Design Computation Input/Output 2022最新文献

{"title":"Components Fusion in Modo Foundry","authors":"Fadhil Fadhil","doi":"10.47330/dcio.2022.pfcu2358","DOIUrl":"https://doi.org/10.47330/dcio.2022.pfcu2358","url":null,"abstract":"The course will introduce Modo software as a tool for component-based design. The design approach will focus on the fusion of discrete components with a distinct fabrication process. The course will introduce the process of starting with a primitive geometry that evolves into a multi-object component. The workshop will also address the upward and downward relationship between different parts of the component. We will introduce multiple techniques that explore multi-resolution surface modelling and the difference between 2D textures and 3D textures. Lastly, we will have a brief introduction of Modo real-time rendering and different ways of lighting strategies and texture mapping.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128580322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aesthetic Measure of Architectural Photography utilizing Computer Vision: Parts-from-Wholes","authors":"Victor Sardenberg, M. Becker","doi":"10.47330/dcio.2022.ggnl1577","DOIUrl":"https://doi.org/10.47330/dcio.2022.ggnl1577","url":null,"abstract":"The existing methods for solution space navigation require numerical values to score solutions. The authors introduce a method of quantitative aesthetic evaluation utilizing Computer Vision (CV) as a criterion to navigate solution spaces. Therefore, aesthetics can complement structural, environmental, and other quantitative criteria. The work stands in the extended history of quantifying the visual aesthetic experience. Some precedents are: Birkhoff [1933] and Max Bense [1965] built an approach with experiments to empirically support a measure, whereas Birkin [2010], Ostwald, and Vaughan [2016] devised the first computational methods working on vector drawings. Our research automates and accelerates aesthetic quantification by utilizing CV to extract computable datasets from images. We are especially keen on architectural images as a shorthand to assign an aesthetic value to design, aiming to navigate the solution space in architecture. This work devises a method for rearranging parts in architectural images focusing on formal aspects, in opposition to semantic segmentation where objects unrelated to architectural design (cars, persons, sky…) are quantified to score images [Verma and Jana and Ramamritham 2018]. It uses Maximally Stable Extremal Regions (MSER) [Matas 2004] to recognize architectural parts because it is superior to similar methods such as SimpleBlobDetector in this task. Our method disassembles the parts in a diagram of scaled parts (Fig. 2) to analyze them in isolation, and a diagram of connectivity graph (Fig. 3), to evaluate relationships. These diagrams are examined to compare photos of buildings, cars, and trees to assess the applicability of such a method to a range of objects. Parts and connections are thus quantified, and these values are inputted in a refined version of Birkhoff’s formula to calculate an aesthetic score for each image for navigating the solution space. Finally, it tests the method to draw comparisons between the discrete and continuous paradigms (Fig. 1) in the contemporary discourse of architecture, comparing Zaha Hadid Architects` Heydar Aliyev Centre and Gilles Retsin´s Diamonds House to argue that there is a difference between the aesthetic effects of continuous and discrete designs, besides their distinction in tectonic logic. The method proved to be an efficient procedure for comparatively quantifying the aesthetic judgment of architectural images, enabling designers to incorporate aesthetics as a complementary criterion for solution space navigation in computational design. The method of computational aesthetic measure for solution space navigation and its calibrations via crowdsourced evaluation of images is further detailed in a paper by the authors being published at the 2022 eCAADe conference.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114777882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eye on HMI - Assessment of Human-Machine Interface with wearable eye-tracking glasses","authors":"M. Oskina, Z. Rusák, Peter Boom","doi":"10.47330/dcio.2022.gpqp2161","DOIUrl":"https://doi.org/10.47330/dcio.2022.gpqp2161","url":null,"abstract":"More and more modern transport modalities are equipped with complex human-machine interfaces (HMI). HMI aim to narrow the information gap between the complex automation system and their human operator to ensure fast, effective interaction and decision making. We see HMI in the traffic controllers' rooms, the ADAS-equipped vehicles, the public transport drivers' rooms, and many other modern transport modes. Designers create HMIs to effectively draw the operator’s attention to the most necessary and critical information and to facilitate accurate and fast decision making. Whether these systems adequately support human operators and achieve the intention of their designer is difficult to test objectively. [Hamilton and Grabowki 2013] showed that visual, manual and cognitive distractions of ADAS-equipped vehicles tend to distract drivers, who in turn behave less safe on the roads. There is, however, no comprehensive overview about the typical cognitive challenges operators facing in different domains of HMI applications and how these challenges can be objectively assessed. We conducted a series of interviews on difficulties of operators’ Human-Machine interface experience with human factors experts working with in railway and ADAS systems and investigated Endsley's situation awareness theory in dynamic systems [Endsley 1995]. Our interviewees reported several typical issues from their HMI studies, including missing events on the HMI displays, information overload of operators, lack of contextual and situational awareness and, as a resulting mismatch in expected and performed operator actions. We aim to develop and objective approach based on mobile eye tracking technology that can be used to characterize operator situation awareness, decision making and task performance and validate HMI designs in specific mobility and industry applications. The first step of our method is HAZOP analysis of the Human-Machine events and operator tasks, which results in a set