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Rethinking Computer-Aided Architectural Design (CAAD) - From generative algorithms and Architectural Intelligence to Environmental Design and Ambient Intelligence 19th

Rethinking Computer-Aided Architectural Design (CAAD) - From...

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19th International Conference on Computer-Aided Architectural Design (CAAD) - Design Imperatives - The Future is Now

作者： Stojanovski, Todor Zhang, Hui Frid, Emma Chhatre, Kiran Peters, Christopher Samuels, Ivor Sanders, Paul Partanen, Jenni Lefosse, Deborah KTH Royal Inst Technol Stockholm Sweden Univ Birmingham Urban Morphol Res Grp Birmingham W Midlands England Deakin Univ Melbourne Vic Australia Tallinn Univ Technol Tallinn Estonia Sapienza Rome Italy

ISBN: (纸本)9789811912801;9789811912795

Computer-Aided Architectural Design (CAAD) finds its historical precedents in technological enthusiasm for generative algorithms and architectural intelligence. Current developments in Artificial Intelligence (AI) and paradigms in Machine Learning (ML) bring new opportunities for creating innovative digital architectural tools, but in practice this is not happening. CAAD enthusiasts revisit generative algorithms, while professional architects and urban designers remain reluctant to use software that automatically generates architecture and cities. This paper looks at the history of CAAD and digital tools for Computer Aided Design (CAD), Building Information Modeling (BIM) and Geographic Information Systems (GIS) in order to reflect on the role of AI in future digital tools and professional practices. Architects and urban designers have diagrammatic knowledge and work with design problems on symbolic level. The digital tools gradually evolved from CAD to BIM software with symbolical architectural elements. The BIM software works like CAAD (CAD systems for Architects) or digital board for drawing and delivers plans, sections and elevations, but without AI. AI has the capability to process data and interact with designers. The AI in future digital tools for CAAD and Computer-Aided Urban Design (CAUD) can link to big data and develop ambient intelligence. Architects and urban designers can harness the benefits of analytical ambient intelligent AIs in creating environmental designs, not only for shaping buildings in isolated virtual cubicles. However there is a need to prepare frameworks for communication between AIs and professional designers. If the cities of the future integrate spatially analytical AI, are to be made smart or even ambient intelligent, AI should be applied to improving the lives of inhabitants and help with their daily living and sustainability.

关键词： Artificial Intelligence (AI) Computer-Aided Architectural Design (CAAD) Architectural intelligence generative algorithms Environmental design Ambient intelligence

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Additively manufactured textiles and parametric modelling by generative algorithms in orthopaedic applications

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RAPID PROTOTYPING JOURNAL 2020年第5期26卷 827-834页

作者： Ricotta, Vito Campbell, R. Ian Ingrassia, Tommaso Nigrelli, Vincenzo Univ Palermo Dipartimento Ingn Palermo Italy Loughborough Univ Dept Design Loughborough Leics England

Purpose The purpose of this paper is to implement a new process aimed at the design and production of orthopaedic devices fully manufacturable by additive manufacturing (AM). In this context, the use of generative algorithms for parametric modelling of additively manufactured textiles (AMTs) also has been investigated, and new modelling solutions have been proposed. Design/methodology/approach A new method for the design of customised elbow orthoses has been implemented. In particular, to better customise the elbow orthosis, a generative algorithm for parametric modelling and creation of a flexible structure, typical of an AMT, has been developed. Findings To test the developed modelling algorithm, a case study based on the design and production of an elbow orthosis made by selective laser sintering was investigated. The obtained results have demonstrated that the implemented algorithm overcomes many drawbacks typical of the traditional CAD modelling approaches. The parametric CAD model of the orthosis obtained through the new approach is characterised by a flexible structure with no deformations or mismatches and has been effectively used to produce the prototype through AM technologies. Originality/value The obtained results present innovative elements of originality in the CAD modelling sector, which can contribute to solving problems related to modelling for AM in different application fields.

