bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation Algorithm Based on Weathered Rock Morphology and Optimization Algorithm for Design Applications
Authors: Ye Wei , Zhao Xiayu , Xu Weiguo 
Published: April 2023

Abstract
The rich organic pore spaces of weathered rocks bring inspiration to architectural design. Based on the existing research on the natural formation mechanism of weathered rocks, this paper proposes two algorithms that achieve natural formation mechanism simulation and morphology simulation. Firstly, this study deeply explores the intrinsic characteristics of weathered rocks; secondly, the basic framework of iterative cyclic calculation by multiple weathering forces is built to make the calculation results of 3D point cloud close to the real morphology of weathered rocks; subsequently, this study innovatively introduces a 2D stacked layer algorithm for optimization while maintaining the morphological characteristics; finally, the architecture design application of the optimization algorithm is verified. Compared with the 3D point cloud simulation algorithm, the 2D layered algorithm can greatly reduce the computational time complexity and control the generated space's utilization.
Research on Real-Time Interactive Spatial Element Optimization Method Based on EEG Signal—Taking Indoor Space Color and Window Opening Size as the Optimization Object
Authors: Zhang Zihuan , Li Zao , Guo Zhe 
Published: April 2023

Abstract
In recent years, the research on digital design and perceptual evaluation has gradually become a hot topic in the field of digital design. Based on digital space optimization theory and perceptual evaluation tools, this study attempts to establish an optimization method to optimize built space elements in real-time using human psychological indicators. This method takes the specific indicators of the Meditation value and Attention value in the human EEG signal analyzed by the TGAM module as the optimization objective, the architectural space color and the window size as the optimization object, and the multi-objective genetic algorithm as the optimization tool. To realize this optimization method, this research combines virtual reality scene and parametric linkage model to establish tool platform and workflow. Taking the optimization of typical residential space as an example by recruiting 50 volunteers to participate in the experiment, this study concludes that this method is effective and feasible through experiment and quantitative analysis of experimental results and lays the foundation for more EEG indicators and more complex spatial element optimization research in the future.
Using Text Understanding to Create Formatted Semantic Web from BIM
Authors: Li Jingming 
Published: April 2023

Abstract
The application of BIM in the building life cycle needs to be continuous. The information collected and accumulated in the early stages should flow to the subsequent phases. However, BIM applications currently focus on collision inspection, compliance inspection, and engineering calculation, few models can be successively used in the following stages. Remodeling is required in the operation and maintenance period, resulting in waste. Meanwhile, some of the information accumulated by BIM might be frequently used in the operation and maintenance stage, while some data are relatively rarely used. The semantic web can help manage building information at all stages. But the generation of a semantic web is mostly manually completed. It is necessary to standardize the repeated semantic description in the model and convert BIM into a standard semantic model for information indexing, reducing the resource consumption of model loading and optimizing the efficiency of the operation and maintenance system. When the existing research transforms from BIM to the semantic web, there will be a lack of information and descriptions of the ownership relationship between entities due to the limitation of formats. To realize the standard transformation from BIM to the semantic web, this work proposes a method of using Natural Language Processing (NLP) to understand the text and infer the relationship between entities according to the knowledge map. First, the entities are extracted from BIM, such as air conditioning unit, electric lamp, fan, etc., if the name of the extracted entity is irregular, the names are translated with the help of NLP and Ontology (such as brick or haystack) to obtain the standard definition. By comparing the complete knowledge graph (such as the knowledge graph of the air conditioning system), the relationships can be deduced, and then a standardized semantic model can be generated.
A Human–Machine Collaborative Building Spatial Layout Workflow Based on Spatial Adjacency Simulation
Authors: Zhong X. , Yu Fujia , Xu Beichen 
Published: April 2023

Abstract
The space layout of a reasonable modular building prototype is a time consuming and complex process. Many studies have optimised automatic spatial layouts based on spatial adjacency simulation. Although machine-produced plans satisfy the adjacency and area constraints, people still need further manual modifications to meet other spatially complex design requirements. Motivated by this, we provide a human–machine collaborative design workflow that simulates the spatial adjacency relationship based on physical models. Compared with previous works, our workflow enhances the automated space layout process by allowing designers to use environment anchors to make decisions in automatic layout iterations. A case study is proposed to demonstrate that the solution generated by our workflow can initially complete different customised design tasks. The workflow combines the advantages of the designer's decision-making experience in manual modelling with the machine's ability in rapid automated layout. In the future, it has the potential to be developed into a designer-machine collaboration tool for completing complex building design tasks.
Collective Intelligence and Effects of Anticipation
Authors: Ljubica Miric 
Published: April 2023

