Architectural Design Research Articles

Promoting auditory accessibility for the Autism Spectrum Disorder (ASD) on the public transport system

The Autism Spectrum Disorder (ASD) is mainly characterized by a difficulty with neurotypical social interactions, a need for orderliness and routine, and a hypersensibility to stimuli. From all sensory stimuli, sound is proven to be most disturbing to autistic individuals. Most autistics experience severe anxiety and stress having to navigate a world that was not built for them. Therefore, this study aims to understand (quantify and qualify) the atypical response to sound - especially on ASD persons - and the neurological factors behind it. The next step on this research is reviewing literature regarding architectural design strategies for ASD. Later on, using a subway station from the city of São Paulo as study object, this paper will propose a guideline for architectural design for ASD focused on the metro system. This study will contribute to the better understanding of the Autistic Community´s sensory diversity and to a more inclusive architectural designing process.

Marnkutyi Parirna Theatre - An acoustically adaptable theatre design

Most schools will only embark on one significant arts complex in their lifetime. As with any space constructed for educational purposes, there is an appeal for accommodating a wide range of educational experiences and uses in that single space. This however often contrasts with venue needs such as the acoustic design needs to accommodate multiple uses. Quality performance venues are typically acoustically fine-tuned for a single use as differing uses can have competing requirements. This can limit performance outcomes when designing a static space, which can leave schools with theatres which are either great at one thing or mediocre at many... acoustically speaking. However, we aspired to deliver more than this for Trinity College. The role of the acoustician, theatre designer and architect became paramount to the venue's successful delivery. Achieving acoustic excellence and aesthetic beauty for a creative space requires a holistic understanding of engineering, architectural design, and artistic vision. Flexibility and adaptability were key to the venue operation, as was ease of operation. This paper presents a case study that includes the combination of music, art and science in the delivery of the 500-seat adaptable theatre known as the Marnkutyi Parirna Theatre in South Australia.

Step-Scheme SnO₂/Zn₃In₂S₆ Catalysts for Solar Production of Hydrogen Peroxide From Seawater.

Photocatalytic generation of H₂O₂, involving both oxygen reduction and water oxidation without sacrificial agents, necessitates maximized light absorption, suitable band structure, and efficient carrier transport. Leveraging the redox capacity this study designs and constructs a step-scheme heterostructured SnO₂/Zn₃In₂S₆ catalyst for H₂O₂ production from seawater under ambient conditions for the first time. This photocatalyst demonstrates a remarkable H₂O₂ production rate of 43.5µmol g⁻¹ min⁻¹ without sacrificial agents, which can be increased to 80.7µmol g⁻¹ min⁻¹ with additional O₂ injection. Extensive in situ and ex situ characterizations, supported by theoretical calculations, reveal efficient carrier transport and robust redox ability, enabling complete photosynthesis of H₂O₂ at the oxidation and reduction sites in the S-scheme SnO₂/Zn₃In₂S₆ heterojunction. Furthermore, it is hypothesized that substituting SnO₂ with other semiconductors such as TiO₂, WO₃, and BiVO₄ can all form S-scheme and the results confirm the feasibility of such catalyst design. Additionally, it demonstrates the recycling and further utilization of the H₂O₂ produced. These findings offer new insights into the design of heterostructure catalyst architectures and present new opportunities for H₂O₂ production from seawater at ambient conditions without sacrificial agents.

Four-stage cascaded adaptive sliding mode control for automatic carrier landing with airwake disturbances and uncertainties

ObjectiveThis paper addresses the carrier landing affected by airwake, parametric uncertainties, and carrier deck motion, its main target being the design of a novel sliding mode based automatic carrier landing system to obtain accurate tracking of the reference trajectory, robustness in terms of disturbances and uncertainties, as well as excellent touchdown accuracy. ApproachFor an aircraft nonlinear dynamics, written under a four-stage cascaded strict feedback form, the design of the novel landing control architecture involves the design of a guidance subsystem, robust sliding mode controllers (for the control of the heading angle, attitude angles, and angular rates), an approach power compensation system, adaptive control laws suppressing the uncertainties and disturbances, a Kalman filter for deck motion prediction, a block computing the reference trajectory, a tracking differentiator block for deck motion compensation, and first-order command filters. Main resultsThe software validation process proves the effectiveness of the sliding mode based control scheme and the suppression of the uncertainties and disturbances. Also, the comparison between the performances of the sliding mode control based carrier landing system and the ones associated to other automatic carrier landing systems shows the superiority of the sliding mode based control scheme, as well as its better touchdown accuracy and landing success rate. SignificanceThis study innovatively transforms the general carrier landing problem into a time-varying tracking control problem for cascaded strict feedback dynamics with disturbances and uncertainties. The new designed automatic carrier landing system is the first control architecture in the literature employing the sliding mode control augmented by adaptive control laws for carrier landing, subjected to airwake, deck motion, and uncertainties.

