms to validate all data related to FLS. Moreover, calculating the egress capacity of means of egress for not only exit doors but also exit stairs. In addition, the research is to identify a clear understanding about ML and BIM through project case studies and how to build a model with the needed accuracy.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	caadria2021_376
id	caadria2021_376
authors	Dounas, Theodoros, Jabi, Wassim and Lombardi, Davide
year	2021
title	Topology Generated Non-Fungible Tokens - Blockchain as infrastructure for a circular economy in architectural design
doi	https://doi.org/10.52842/conf.caadria.2021.2.151
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 151-160
summary	The paper presents a new digital infrastructure layer for buildings and architectural assets. The infrastructure layer consists of a combination of topology graphs secured on a decentralised ledger. The topology graphs organise non-fungible digital tokens which each represent and correspond to building components, and in the root of the graph to the building itself.The paper presents background research in the relationship of building representation in the form of graphs with topology, of both manifold and non manifold nature. In parallel we present and analyse the relationship between digital representation and physical manifestation of a building, and back again. Within the digital representations the paper analyses the securing and saving of information on decentralised ledger technologies (such as blockchain). We then present a simple sample of generating and registering a non-manifold topology graph on the Ethereum blockchain as an EC721 token, i.e. a digital object that is unique, all through the use of dynamo and python scripting connected with a smart contract on the Ethereum blockchain. Ownership of this token can then be transferred on the blockchain smart contracts. The paper concludes with a discussion of the possibilities that this integration brings in terms of material passports and a circular economy and smart contracts as an infrastructure for whole-lifecycle BIM and digitally encapsulates of value in architectural designPlease write your abstract here by clicking this paragraph.
keywords	Blockchain; Tokenisation; Topology; Circular Economy; decentralisation
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_001
id	caadria2021_001
authors	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.)
year	2021
title	CAADRIA 2021: Projections, Volume 2
doi	https://doi.org/10.52842/conf.caadria.2021.2
source	PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, 764 p.
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	caadria2021_000
id	caadria2021_000
authors	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.)
year	2021
title	CAADRIA 2021: Projections, Volume 1
doi	https://doi.org/10.52842/conf.caadria.2021.1
source	PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, 768 p.
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	caadria2021_216
id	caadria2021_216
authors	Aman, Jayedi, Tabassum, Nusrat, Hopfenblatt, James, Kim, Jong Bum and Haque, MD Obidul
year	2021
title	Optimizing container housing units for informal settlements - A parametric simulation & visualization workflow for architectural resilience
doi	https://doi.org/10.52842/conf.caadria.2021.1.051
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 51-60
summary	In rapidly growing cities like Dhaka, Bangladesh, sustainable housing in urban wetlands and slums present a challenge to more affordable and livable cities. The Container Housing System (CHS) is among the latest methods of affordable, modular housing quickly gaining acceptance among local stakeholders in Bangladesh. Even though container houses made of heat-conducting materials significantly impact overall energy consumption, there is little research on the overall environmental impact of CHS. Therefore, this study aims to investigate the performance of CHS in the climatic context of the Korail slum in Dhaka. The paper proposes a building envelope optimization and visualization workflow utilizing parametric cluster simulation modeling, multi-objective optimization (MOO) algorithms, and virtual reality (VR) as an immersive visualization technique. First, local housing and courtyard patterns were used to develop hypothetical housing clusters. Next, the CHS design variables were chosen to conduct the MOO analysis to measure Useful Daylight Illuminance and Energy Use Intensity. Finally, the prototype was integrated into a parametric VR environment to enable local stakeholders to walk through the clusters with the goal of generating feedback. This study shows that the proposed method can be implemented by architects and planners in the early design process to help improve the stakeholders understanding of CHS and its impact on the environment. It further elaborates on the implementation results, challenges, limitations of the parametric framework, and future work needed.