关键词： Additive manufacturing Additively manufactured textiles generative algorithms Elbow orthosis CAD modeling AM technologies

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Topology optimization for heat conduction using generative design algorithms

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2017年第3期55卷 1063-1077页

作者： Lohan, Danny J. Dede, Ercan M. Allison, James T. Univ Illinois Dept Ind & Enterprise Syst Engn 117 Transportat Bldg104 S Mathews Ave Urbana IL 61801 USA Toyota Res Inst North Amer Elect Res Dept 1555 Woodridge Ave Ann Arbor MI 48105 USA

In this article we present a new approach to topological design for steady-state heat conduction. The method capitalizes on the use of a generative algorithm to represent topology, resulting in a decrease in the number of variables in the design description. Using a generative algorithm as a design abstraction, the optimization technique is targeted to dendritic topologies that are known to perform well for heat conduction. Specifically, a traditional topology optimization technique (SIMP) is confirmed to produce branching characteristics in optimal designs. The Space Colonization Algorithm, which can generate similar topological patterns, is selected for in-depth investigation. A genetic algorithm drives generation of design candidates, providing a highly diversified search of the target design space. Finally, several synthesized optimal designs for steady-state heat conduction, derived using the described algorithms, are compared using commercial finite element software.

关键词： Topology optimization generative algorithms Conductive heat transfer

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Managing variable-dimension structural optimization problems using generative algorithms

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2015年第4期52卷 695-715页

作者： Khetan, Ashish Lohan, Danny J. Allison, James T. Univ Illinois Dept Ind & Enterprise Syst Engn 117 Transportat Bldg104 S Mathews Ave Urbana IL 61801 USA

This article introduces a novel design abstraction concept for efficient truss topology and geometry optimization. The core advancement introduced here is to represent truss topology and geometry using rules of generative algorithms, and to operate on generative algorithm rules using a genetic algorithm rather than directly on the design description. This indirect design representation supports efficient exploration of variable and high-dimension design topologies. generative design strategies are also independent of any kind of ground structure, thus avoiding the inherent limitations of ground structure approaches that may hinder innovative design solutions by defining a priori what topologies may be considered. We present new generative algorithm strategies that automatically satisfy structural stability constraints, and that can produce truss topologies with a diversity of patterns within an individual truss design. Truss topology and geometry is optimized in an outer-loop by a genetic algorithm that operates on generative algorithm rules, and size optimization is performed in an inner-loop for each candidate topology using sequential linear programming. The proposed methodology supports concurrent optimization of truss topology, geometry, and size. The generative algorithm abstraction layer also supports the design of variable-dimension structures, which can be generated from the same fixed-dimension rule set. Finally, we demonstrate the effectiveness of the new methodology by examining archetypal two- and three-dimensional truss design optimization problems.

关键词： Truss topology optimization generative algorithms Structural optimization

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Reverse Engineering Workflows for the Structural Assessment of Historical Buildings

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INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE 2025年第5期19卷 702-723页

作者： Massafra, Angelo Prati, Davide Gulli, Riccardo Alma Mater Studiorum Univ Bologna Dept Architecture DA Viale Risorgimento 2 I- 40136 Bologna Italy Univ Bergamo Dept Engn & Appl Sci DISA Bergamo Italy

Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the "deformed state", with their ideal configuration, reconstructed using VP algorithms, considered the "original state". The outcomes of this comparison facilitate the investigation of the components' structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

关键词： Built cultural heritage generative algorithms parametric modeling reverse engineering structural analysis terrestrial laser scanning

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Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae640页

作者： Ultsch, Alfred Loetsch, Joern Univ Marburg DataBion Res Grp Hans Meerwein Str 6 D-35032 Marburg Germany Goethe Univ Inst Clin Pharmacol Theodor Stern Kai 7 D-60590 Frankfurt Germany Univ Helsinki Fac Med Haartmaninkatu 8 Helsinki 00014 Finland Fraunhofer Inst Translat Med & Pharmacol ITMP Theodor Stern Kai 7 D-60596 Frankfurt Germany