Are the mycelium-based diffusers applicable?

The use of materials based on the mycelial growth on recycled materials is widely studied. The absorbent properties of such composites can be found in a number of articles. However, this one focuses on the possibility of using mycelium-based composites for the design of sound diffusers. Diffusion parameters were determined in the form of normalized and non-normalized diffusion coefficient in a free field according to ISO 17497-2. The measured results show that the use of mycelium for the construction of diffusers is promising. However, the results are not the best, which is due to the architectural design. . For further development, the optimization of the shape of the diffusers based on acoustic requirements is expected.

Direct ink writing of engineered MOF-based hybrid composite empowering dendrite free zinc ion battery anode

The relationship between morphology and electrochemical performance of zinc-ion battery (ZIB) anodes is crucial. Optimizing zinc-ion intercalation efficiency, suppressing dendrite formation, and mitigating shape changes are achievable through thoughtful anode architecture design. Herein, we have engineered a hybrid composite by coordinating a metal-organic framework with phenolic formaldehyde resin using a direct ink writing approach to rationally design a gridline-patterned 3DP-UiON-PHC@Zn. This anode in a symmetrical cell shows exceptional stability with a low voltage hysteresis of 15 mV at 0.1 mA cm−2 over 2375 h and 67 mV at 0.5 mA cm−2 over 4470 h. When paired with a 3DP-VOC@Al cathode in a full cell, it achieves a remarkable areal capacity of 85.1 mAh cm2 (94.8 %) and maintains a capacity retention of 122 mAh g−1 (88.6 %) over 900 cycles at 0.1 A g−1. It also offers a power density of 69.1 W kg−1 and an energy density of 34.6 Wh kg−1. Kinetic analysis reveals that the charge storage mechanism is primarily capacitive (94.7 %). This study concludes that DIW significantly improves anode performance by minimizing dendrite growth and improving electrochemical stability, while the 3D-printed hybrid composite ensures robust mechanical stability and a porous structure.

Development of engineered polymeric reinforced cementitious composite (EPRC) using nature-inspired hollow architectures: Flexural experimental and numerical evaluations

This study explores the integration of nature-inspired architectural designs into Mechanics of Materials (MoM)-based reinforcement layouts for reinforced cementitious composites (RCs). While traditional MoM layouts focus on longitudinal rebars for tensile strength, this study aims to enhance the flexural properties of RCs by incorporating nature-inspired elements like hollow tubes found in plant stems. The research utilizes both experimental and numerical methods to evaluate the flexural performance of engineered polymeric reinforced cementitious composites (EPRCs) with integrated nature-inspired motifs. A numerical model, based on the material properties of reinforcing elements and the cementitious matrix, was developed to simulate the flexural performance of EPRCs reinforced with both solid and hollow bars, including MoM-based solid and hollow rebars. The model's predictions were validated through experimental testing of 3D-printed MoM-based solid and hollow rebars under three-point bending conditions. The results demonstrated that EPRCs reinforced with hollow rebars exhibited significantly improved flexural properties compared to those with solid rebars. Specifically, the hollow-reinforced samples achieved an average modulus of rupture of 8.68 MPa, toughness of 11.76 N m, and mid-point deflection of 1.99 mm, representing increases of 34 %, 55 %, and 93 %, respectively, over their solid counterparts. These enhancements are attributed to improved shear reinforcement and bond strength despite the reduced moment of inertia of the hollow rebars. The study provides valuable insights for optimizing the mechanical properties of RCs in civil engineering applications through the incorporation of nature-inspired architectural designs.

Constructing Molecular Networks on Metal Surfaces through Tellurium-Based Chalcogen-Organic Interaction.

On-surface molecular self-assembly presents an important approach to the development of low-dimensional functional nanostructures and nanomaterials. Traditional strategies primarily exploit hydrogen bonding or metal coordination, yet the potential of chalcogen bonding (ChB) for on-surface self-assemblies remains underexplored. Here, we explore fabricating molecular networks via tellurium (Te)-directed chalcogen-organic interactions. Employing carbonitrile molecules as molecular building blocks, we have achieved extended 2D networks exhibiting a 4-fold binding motif on Au(111), marking a notable difference from the conventional coordinative interaction involving transition metals. Our findings, supported by density functional theory (DFT) and scanning tunneling spectroscopy (STS), show that the Te-carbonitrile interaction exhibits lower stability compared to the metal-organic coordination, and the construction of the Te-directed molecular networks does not alter the electronic properties of the involved molecules. Introducing chalcogen-directed interactions may expand the spectrum of strategies in supramolecular assembly, contributing to the design of advanced molecular architectures for nanotechnological applications.