Abstract
Based on the extrapolation of contemporary theories that mind is a prediction machine, this paper points out the repetitive nature of the prediction parameters of collective intelligence anticipation scenarios and questions the hybridity of fears and desires regarding the evolution of artificial intelligence. Through the analysis of mnemonic principles of remembrance it combines the ancient technique of the art of memory with the contemporary views on the dynamics of perceptions in order to establish a link between the spatial constructs and their rapid expansion through the presence of the digital medium while in parallel suggesting an approach to the integration of the necessity for a more sophisticated systems of artificial intelligence into the collective intelligence. The main question of this paper is whether the anticipation of the future results in the creation of such a future, can a prediction-based interaction between man and machine govern its outcome? In order to attempt to develop potential new methods of integrating an idea of a different, more neutral outcome, the focus will be on the characteristics of perception that surpass the influenced, emotional response, and the observation of the general, innate human mechanisms of alignment. Through the study of the principles of memory the aim of this paper is to ask whether the individual comprehension of space and time as disengaged in given examples can in fact produce a system of ideas compatible in its nature to that of the machine itself. What are the main aspects of both human and machine that would stem their hybridity, and how should the collective intelligence adapt to enable the interlink?
A Rapid Wind Velocity Prediction Method in Built Environment Based on CycleGAN Model
Authors: Tan Chuheng , Zhong Ximing 
Published: April 2023

Abstract
Although the wind microclimate and wind environment play important roles in urban prediction, the time-consuming and complicated setup and process of wind simulation are widely regarded as challenges. There are several methods to use deep learning (DL) models for wind speed prediction by labeling pairs of wind simulation dataset samples. However, many wind simulation experiments are needed to obtain paired datasets, which is still time-consuming and cumbersome. Compared with previous studies, we propose a method to train a DL model without labelling paired data, which is based on Cycle Generative Adversarial Network (cycleGAN). To verify our hypothesis, we evaluate the results and process of the pix2pix model (requires paired datasets) and cycleGAN (does not requires paired datasets), and explore the difference of results between these two DL models and professional CFD software. The result shows that cycleGAN can perform as well as pix2pix in accuracy, indicating that some random city plans image samples and random wind simulation samples can train surrogate models as accurate as labelled DL methods. Although the DL method has similar results to the professional CFD method, the details of the wind flow results still need improvement. This study can help designers and policymakers to make informed decisions to choose Dl methods for real-time wind speed prediction for early-stage design exploration.
Demand-Driven Distributed Adaptive Space Planning Based on Reinforcement Learning
Authors: Jiang Wanzhu , Wang Jiaqi 
Published: April 2023

Abstract
In the second digital turn, the architecture driven by big data logic is gradually shifting from a traditional static entity to an intellective living organism. This paper explores a space planning algorithm that applies reinforcement learning to the multi-agent system to achieve condition adaptability. This algorithm contains an inclusive environment and programmable agents that represent independent spaces. Through reinforcement learning, personalized space needs are quantified as the agent’s Space Schema, which can provide adaptive behavior strategies to adjust volumetric room boundaries. The spatial organization emerges in multi-agent competition, guided by the Negotiation Schema, realizing the dynamic equilibrium of spatial relations and the stable maximization of collective interests. Through real-time interaction and distributed decision-making, this bottom-up method defines a new architectural paradigm that continuously changes based on demands with its high degree of variability, adaptability and evolvability.
AI Urban Voids: A Data-Driven Approach to Urban Activation
Authors: Algamdey Amal , Mastalski Aleksander , Chronis Angelos , Gurung Amar , Vargas Felipe Romero , Bodenbender German , Khairallah Lea 
Published: April 2023

Abstract
With the development of digital technologies, big urban data is now readily available online. This opens the opportunity to utilize new data and create new relationships within multiple urban features for cities. Moreover, new computational design techniques open a new portal for architects and designers to reinterpret this urban data and provide much better-informed design decisions. The “AI Urban Voids'' project is defined as a data-driven approach to analyze and predict the strategic location for urban uses in the addition of amenities within the city. The location of these urban amenities is evaluated based on predictions and scores followed by a series of urban analyses and simulations using K-Means clustering. Furthermore, these results are then visualized in a web-based platform; likewise, the aim is to create a tool that will work on a feedback loop system that constantly updates the information. This paper explains the use of different datasets from Five cities including Melbourne, Sydney, Berlin, Warsaw, and Sao Paulo. Python, Osmx libraries and K-means clustering open the way to manipulate large data sets by introducing a collection of computational processes that can override traditional urban analysis.
Collapsing Complexities: Encoding Multidimensional Architecture Models into Images
Authors: Sándor Viktória , Bank Mathias , Schinegger Kristina , Rutzinger Stefan 
Published: April 2023