Small sample sizes in biomedical research often led to poor reproducibility and challenges in translating findings into clinical applications. This problem stems from limited study resources, rare diseases, ethical considerations in animal studies, costly expert diagnosis, and others. As a contribution to the problem, we propose a novel generative algorithm based on self-organizing maps (SOMs) to computationally increase sample sizes. The proposed unsupervised generative algorithm uses neural networks to detect inherent structure even in small multivariate datasets, distinguishing between sparse "void" and dense "cloud" regions. Using emergent SOMs (ESOMs), the algorithm adapts to high-dimensional data structures and generates for each original data point k new points by randomly selecting positions within an adapted hypersphere with distances based on valid neighborhood probabilities. Experiments on artificial and biomedical (omics) datasets show that the generated data preserve the original structure without introducing artifacts. Random forests and support vector machines cannot distinguish between generated and original data, and the variables of original and generated data sets are not statistically different. The method successfully augments small group sizes, such as transcriptomics data from a rare form of leukemia and lipidomics data from arthritis research. The novel ESOM-based generative algorithm presents a promising solution for enhancing sample sizes in small or rare case datasets, even when limited training data are available. This approach can address challenges associated with small sample sizes in biomedical research, offering a tool for improving the reliability and robustness of scientific findings in this field. Availability: R library "Umatrix" (https://***/package=Umatrix).

关键词： machine learning generative algorithms data science biomedical data self-organizing maps artificial neurons data generation

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ChatGPT in medical imaging higher education

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RADIOGRAPHY 2023年第4期29卷 792-799页

作者： Currie, G. Singh, C. Nelson, T. Nabasenja, C. Al-Hayek, Y. Spuur, K. Charles Sturt Univ Wagga Wagga NSW Australia Charles Sturt Univ Port Macquarie NSW Australia Charles Sturt Univ Sch Dent & Med Sci Locked Bag 588 Wagga Wagga NSW 2678 Australia

Introduction: Academic integrity among radiographers and nuclear medicine technologists/scientists in both higher education and scientific writing has been challenged by advances in artificial intelligence (AI). The recent release of ChatGPT, a chatbot powered by GPT-3.5 capable of producing accurate and human-like responses to questions in real-time, has redefined the boundaries of academic and scientific writing. These boundaries require objective evaluation. Method: ChatGPT was tested against six subjects across the first three years of the medical radiation science undergraduate course for both exams (n = 6) and written assignment tasks (n = 3). ChatGPT submissions were marked against standardised rubrics and results compared to student cohorts. Submissions were also evaluated by Turnitin for similarity and AI ***: ChatGPT powered by GPT-3.5 performed below the average student performance in all written tasks with an increasing disparity as subjects advanced. ChatGPT performed better than the average student in foundation or general subject examinations where shallow responses meet learning outcomes. For discipline specific subjects, ChatGPT lacked the depth, breadth, and currency of insight to provide pass level ***: ChatGPT simultaneously poses a risk to academic integrity in writing and assessment while affording a tool for enhanced learning environments. These risks and benefits are likely to be restricted to learning outcomes of lower taxonomies. Both risks and benefits are likely to be constrained by higher order taxonomies. Implications for practice: ChatGPT powered by GPT3.5 has limited capacity to support student cheating, introduces errors and fabricated information, and is readily identified by software as AI generated. Lack of depth of insight and appropriateness for professional communication also limits capacity as a learning enhancement tool.

关键词： ChatGPT Artificial intelligence Higher education Academic integrity generative algorithms Language model

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generative Design for Additively Manufactured Textiles in Orthopaedic Applications 10th

Generative Design for Additively Manufactured Textiles in Or...