Translating Four-Character Structures in Chinese Literary Works on Traditional Architecture—A Case Analysis of Canal Towns South of the Yangtze and Folk Houses South of the Yangtze

Traditional Chinese architecture boasts a unique style and profound cultural connotations. Accordingly, the related literary works attain value for international dissemination. The four-character structures in these works serve as concise expressions for describing architectural details and design concepts. However, due to their linguistic implicitness, grammatical structure diversity, and rich cultural images, their translation into English requires multidimensional shift, redundancy elimination, and the avoidance of cultural defaults. This research has developed an effective translation workflow that leverages the synergistic strengths of Large Language Models (LLMs) and the human translator. By strategically employing tailored methods and techniques, it addresses the challenges in the translation process. Analysis suggests: It is recommended that the technique of shift be employed to address the implicit content and complex grammatical structures in translating four-character structures in Chinese literary works on traditional architecture into English. Besides, the selective application of combination and omission can help deal with information redundancy. Furthermore, it is advisable to apply foreignization and a combined method of literal translation and paraphrasing in conveying cultural images. These approaches will, to a certain extent, convert the untranslatability of four-character structures into translatability, enhancing the dissemination of the essence of traditional Chinese architecture.

Adaptive search for broad attention based vision transformers

Vision Transformer (ViT) has prevailed among computer vision tasks for its powerful capability of image representation recently. Frustratingly, the manual design of efficient architectures for ViTs can be laborious, often involving repetitive trial and error processes. Furthermore, the exploration of lightweight ViTs remains limited, resulting in inferior performance compared to convolutional neural networks. To tackle these challenges, we propose Adaptive Search for Broad attention based Vision Transformers, called ASB, which automates the design of efficient ViT architectures by utilizing the broad search space and an adaptive evolutionary algorithm. The broad search space facilitates the exploration of a novel connection paradigm, enabling more comprehensive integration of attention information to improve ViT performance. Additionally, an efficient adaptive evolutionary algorithm is developed to efficiently explore architectures by dynamically learning the probability distribution of candidate operators. Our experimental results demonstrate that the adaptive evolution in ASB efficiently learns excellent lightweight models, achieving a 55% improvement in convergence speed over traditional evolutionary algorithms. Moreover, the effectiveness of ASB is validated across several visual tasks. For instance, on ImageNet classification, the searched model attains a performance of 77.8% with 6.5M parameters and outperforms state-of-the-art models, including EfficientNet and EfficientViT networks. On mobile COCO panoptic segmentation, our approach delivers 43.7% PQ. On mobile ADE20K semantic segmentation, our method attains 40.9% mIoU. The code and pre-trained models will be available soon in ASB-Code.

Investigation of the sound insulation and natural ventilation performance of a metamaterial-based open window

Recent advancements in metamaterials have created new opportunities to enhance indoor environmental quality (IEQ) by integrating natural ventilation and sound insulation in architectural designs. Metamaterial-based windows can facilitate natural ventilation, contributing to energy savings while also protecting against outdoor noise. However, international standards for assessing façade sound insulation typically assume all partitions are closed, with only the Danish standard DS 447:2021 accounting for partially open conventional windows. These standards do not address simultaneous noise mitigation and natural ventilation. To fill this gap, this study evaluated a prototype metamaterial-based device called the Acoustic Metawindow (AMW) unit using three experimental methods. These methods included the diffuse field method (ISO 10140 series), an adapted directive sound field method (EN 1793-6), and beamforming to control sound leakages from the AMW unit in both closed and open states. Additionally, the study used an adapted ISO 9972 method to assess the ventilation potential alongside sound insulation of the open AMW unit. The findings revealed that the AMW unit significantly outperformed equivalently open windows, achieving a sound level difference (Dn,e) improvement of 5–15 dB in the 500–5000 Hz frequency range, resulting in a total Dn,e,w of 30 dB. The study also indicated a correlation between the frequency range of effective sound insulation and the metamaterial design features of the AMW unit. This research suggests the possibility of a more inclusive standard that considers partially open sound-insulating devices as windows in the built environment, combining noise mitigation and energy saving.