keywords	Multi-objective Optimization; Building Energy Use; CHS; Informal Settlements; Parametric VR
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_069
id	ascaad2021_069
authors	Cheddadi, Aqil; Kensuke Hotta, Yasushi Ikeda
year	2021
title	Exploring the Self-Organizing Structure of the Moroccan Medina: A Simulation Model for Generating Urban Form
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 672-685
summary	This research explores the use of generative design and computational simulations in the exploration of urban compositions based on traditional urban forms from North Africa. Upon the examination of these urban settlements, we discuss the relationship between traditional urban form and generative urbanism theory. We investigate several factors that allow these self-generated urban tissues to be highly adaptive to social, spatial, and environmental change. Following this, we formulate guidelines to reinterpret some of the characteristics of these urban forms. Built on these features, the simulation seeks to explore the generation of abstract urban forms and their optimization. In this regard, this experiment utilizes 3D and parametric design tools (Rhinoceros 3D and Grasshopper) to define a generative urban simulation and optimization model. It explores the use of algorithmic design methodology in the definition and optimization of the generated urban form. For this purpose, grid-based operations with base modules are used in conjunction with introverted urban blocks. We employ evolutionary algorithms and Pareto front methodology to visualize and rank a multitude of optimized results that are evaluated using three different and conflicting design objectives: sun exposure, physical accessibility, and urban density. The results are ranked and analyzed by comparing the outcomes of these different objective functions. The result of this study shows that it is possible to allow a degree of diversification of a myriad of urban configurations with a generative form-finding algorithm while still maintaining a rather commendable adaptability to various design constraints in the case of high-density settings. In this research, it is anticipated that an algorithmic design model is a fitting contemporary solution that can simulate the philosophy of a design made without a designer and offer a wide range of objective-based spatial solutions. It sets the stage for a discussion about the relevance of reinterpreting traditional urban forms from north Africa by designing a generative model that allows for self-organization.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	sigradi2021_250
id	sigradi2021_250
authors	Dotta Correa, Sara, Vaz, Carlos Eduardo Verzola, Pizzetti Mariano, Pedro Oscar and Maia, Mirian Aparecida
year	2021
title	A Shape Grammar Implementation: The Case of Fishing Villages in Santa Catarina
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 725–736
summary	The fishing villages of the Santa Catarina state, in Southern Brazil, have been suffering from a process of transformation that accompanies the replacement of the activities related to artisanal fishing in order to insert the dynamics of tourism. Aiming to preserve the underlying logic responsible for generating these self-built settlements, a shape grammar was elaborated and implemented in a visual programming environment to test its efficiency in reproducing the compositional language of the villages. The method involved the historical context and constructive typologies analysis, which made it possible to extract the corresponding rules regarding the spatial configuration of the corpus. The result emerged as a descriptive grammar, which later was implemented in a parametric modeling environment, algorithms in C# were used to generate the compositions with the aid of computational strategies based on random numbers, stochastic research and object-oriented programming.
keywords	Gramática da Forma, Modelagem Paramétrica, Programaçao, Comunidades Pesqueiras
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	caadria2021_113
id	caadria2021_113
authors	Fink, Theresa, Vuckovic, Milena and Petkova, Asya
year	2021
title	KPI-Driven Parametric Design of Urban Systems
doi	https://doi.org/10.52842/conf.caadria.2021.2.579
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 579-588
summary	We present a framework for data-driven algorithmic generation and post-evaluation of alternative urban developments. These urban developments are framed by a strategic placement of diverse urban typologies whose spatial configurations follow design recommendations outlined in existing building and zoning regulations. By using specific rule-based generative algorithms, different spatial arrangements of these urban typologies, forming building blocks, are derived and visualized, given the aforementioned spatial, legal, and functional regulations. Once the envisioned urban configurations are generated, these are evaluated based on a number of aspects pertaining to spatial, economic, and thermal (environmental) dimensions, which are understood as the key performance indicators (KPIs) selected for informed ranking and evaluation. To facilitate the analysis and data-driven ranking of derived numeric KPIs, we deployed a diverse set of analytical techniques (e.g., conditional selection, regression models) enriched with visual interactive mechanisms, otherwise known as the Visual Analytics (VA) approach. The proposed approach has been tested on a case study district in the city of Vienna, Austria, offering real-world design solutions and assessments.