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10th International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing, JCM 2020

作者： Ricotta, V. Campbell, R. Ian Ingrassia, T. Nigrelli, V. Dipartimento di Ingegneria Università degli Studi di Palermo Viale delle Scienze Palermo90128 Italy Loughborough Design School Loughborough University LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9783030705657

The aim of this work is to implement a new process for the design and production of orthopaedic devices to realize entirely by Additive Manufacturing (AM). In particular, a generative algorithm for parametric modelling of flexible structures to use in orthopaedic devices has been developed. The developed modelling algorithm has been applied to a case study based on the design and production of a customized elbow orthosis made by Selective Laser Sintering. The results obtained have demonstrated that the developed algorithm overcomes many drawbacks typical of traditional CAD modelling approaches. FEM simulations have been also performed to validate the design of the orthosis. The new modelling algorithm allows designers to model flexible structures with no deformations or mismatches and to create parametric CAD models to use for the production of orthopaedic devices through AM technologies. © 2021, The Author(s).

关键词： Additive Manufacturing Additively manufactured textiles CAD modelling Elbow orthosis generative algorithms

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The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities

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ATENCION PRIMARIA 2024年第2期56卷 102820页

作者： Vidal-Alaball, Josep Zafra, Robert Panades Escale-Besa, Anna Martinez-Millana, Antonio Fdn Inst Univ Recerca Atencio Primaria Salut Unitat Suport Recerca Catalunya Cent Barcelona Spain Inst Catala Salut Grp Recerca Promocio Salut Ambit Rural Gerencia Atencio Primaria & Comunitat Catalunya C Barcelona Spain Univ Cent Catalunya Univ Vic Fac Med Barcelona Spain Grp Salut Digital CAMFIC Barcelona Spain Inst Catala Salut Equip Atencio Primaria Anoia Rural Gerencia Atencio Primaria & Comunitat Catalunya C Barcelona Spain Inst Catala Salut Equip Atencio Primaria Navas Balsareny Gerencia Atencio Primaria & Comunitat Catalunya C Barcelona Spain Univ Politecn Valencia Inst Univ Invest Aplicac Tecnol Informac & Comuni Valencia Spain

Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.(c) 2023 The Authors. Published by Elsevier Espan similar to a, S.L.U. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Artificial intelligence Primary care Digital health Machine learning algorithms generative algorithms

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Analysis and management of structural deformations through parametric models and HBIM workflow in architectural heritage

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JOURNAL OF BUILDING ENGINEERING 2022年 45卷

作者： Moyano, Juan Gil-Arizon, Ignacio Nieto-Julian, Juan E. Marin-Garcia, David Univ Seville Dept Graph Express & Bldg Engn Higher Tech Sch Bldg Engn 4A Reina Mercedes Ave Seville 41012 Spain

Maintaining the structure and ornaments of historical buildings is essential to preserve cultural heritage in any society. Thus, dating the state and the evolution of the elements requires a special treatment, including the application of both advanced numerical analysis and non-invasive data acquisition techniques. In the latter, making digital copies of geometries through 3D reconstruction models is of great interest to compare and analyse structural evolutionary data accurately. For this purpose, appropriate software with containers of information correlated to the parametric elements in a BIM environment should be used. However, it is imperative to advance from static to dynamic models to collect the structural transformations caused by both the pass of time and other factors. The methodology followed in this paper is based on the experimentation by creating digital twins. The portico of a courtyard in a historical building from the 18th century was used as test bed. Based on the bibliographical recommendations, the terrestrial laser scanner is applied as a technique to acquire accurate data. The point cloud is used as a referential auxiliary to survey the model in the BIM platform with the Revit software. To assess the quality of the model built and to analyse the structural deviations between the parametric model and the actual geometry, the Dynamo script is used. To validate the experimentation, structural deviations are measured using both the parametric model and the point cloud with CloudCompare, a software for data treatment. The results were very positive because the deviation between the data obtained by Dynamo (c) and CloudCompare in the most unfavourable construction unit was between 0.5 and 1.17 cm, so these techniques are highly appropriate to review visual records and to analyse structural deviations. This new approach presents a new gap in the 3D reconstruction to date and control architectural structures, particularly in historical buildings.

关键词： HBIM Terrestrial laser scanning generative algorithms Structural systems reverse engineering Cultural management

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