of use cases for the eye-tracking experiments. In the experiments, we use wearable eye-tracking glasses combined with AI based computer vision algorithms. Wearable eyetracking enables us to conduct studies in real world scenarios, while AI based computer vision helps use to automatically identify relevant events and streamline the eye tracking data analysis workflow. With the use of glasses, we collect hotspot analysis, sequence of eye movement analysis, time to capture alarms and other parameters. Finally, we use an AI (and open AI) component in the glasses to mark the event of interest and track when the eye interacts with an area or an event of interest. We process gained data to conclude the events engagement, mistakes in responses, and missed out information and explain the root causes. In the past period, we conducted a pilot study to validate the quality of data collected with the openeye eye-tracking equipment (https://kexxu.com/ ). In the next step, we will use validate our method in a fu","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124222795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postdigital Architecture. What Now?!","authors":"M. Colletti","doi":"10.47330/dcio.2022.kkrr9918","DOIUrl":"https://doi.org/10.47330/dcio.2022.kkrr9918","url":null,"abstract":"The hyphenated term ‘post-digital’ was coined over twenty years ago by sonic artist Kim Cascone as a means of thinking about the aesthetics of the time beyond the digital revolution in electronic music, and then adopted in the arts, media, and architecture. In architecture, the term has been adopted to promote an anti-digital ‘downgrading’ aesthetic agenda: as post(minus)digital. However, if understood as post(plus)digital – in short: postdigital – the expression appears to be indicative of an evolution of the digital beyond its first implementation as numerical or virtual alternative to traditional analogue design and making techniques. What postdigital traits and characteristics can be identified? How can they be implemented in architectural design? Why does it matter?","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117015203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Material Based Computational Design Strategies","authors":"Dustin White","doi":"10.47330/dcio.2022.ngwc1201","DOIUrl":"https://doi.org/10.47330/dcio.2022.ngwc1201","url":null,"abstract":"The lecture outlines the past five years of a research-based design practice with an interest how technology, craft, and materials come together in ways that explore the boundaries between design, architecture, and other disciplines. Specifically, the pedagogy of material based computational strategies supporting the integration of form, material, and structure by incorporating physical form-finding strategies with digital analysis and fabrication processes. In this approach material often comes before shape, with material explorations as the premise for making and fabricating, and design decisions that emerge from the results of the material experiments and testing. The work produced by my students and myself seeks to challenge digital technology and fabrication to further the relationship of material to machine and material to design. With the intent to develop and employ novel software techniques that aid in the translation from the virtual world to the physical medias we engage through craft and technology to hybridize design and making. The work presented varies in scale, technique, method, intent, and fabrication processes but is fascinated with thinking though material based computational design strategies.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132745074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KPF Karst Mountains Inspired Facades Optimisation","authors":"Gökhan Ongun, Mustafa Chehabeddine, E. Ramos, Ji Soo Han, Jonathan Dreyfus","doi":"10.47330/dcio.2022.nzjq3625","DOIUrl":"https://doi.org/10.47330/dcio.2022.nzjq3625","url":null,"abstract":"","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approach to Digital Twins of Woody Vegetation (Trees and Shrubs)","authors":"Andreas Luka, Yong Guo","doi":"10.47330/dcio.2022.mujm9015","DOIUrl":"https://doi.org/10.47330/dcio.2022.mujm9015","url":null,"abstract":"","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"42 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120820921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning assisted preliminary design methodology for repetitive design features in complex structures","authors":"Omar A.I. Azeem, L. Iannucci","doi":"10.47330/dcio.2022.caxl3310","DOIUrl":"https://doi.org/10.47330/dcio.2022.caxl3310","url":null,"abstract":"The current industrial practice used at the preliminary design stage of complex structures involves the use of multifidelity submodelling simulations to predict failure behaviour around geometric and structural design features of interest, such as bolts, fillets, and ply drops. A simplified global model without the design features is first run and the resulting displacement fields are transferred to multiple local models containing the design features of interest. The creation of these high-fidelity local feature models is highly expert dependent, and their subsequent simulation is highly time-consuming. These issues compound as these design features are typically repetitive in complex structures. This leads to long design and development cycles. Application of machine learning to this framework has the potential to capture a structural designer’s modelling knowledge and quickly suggest improved design feature parameters, thereby addressing the current challenges. In this work, we provide a proof of concept for a machine learning assisted preliminary design workflow, see Figure 1, whereby feature-specific surrogate models may be trained offline and used for faster and simpler design iterations. The key challenge is to maximise the prediction accuracy of failure metrics whilst managing the high dimensions required to represent design feature simulation parameters in a minimum training dataset size. These challenges are addressed using: a modified Latin Hypercube Sampling scheme adjusted to improve design of experiment in composite materials; a bi-linear work-equivalent homogenisation scheme to reduce the number of nodal degrees of freedom; a non-local volume-averaged stress-based approach to reduce the number of target features; and linear superposition of stacked bi-directional LSTM neural network models. This methodology is demonstrated in a case study of predicting the stresses of open hole composite laminates in an aerospace C-spar structure. Results highlight the high accuracy (>90%) and time saving benefit (>15x) of this new approach. This methodology may be used to faster correct and iterate the preliminary design of any large or complex structure where there are repetitive localised design features that may contribute to failure, such as in Formula 1 or wind turbines. Combined with exascale computing this methodology may also be applied for predictive virtual testing of digital twins.