keywords	Urban design evaluation; parametric modelling; urban simulation; environmental performance; visual analytics
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	cdrf2021_129
id	cdrf2021_129
authors	Fuyuan Liu, Min Chen, Lizhe Wang, Xiang Wang, and Cheng-Hung Lo
year	2021
title	Custom-Fit and Lightweight Optimization Design of Exoskeletons Using Parametric Conformal Lattice
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_12
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	This paper presents an integrated design method for the customization and lightweight design of free-shaped wearable devices, illustrated by a lower limb exoskeleton. The customized design space is derived from the 3D scanning models. Based on the finite element analysis, the structural framework is determined through topology optimization with allowable strength. By means of generative design, the lattice library is constructed to fill the frames under different conformal algorithms. Finally, the proposed method is illustrated by the exoskeleton design case.
series	cdrf
email
last changed	2022/09/29 07:53

_id	ecaade2021_252
id	ecaade2021_252
authors	Kotov, Anatolii and Vukorep, Ilija
year	2021
title	Gridworld Architecture Testbed
doi	https://doi.org/10.52842/conf.ecaade.2021.1.037
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 37-44
summary	Over centuries architects have developed frameworks of representation of the built surroundings in diverse types of drawings or models. With the rise of digital techniques, virtual models slowly replace these representation techniques but are still far from replicating the real world's ambiguity and complexity. This paper wants to address the representational problems of architecture combined with architecture-related AI systems and missing standardized tests for such systems. For this, we suggest a standardized computational testbed that can serve for developing, testing and benchmarking design solutions for abstracted architectural problems with various AI approaches in a game-like environment.Furthermore, this paper will discuss architectural problems' subdivision into atomic subtasks solvable by specific AI systems. Ideally, there is a waste number of possible architectural subtasks that can be applied. The paper presents some examples of possible architectural game strategies that abstractly deal with concepts of walls and borders, zones and connections. Although this paper mentions different Reinforcement Learning techniques, it is not focusing on fine-tuning the AI algorithms. It aims to help achieve automation of specific design workflow phases, then in the longer term to optimize and propose alternative design solutions and improve the architectural community's overall work.
keywords	Gridworld Testbed; AI Aided Architecture; Benchmarking AI Algorithms
series	eCAADe
email
last changed	2022/06/07 07:51

_id	caadria2021_196
id	caadria2021_196
authors	Lu, Yueheng, Tian, Runjia, Li, Ao, Wang, Xiaoshi and Jose Luis, Garcia del Castillo Lopez
year	2021
title	CubiGraph5K - Organizational Graph Generation for Structured Architectural Floor Plan Dataset
doi	https://doi.org/10.52842/conf.caadria.2021.1.081
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 81-90
summary	In this paper, a novel synthetic workflow is presented for procedural generation of room relation graphs of floor plans from structured architectural datasets. Different from classical floor plan generation models, which are based on strong heuristics or low-level pixel operations, our method relies on parsing vectorized floor plans to generate their intended organizational graph for further graph-based deep learning. This research work presents the schema for the organizational graphs, describes the generation algorithms, and analyzes its time/space complexity. As a demonstration, a new dataset called CubiGraph5K is presented. This dataset is a collection of graph representations generated by the proposed algorithms, using the floor plans in the popular CubiCasa5K dataset as inputs. The aim of this contribution is to provide a matching dataset that could be used to train neural networks on enhanced floor plan parsing, analysis and generation in future research.