Abstract
The paper details a 3D to 2D encoding method, which can store complex digital 3D models of architecture within a single image. The proposed encoding works in combination with a point cloud notation and a sequential slicing operation where each slice of points is stored as a single row of pixels in the UV space of a 1024 × 1024 image. The performance of the notation system is compared between a StyleGan2 and existing image editing methods and evaluated through the production of new 3D models of houses with material attributes. The uncovered findings maintain the relatively high level of detail stored through the encoding while allowing for innovative ways of form-finding—producing new and unseen 3d models of architectural houses.
Augmented Bricks an Onsite AR Immersive Design to Fabrication Framework for Masonry Structures
Authors: Song Yang , Agkathidis Asterios , Koeck Richard 
Published: April 2023

Abstract
The Augmented Bricks research project aims to develop an immersive design to fabrication framework for the assembly of masonry building components by incorporating robotic fabrication and augmented reality (AR) technologies. Our method incorporates two main phases: firstly, the design phase in which users’ gestures and interactions are being identified in AR for the immersive design and simulation process; secondly, an innovative robotic assembly phase in which users can control a robotic arm for assembly by interacting with the AR user interface (UI). Our framework is validated by the design and assembly of four brick-based columns. Our findings highlight that the proposed design to fabrication framework offers a novel, intuitive design inspiration and experience beyond the traditional design methods. It returns the task of assembling parametric structures with high-tech equipment back to the designers, allowing them to master and participate in the entire design to the fabrication process. The impact of this practice-based research will allow architects and designers to modify and construct their designs more simply and intuitively through the AR environment.
Exploration and Design of the Contemporary Bracket Set Through Topology Optimization
Authors: Duan C. B. , Shen S. Y. , Bao D. W. , Yan X. 
Published: April 2023

Abstract
Dou Gong, pronounced in Chinese, and known as Bracket Set, is a vital support component in the ancient wooden tectonic systems. It is located between the column and the beam and connects the eave and pillar, making the heavy roof extend out of the eaves longer. The development of the bracket set is entirely a microcosm of the development of ancient Chinese architecture; the aesthetic structure and oriental artistic temperament behind the bracket make it gradually become the cultural and spiritual symbol of traditional Chinese architecture. In the contemporary era, inheriting and developing the bracket set has become an essential issue. This paper introduces the topological optimization method bi-directional evolutionary structural optimization (BESO) for form-finding. Through analyzing the development trend of bracket set and mechanical structure, the authors integrate 2D and 3D optimization methods and apply the hybrid methods to form-finding. This research aims to design a new bracket set corresponding to “structural performance-based aesthetics.“ The workflow proposed in this paper is valuable for architrave and other traditional building components.
Large-Scale 3D Printing Using Recycled PET. The Case of Upcycle Lab @ DB Schenker Singapore
Authors: Raspall Felix , Bañón Carlos 
Published: April 2023

Abstract
Large-scale additive manufacturing for architectural applications is a growing research field. In the recent years, several real-scale projects demonstrated a preliminary viability of this technology for practical applications in architecture. Concurrently, the use of recycled polymers in 3d printing has progressed as a more sustainable feed for small-scale applications. However, there are limited empirical examples on the use of additive manufacturing using recycled polymers in large-scale and real-life architectural applications. This project develops two design and fabrication approaches to large-scale manufacturing using recycled Polyethylene Terephthalate (PET) from single-use bottles into large design elements and tests them in a real-life project. The two designs are discussed in detail: a 4 m diameter dome-like chandelier printed with a robotic extruder using recycled PET pellets, and a 3.5 m diameter chandelier using a Fused Deposition Modeling (FDM) printing farm. The paper covers the state of the art of related printing technologies and their gaps, describes the printing process developed in this research, details the design of the domes, and discusses the empirical evidence on the benefits and drawbacks of large-scale additive manufacturing using recycled polymers. Overall, the research demonstrates the possibilities of large-scale additive manufacturing using recycled polymers, adding findings form a real-life project to the growing body of research on additive manufacturing in architecture.
Towards a Digital Repertoire: Design and Fabrication of a Robotically-Milled Brass Chandelier
Authors: Loh Paul , Leggett David 
Published: April 2023