The conductor model of consciousness, our neuromorphic twins, and the human-AI deal

AbstractCritics of Artificial Intelligence (AI) posit that artificial agents cannot achieve consciousness even in principle, because they lack certain necessary pre-conditions present in biological agents. Here we highlight arguments from a neuroscientific and neuromorphic engineering perspective as to why such a strict denial of consciousness in artificial agents is not compelling. Based on the construction of a co-evolving neuromorphic twin, we argue that the differences between a developing biological and artificial brain are not fundamental and are vanishing with progress in neuromorphic architecture designs mimicking the human blueprint. To characterise this blueprint, we propose the Conductor Model of Consciousness (CMoC) that builds on neuronal implementations of an external and internal world model, while gating and labelling information flows. An extended turing test lists functional and neuronal correlates of biological consciousness that are captured by the CMoC. These correlates provide the grounding for how biological or artificial agents learn to distinguish between sensory activity generated from outside or inside of the brain, how the perception of these activities can itself be learned, and how the information flow for learning an internal world model is orchestrated by a cortical meta-instance, which we call the conductor. Perception comes with the distinction of sensory and affective components, with the affective component linking to ethical questions that are inherent in our multidimensional model of consciousness. Recognizing the existence of a blueprint for a possible artificial consciousness encompasses functional, neuronal and ethical dimensions, begging the question: How should we behave towards agents that are akin to us in the inner workings of their brains? We sketch a human-AI deal, balancing the growing cognitive abilities of artificial agents, and the possibility to relieve them from suffering of negative affects, with a protection for the rights of humans.

Algorithm and Tool Development for Creative Generation of Graphic Design of Folk Houses and Ancient Buildings Integrating Cultural and Creative Elements

This study presents an innovative approach called CNN-GA for graphic design for folk houses and ancient buildings, which integrates Convolutional Neural Networks (CNN) with Genetic Algorithms (GA) to foster the creation of culturally rich and aesthetically appealing graphic designs in architecture. Our research focuses on capturing the essence of folk houses and ancient buildings, deeply rooted in cultural heritage, and reimagining them through a modern computational lens. The CNN component of our model is trained on a diverse array of architectural imagery, enabling it to effectively recognize and categorize key elements such as motifs, textures, and structural forms inherent to various architectural styles. This neural network acts as an intelligent extractor of cultural and aesthetic features, providing a nuanced understanding of traditional architectural elements. The extracted features are then input into a GA, which embarks on an evolutionary process of design generation. This process iteratively combines and refines the architectural elements, fostering a creative exploration of design possibilities that maintain cultural integrity while introducing innovative interpretations. The synergy of CNN and GA in our CNN-GA framework allows for an automated yet insightful design process, yielding graphic designs that are not only architecturally sound but also resonate with the rich cultural narratives of folk houses and ancient buildings. This research holds significant potential in revolutionizing architectural graphic design, offering a novel tool for architects and designers to merge traditional aesthetics with contemporary design paradigms.

Ontology-Based Design Features for Representing Constructability in Architectural Design: Toward BIM in Off-Site Construction

Ontology-Based Design Features for Representing Constructability in Architectural Design: Toward BIM in Off-Site Construction

Digital Mini-Archives: Social Media Users as Curators of an Architectural Utopia

This paper explores the importance and meaning of a new concept of architectural archives – digital mini-archives in the form of social media profiles (Instagram profiles), whether they are public or private. The paper's interpretation of digital architectural archives is based on Jacques Derrida's interpretation of archives. The aim is to analyze the relationship between archivists and users in the context of social media platforms, and the structure and content of digital mini-archives. I tried to select examples of different Instagram profiles whose content (photos, videos, and quotes) relates to architectural history and architectural design presented in an innovative manner. The hypothesis emphasizes a curating role of social media users as interpreters of architectural history references and their (new) meanings, as well as curators of the future of architecture. Also, the concept of architectural utopia concerns Nathaniel Coleman's theory of utopia in architecture and its contemporary interpretations. There is a strong bond between architectural utopia and digital mini-archives which enable architecture's utopian potential because of their flexibility, openness to change, and transformability. Thus, experimental projects with utopian potential are created based on digital mini-archives. However, do these digital mini-archives contribute by deciding which (part of) architectural history ought to be forgotten or remembered in collective memory and translated to another temporal context through experimental architectural design?

Training-Free Transformer Architecture Search With Zero-Cost Proxy Guided Evolution.