keywords	Graph Theory; Algorithm; Architecture Design Dataset; Organizational Graph
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ascaad2021_063
id	ascaad2021_063
authors	Ronagh, Ehsan; Mohammadjavad Mahdavinejad, Anoosha Kia
year	2021
title	A New Paradigm in Generative Design Linking Parametric Architecture and Music to Form Finding
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 227-240
summary	In recent years, geometry and innovations have become an important topic in contemporary architecture. In addition, the 21st century is considered as a new era in architectural design. Computer software development has introduced the theory of form-finding. The present study proposes a novel design and construction method in form-finding based on the relationship between parametric architecture and music. To achieve this goal, several algorithms were designed. The simulation was performed in Rhino with Grasshopper and Firefly plugins, and extensive prototyping of the shells was performed at High-performance Architecture Lab (HAL). This study is aimed at presenting a new design and construction method as a generative design that can use two main characteristics of sound namely frequency and intensity over time. The design also forms the numerical outputs of the music to deform the modular two-dimensional geometric patterns and transform them into three-dimensional parametric shells. The resulting research is fully applicable at a large scale such as urban landscape and small scale as interior design.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	sigradi2022_65
id	sigradi2022_65
authors	Roncoroni, Umberto
year	2022
title	Programming complex 3D meshes. A generative approach based on shape grammars.
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 335–346
summary	This article summarizes the results of art based research developed thanks to a grant by the PUCP University of Lima in 2021-2022. An open source generative solution will be described, based on generative grammars, to create very complex and programmable 3D meshes. Analyzing hundreds of models generated with these algorithms, a solution was found based on the idea of “intelligent meshes”, which change their behavior during the modeling process. This is done using tags, or vertices identifiers, that, like genes, describe the topological characteristics of each vertex and its generative development during the process. Tags can be programmed interactively editing its data with tools provided by the interface or using generative grammars that allow an incredible variety of complex forms and stimulate the user creativity. The research findings also elucidate some important conceptual issues, like the importance of original technology development to defend cultural identity.
keywords	Computational creativity, Cultural identity, Generative grammars
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	sigradi2023_234
id	sigradi2023_234
authors	Santos, Ítalo, Andrade, Max, Zanchettin, Cleber and Rolim, Adriana
year	2023
title	Machine learning applied in the evaluation of airport projects in Brazil based on BIM models
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 875–887
summary	In a country with continental dimensions like Brazil, air transport plays a strategic role in the development of the country. In recent years, initiatives have been promoted to boost the development of air transport, among which the BIM BR strategy stands out, instituted by decree n-9.983 (2019), decree n-10.306 (2020) and more recently, the publication of the airport design manual (SAC, 2021). In this context, this work presents partial results of a doctoral research based on the Design Science Research (DSR) method for the application of Machine Learning (ML) techniques in the Artificial Intelligence (AI) subarea, aiming to support SAC airport project analysts in the phase of project evaluation. Based on a set of training and test data corresponding to airport projects, two ML algorithms were trained. Preliminary results indicate that the use of ML algorithms enables a new scenario to be explored by teams of airport design analysts in Brazil.
keywords	Airports, Artificial intelligence, BIM, Evaluation, Machine learning.
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	ijac202119310
id	ijac202119310
authors	Schwartz, Yair; Raslan, Rokia; Korolija, Ivan; Mumovic, Dejan
year	2021
title	A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 401–430
summary	Building performance evaluation is generally carried out through a non-automated process, where computational models are iteratively built and simulated, and their energy demand is calculated. This study presents a computational tool that automates the generation of optimal building designs in respect of their Life Cycle Carbon Footprint (LCCF) and Life Cycle Costs (LCC). This is achieved by an integration of three computational concepts: (a) A designated space-allocation generative-design application, (b) Using building geometry as a parameter in NSGA-II optimization and (c) Life Cycle performance (embodied carbon and operational carbon, through the use of thermal simulations for LCCF and LCC calculation). Examining the generation of a two-storey terrace house building, located in London, UK, the study shows that a set of building parameters combinations that resulted with a pareto front of near-optimal buildings, in terms of LCCF and LCC, could be identified by using the tool. The study shows that 80% of the optimal building’s LCCF are related to the building operational stage (o= 2), while 77% of the building’s LCC is related to the initial capital investment (o= 2). Analysis further suggests that space heating is the largest contributor to the building’s emissions, while it has a relatively low impact on costs. Examining the optimal building in terms compliance requirements (the building with the best operational performance), the study demonstrated how this building performs poorly in terms of Life Cycle performance. The paper further presents an analysis of various life-cycle aspects, for example, a year-by-year performance breakdown, and an investigation into operational and embodied carbon emissions.