Abstract
The paper described the design and fabrication of a robotically-milled brass chandelier using a bespoke vertical axial revolving material holder as a robotic fixture. While the technique described is for a chandelier design, it has potential architectural applications, as demonstrated by architects such as Barkow Leibinger. The significance of this research lies in the increased flexibility of the technique performed using a robotic arm compared to the current industrial method using tubematic laser cutter. In addition, the paper outlined the design of the robotic fixture and the computational workflow to create an integrated design-to-fabrication workflow. The research highlighted robotic systems as a potential design environment through reflection on Material Engagement Theory (MET) framework. Critically, the workflow constructed design feedback as robotic agencies that provide affordances through the fabrication setup. Such affordances contribute to the designing process and refine craftsmanship by creating transactional relationships between tools and material as a digital repertoire. This emerging design environment extends robotic research into design practice.
Morphology of Free-Form Timber Structure Determination by LSTM Oriented by Robotic Fabrication
Authors: Meng Yiping , Sun Yiming , Chang Wen-Shao 
Published: April 2023

Abstract
Robotic arms are increasingly being used as an automation tool in non-standardized fabrication and construction, while the mechanical characteristics can also impact the accomplishment or the accuracy of the components. Timber is regularly used in different scales of a non-standard free-form structure fabricated by the robotic arm. The anisotropic mechanical characteristics of timber constrain the structural morphology. Developing a method of determining the morphology that meets the technical restrictions of the robotic arm and the material properties of timber is the aim of this research. In this paper, taking Centre Pompidou-Metz as a geometric case, glue-laminated timber as the main construction material, LSTM is applied for predicting the shape of the element. The geometric data is transformed into the fabrication data to testify to the kinematic singularities. The limitation of the workspace is derived from the Monte-Carlo method based on the DH model of the robotic arm. The experimental results show that the proposed method is effective in predicting the curves that match the characteristics of timber materials and robotic fabrication constraints.
Performative Ornament: Enhancing Humidity and Light Levels for Plants in Multispecies Design
Authors: Macruz Andrea , Daneluzzo Mirko , Tawakul Hind 
Published: April 2023

Abstract
The paper shifts the design conversation from a human-centered design methodology to a posthuman design, considering human and nonhuman actors. It asks how designers can incorporate a multispecies approach to creating greater intelligence and performance projects. To illustrate this, we describe a project of “ornaments” for plants, culminating from a course in an academic setting. The project methodology starts with “Thing Ethnography” analyzing the movement of a water bottle inside a house and its interaction with different objects. The relationship between water and plant was chosen to be further developed, considering water as a material to increase environmental humidity for the plant and brightness through light reflectance and refraction. 3D printed biomimetic structures as supports for water droplets were designed according to their performance and placed in different arrangements around the plant itself. Humidity levels and illuminance of the structures were measured. Ultimately, this created a new approach for working with plants and mass customization. The paper discusses the resultant evidence-based design and environmental values.
Regression-Based Inductive Reconstruction of Shell Auxetic Structures
Authors: Vivanco Tomás , Ojeda Juan Eduardo , Yuan Philip 
Published: April 2023

Abstract
This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
Common Ground—Online Platforms for Bottom-Up Collaborative Decision Making in Design Education
Authors: Stephan Nicolas , Lemarié Marine , Schinegger Kristina 
Published: April 2023

Abstract
Co-creation and real-time collaboration have always been an integral potential of digital design methodologies and have been accelerated by the rapid digitalization of teaching due to current societal developments. This paper discusses the prototype of a real-time multiplayer building platform as a video game developed for a first-year design studio impacted by pandemic-related teaching restrictions. The aim was to develop a methodology that enables first-year students to meet peers, build models collaboratively, and teach implicit design knowledge such as aesthetics and formal analysis while allowing individual creativity within the populous class. Through a combination of a step-by-step iterative design system and a real-time decentralized multi-player platform, students can work collaboratively on common digital designs. The design method is based upon building units and individualized strategies of aggregation and differentiation that are built up into larger structures. Special focus is paid to how new online platforms created for architecture education can migrate the advantages of physical intuitive design methods to a digital setting and eventually fill the gap of lacking implicit knowledge pedagogies.