Transformers have shown remarkable performance, however, their architecture design is a time-consuming process that demands expertise and trial-and-error. Thus, it is worthwhile to investigate efficient methods for automatically searching high-performance Transformers via Transformer Architecture Search (TAS). In order to improve the search efficiency, training-free proxy based methods have been widely adopted in Neural Architecture Search (NAS). Whereas, these proxies have been found to be inadequate in generalizing well to Transformer search spaces, as confirmed by several studies and our own experiments. This paper presents an effective scheme for TAS called TRansformer Architecture search with ZerO-cost pRoxy guided evolution (T-Razor) that achieves exceptional efficiency. First, through theoretical analysis, we discover that the synaptic diversity of multi-head self-attention (MSA) and the saliency of multi-layer perceptron (MLP) are correlated with the performance of corresponding Transformers. The properties of synaptic diversity and synaptic saliency motivate us to introduce the ranks of synaptic diversity and saliency that denoted as DSS++ for evaluating and ranking Transformers. DSS++ incorporates correlation information among sampled Transformers to provide unified scores for both synaptic diversity and synaptic saliency. We then propose a block-wise evolution search guided by DSS++ to find optimal Transformers. DSS++ determines the positions for mutation and crossover, enhancing the exploration ability. Experimental results demonstrate that our T-Razor performs competitively against the state-of-the-art manually or automatically designed Transformer architectures across four popular Transformer search spaces. Significantly, T-Razor improves the searching efficiency across different Transformer search spaces, e.g., reducing required GPU days from more than 24 to less than 0.4 and outperforming existing zero-cost approaches. We also apply T-Razor to the BERT search space and find that the searched Transformers achieve competitive GLUE results on several Neural Language Processing (NLP) datasets. This work provides insights into training-free TAS, revealing the usefulness of evaluating Transformers based on the properties of their different blocks.

Surface quality prediction in-situ monitoring system: A deep transfer learning-based regression approach with audible signal

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

Sustainable Design Methods Translated from the Thermodynamic Theory of Vernacular Architecture: Atrium Prototypes

In the context of China’s sustainable development and dual carbon goals, research on thermodynamic architecture theory and vernacular architecture increasingly aligns with international trends, developing distinct characteristics. This research addresses the challenge of rapid changes in the built environment by focusing on climate adaptability and passive technologies. However, the development of thermodynamic theory in vernacular architecture faced technical limitations in the early 21st century and was later overshadowed by the industry’s reliance on active technologies to meet green building standards, resulting in a reduced role for architects in the green building field. This article traces the origins of passive architecture, rooted in vernacular architecture, and applies thermodynamic theory to explore architectural prototypes. It examines the theoretical feasibility of architectural design in achieving low-carbon and sustainable goals, aiming to fill a gap in thermodynamic theory within the broader context of sustainable architectural development. After demonstrating the various passive prototypes inherent in vernacular architecture, this paper proposes a courtyard prototype focused on residential comfort for design translation and analysis. The research methods employed include bioclimatic charting, balance point temperature analysis in time series, and extensive computer simulations. Through the process of prototype extraction, performance analysis, validation, and optimization, the paper systematically discusses sustainable design methods within the framework of thermodynamic architecture theory. It also provides practical demonstrations of these methods across four distinct climate regions in China. By translating vernacular architectural designs, this research systematically organizes the theoretical framework for architects’ early involvement in low-carbon and green building design, offering a theoretical foundation for initiating the design process through prototype translation while guiding the generation of green ecological buildings.

Architecture designing of digital twin in a healthcare unit.

Objectives: This study proposes a novel architecture for designing digital twins in healthcare units. Methods: A systematic research methodology was employed to develop architecture design patterns. In particular, a systematic literature review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to answer specific research questions and provide guidelines for designing the architecture. Subsequently, a case study was designed and analyzed at a chemotherapy treatment center for outpatients. Results: System architecture knowledge was distilled from this real-world case study, supplemented by existing software and systems design patterns. A novel five-layer architecture for digital twins in healthcare units was proposed with a focus on the security and privacy of patients' information. Conclusion: The proposed digital twin architecture for healthcare units offers a comprehensive solution that provides modularity, scalability, security, and interoperability. The architecture provides a robust framework for effectively and efficiently managing healthcare environments.

Addressing atmospheric absorption in adaptive rectangular decomposition.

This paper focuses on the adaptive rectangular decomposition (ARD) scheme, a wave-based method employed for acoustic simulations. ARD holds promise for diverse applications. In architectural design, it can forecast acoustical parameters, facilitating the creation of spaces with superior sound quality. Moreover, in the domain of acoustic virtual reality, ARD can offer users a more immersive and lifelike acoustic environment. This enhancement proves advantageous for all of these applications, enabling the simulation of larger environments with heightened precision. Despite its notable efficiency, ARD faces a significant drawback: the absence of atmospheric absorption modeling. The principal aim of this study is to rectify this limitation, thereby augmenting the capabilities of the ARD algorithm.