keywords	Generative design, genetic algorithms, thermal simulation, life cycle, carbon, LCA, NSGA-II, building performance
series	journal
email
last changed	2024/04/17 14:29

_id	ascaad2021_041
id	ascaad2021_041
authors	Taºdelen, Sümeyye; Leman Gül
year	2021
title	Social Network Analysis of Digital Design Actors: Exploratory Study Covering the Journal Architectural Design
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 280-292
summary	This research asks the question of how the design knowledge production mechanism is processed differentiates digital design actors from each other in the social media/professional and academic fields of architecture. Due to the broad nature of the research question, the study focuses on academia and academia-related media through prominent architect-authors and subject titles in the literature. Bourdieu’s concept of capital is introduced, in which cultural and symbolic capital are considered part of the production values of digital design actors. Digital design actors use image-based social media tools such as Instagram effectively. The paper uses two methods: the first is a bibliographical analysis of author-texts, and the second is a social network analysis. By employing the keyword-based search from the Web of Science database, this study has managed to extract papers with full records (citations, keywords, and abstracts), with the journal Architectural Design having most publications. Considering that both academicians and professionals contribute to publications in Architectural Design, we selected all its publications between 2010-2020 for bibliometric analysis. These analysis techniques include the bibliometric network analyses and social network analysis with the focus on visualizing the algorithms and statistical calculations of well-established metrics. The research reveals the most critical nodes of the bibliometric network by calculating the appropriate central metrics. The network formed by the selected Instagram accounts of digital design actors are shown to be a small-scale network group, while the hashtags of digital design concepts are more numerous than the digital design actors.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	sigradi2021_134
id	sigradi2021_134
authors	Uzun, Can
year	2021
title	What can Colors and Shapes Tell about Generative Adversarial Networks?
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 161–171
summary	The study aims to understand the how’s and what’s of creating an architectural dataset for generative adversarial nets through the evaluation of the effects of colors and shapes in image datasets on generative adversarial nets. Throughout the paper, six generative adversarial network training sessions are conducted on DCGAN and context-encoder algorithms with three different datasets having different complexities for colors and shapes. Firstly the color and shape complexities are analyzed for datasets. For color complexity, heuristic analyze is applied and for shape complexity, gray level occurrence matrix entropy which gives the textural complexity is utilized. In the end, the complexities and the training results are evaluated. Results show that color complexity has an important role for generative adversarial networks to generate colors correctly. Regularity in shape complexity /gray level co-occurrence matrix entropy distribution facilitates the algorithm training and shape generating processes.
keywords	Context-Encoder, GAN, Colors, Shapes
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	ecaade2021_247
id	ecaade2021_247
authors	Wibranek, Bastian, Liu, Yuxi, Funk, Niklas, Belousov, Boris, Peters, Jan and Tessmann, Oliver
year	2021
title	Reinforcement Learning for Sequential Assembly of SL-Blocks - Self-interlocking combinatorial design based on Machine Learning
doi	https://doi.org/10.52842/conf.ecaade.2021.1.027
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 27-36
summary	Adaptive reconfigurable structures are seen as the next big step in the evolution of architecture. However, to achieve this vision, new tools are required that enable autonomous configuration of given elements based on a specified design objective. Various approaches have been considered in the past, ranging from rule-based methods to evolutionary optimization. Although successful in applications where search heuristics or informative objective functions can be provided, these methods struggle with long-term planning problems. In this paper, we tackle the problem of sequential assembly of SL-blocks which has the character of a combinatorial optimization problem. We explore the applicability of deep reinforcement learning algorithms that recently showed great success on combinatorial problems in other domains, such as board games and molecular design. We highlight the unique challenges presented by the architectural design setting and compare the performance to evolutionary computation and heuristic search baselines.
keywords	Reinforcement Learning; Architectural Assembly; Discrete Design; SL-blocks; Dry Joined
series	eCAADe
email
last changed	2022/06/07 07:57

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