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fragmented Layers of Design Thinking: Limitations and Opportunities of Neural Language Model-assisted processes for Design Creativity","authors":"E. Vermisso","doi":"10.47330/dcio.2022.mmlw2640","DOIUrl":"https://doi.org/10.47330/dcio.2022.mmlw2640","url":null,"abstract":"This paper offers insights about the otherwise limited NLM-driven methodologies, supporting an examination of design creativity following the ‘process’ approach. [Abraham 2018] Recent application of AI models which rely on natural language processing (semantic references) is increasingly popular because of their directness and ease-of-use. Neural Language Models (NLMs) like VQGAN+CLIP, DALL-E, MidJourney) offer promising results, [Rodrigues, et al. 2021] seemingly bypassing the need for expensive datasets and technical expertise. Naturally, such models are limited because they cannot capture the multimodal complexity of architectural thinking and human cognition in general [Penrose 1989]. Alternative approaches propose the combination of NLMs with other artificial neural networks (ANNs) i.e. StyleGAN; CycleGAN which are custom-trained on domain-specific data. [Bolojan, Vermisso and Yousif 2022] Architects seek to expand their agency within such AI-assisted processes by controling the input encoding, so they can subsequently convert the generated outcomes to 3D models fairly directly. Still, AI models of computer vision like NLMs and GANs offer 2-dimensional output, which requires extensive decoding into 3-dimensional format. While this may seem severely constraining, it presents a silver lining when it comes to furthering design creativity. Designers are asked to scrutinize their methods from a cognitive standpoint, because these methodologies not only encourage, but demand thorough interrogation of the design intentionality, the design decision making factors and qualification criteria. Text-to-image correlation, on which NLMs rely, and their 2-dimensional output, ensure that certain important considerations are not circumvented. Instead of obtaining a 3D model, multiple possible -fragmented- versions of it are separately implied. Often, ‘fake’ images generated by the ANNs promote contradictory inferences of space, which require further examination. The hidden opportunity within the limited format of AI models echo Neil Spiller’s comments about the advantage of drawing over animation techniques twenty years ago: “Enigma is a creative tool that allows designers to see bifurcated outcomes in their sketches and drawings; it plays on the inability of drawings to faithfully record the distinct placement and extent of architectural elements”. [Spiller 2001] Comparing animations to static drawings, Spiller praised the drawing’s ability to hold “…an imagined past and an imagined future”. ‘Reading’ these results involves the (human) disentanglement of high and low-level features and consciously allocating their corresponding qualities for curation. The process of evaluating ‘parts-to-whole’ visual relationships is noteworthy because it depends on shifting our attention away from certain features, and an unconscious binding of visual elements. [Dehaene 2014] The philosopher Alain wrote that “The art of paying attention, the great art,…supposes the art of ","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123160990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation and Fabrication of Elastic Deployable Stripe Structures","authors":"P. Baquero, Daoming Liu, Yota Adilenido","doi":"10.47330/dcio.2022.fbeo7122","DOIUrl":"https://doi.org/10.47330/dcio.2022.fbeo7122","url":null,"abstract":"Deployable structures have many applications in architecture, from kinetic pavilions to temporary structures, to retractable rooftops. There are various advantages to building deployable elements in a factory, and then deploying them on site. It is simpler to join stripes in flat arrangements than to put them together in three dimensions. This study focuses on a novel approach for creating and simulating systems of elastic stripes and how they can be utilized to build 3d-surfaces. The goal is to find the correct 2D stripes geometry that when deployed corresponds to a given 3D designed surface. Deployment simulation is essential to the design phase, and it is believes that by designing, simulating, and re-using data from already tested physical models, kinetic design methodological framework would naturally transition from a Design-Fabrication-Simulation workflow into a Design-Simulation-Fabrication one. (Raviv et al. 2014). In order to find the stripes deployment and its proximity to the final 3d surface, three experiments are examined ere: Starting from a simple case, in order to get a negative Gaussian curvature (Figure 1, Top) and observe the transformation and distortion of the flat faces, a hexagonal flat model has been vertically extruded and anchored in two points. Then, investigating further deploying techniques for negative curvature surfaces, a 2D linear set of equal stripes is deployed evenly by adding a locker stripe on its ends (Figure 2, Bottom). From the prototype and the simulation deployment the distortion produced a negative Gaussian curvature. A more complex example of curved stripes, using a locker stripe at the start and connecting between them, a group of four curved deployable stripes were distorted and joined to create four arches while maintaining their opening state (Figure 3, Right). The same distortion was seen in both the arches simulation and the prototype.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122011972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}