Hasil untuk "Architectural drawing and design"

B. Bratton

R. Withagen, H. J. Poel, D. Araújo et al.

Mohammed Fawzi Ragheb, Rowaida Rashed, Ayat Ismail et al.

<p class="p1"><span lang="EN-US">This paper discusses the growing role of live projects as experiential learning practices in architectural education. It aims to explore how live projects are conceptualized, implemented, and integrated into design curricula across diverse contexts. Employing the PRISMA 2020 framework, the study systematically reviews literature from major academic databases. Three key findings emerge: live projects enhance student learning by linking theory with practice, promote social engagement through community collaboration, and support interdisciplinary, context-responsive education. Limitations include a lack of longitudinal impact studies and inconsistencies in terminology and assessment. Drawing from the discussion and conclusion, the paper contributes a synthesized understanding of live project pedagogy, identifies operational and pedagogical challenges, and highlights their potential to reshape architectural education. It also calls for more rigorous research, sustainable institutional support, and broader adoption of live projects as a transformative model that aligns academic learning with professional and societal responsibilities.</span></p><p class="p1"><span lang="EN-US"><br /></span></p><p class="p1"><strong><span lang="EN-US">Received on, 04 April 2025 </span></strong></p><p class="p1"><strong><span lang="EN-US">Accepted on, 27 </span>April 2025 </strong></p><p class="p1"><strong>Published on, 08 May 2025</strong></p>

Shuangzhi TIAN, Ming YU, Wenxiao LI et al.

ObjectiveThe intensification of climate change has led to a significant escalation in flood risk within shallow mountainous areas, posing a severe threat to human life, health, and ecological security. These transitional areas, often situated at the interface between mountainous terrain and urbanized plains, are uniquely vulnerable to the hydrological impacts of extreme precipitation. Existing research has established that green infrastructure (GI), through its influence on fundamental hydrological processes such as the rainfall – runoff and runoff – sediment relationships, can play a pivotal role in stormwater management. However, the current body of literature predominantly focuses on two main scales: the effectiveness of individual GI elements at the localized plot level and the impact of the broader green space matrix at the large basin scale. Consequently, a critical knowledge gap persists concerning the influence of the spatial configuration of GI patches — such as their shape, size, and degree of fragmentation — on hydrological responses at the finer, sub-basin scale, which is the most relevant scale for understanding flood generation. Clarifying the mechanisms through which GI spatial patterns affect mountainous stormwater runoff and subsequently optimizing these patterns are crucial steps toward enhancing the flood prevention and control capabilities of shallow mountainous areas. This research aims to bridge the knowledge gap by elucidating these mechanisms and developing an optimization framework to mitigate the adverse effects of extreme rainfall in the sensitive shallow mountainous areas.MethodsThis research adopts a two-stage research framework, comprising the two stages of mechanism exploration and pattern optimization. In the stage of exploration of hydrological mechanisms, two sample basins are selected within the shallow mountainous area of Beijing and, based on historical meteorological data and land cover data, the SWAT (soil and water assessment tool) model is used to simulate runoff generation in mountainous sub-basins with high spatiotemporal resolution. Meanwhile, machine learning methods, specifically an XGBoost-based model, are applied to the sample data to construct a high-accuracy predictive model for stormwater runoff generation, with a focus on GI spatial pattern characteristics as predictor variables. To interpret the machine learning results, the SHAP (SHapley Additive exPlanations) framework is employed to quantitatively elucidate the impact mechanisms of various GI spatial pattern metrics on mountainous stormwater runoff. In the pattern optimization stage, key GI spatial metrics are identified as optimization variables based on their hydrological influence. Under a dual-objective framework emphasizing both cost-effectiveness and flood mitigation efficacy, the NSGA-Ⅱ (nondominated sorting genetic algorithm Ⅱ) is used to optimize GI configuration for a representative shallow mountainous area. The effectiveness of these optimizations in reducing flood risks is validated through extreme historical rainfall scenarios.ResultsThe resulting predictive model for mountainous runoff generation demonstrates excellent simulation and forecasting capabilities, especially in modeling the influence of GI spatial pattern changes on runoff processes in complex mountainous terrains. The interpretive analysis using SHAP on the trained model provides crucial insights into the underlying mechanisms. Among the numerous GI landscape metrics evaluated, two features emerge as the most critical drivers positively correlated with increased mountainous stormwater runoff: the patch density (PD) of closed-canopy deciduous broad-leaved forests and the percent of landscape (PLAND) occupied by grasslands. The analysis reveals that an increase in either of the aforesaid two metrics consistently contributes to higher predicted runoff volumes. In contrast, the spatial pattern characteristics of other vegetation types, such as closed-canopy evergreen coniferous forests and closed-canopy deciduous coniferous forests, are found with a comparatively weak and less significant influence on the hydrological response. During the multi-objective pattern optimization process, using the two most influential metrics (PD and PLAND) as adjustable variables for a typical area, the optimized spatial pattern is able to reduce flood risk by 13.5% under the scenario of once-in-a-century extreme rainfall.ConclusionThe XGBoost machine learning model displays outstanding applicability for flood risk assessment and hydrological scenario simulation in shallow mountainous areas. An in-depth analysis of the GI spatial metrics identified by SHAP interpretation suggests that the fragmentation resulting from increased PD of closed-canopy deciduous broad-leaved forests, together with the impact of grassland PLAND on the runoff coefficient, are the core driving factors of stormwater runoff generation in these mountainous contexts. Additionally, the shape and configuration of grassland patches may further promote stormwater runoff. Accordingly, in the process of optimizing GI spatial arrangements in shallow mountainous areas, enhancing the connectivity of closed-canopy deciduous broad-leaved forest while reducing the size of large grassland patches is found conducive to forming optimal GI layouts that reduce flood risk under extreme precipitation. Through the application of interpretable machine learning techniques, this research reveals the underlying mechanisms by which different GI spatial pattern metrics influence mountain runoff generation and, based on these findings, effectively reduces regional flood risk during extreme rainfall events. The methodological approach and practical guidance provided by this research offer robust technical support for flood-mitigating green space planning in similar shallow mountain terrains and contribute valuable experience for regional adaptation to intensified climate-driven stormwater challenges.

Nicholas Davis, Janet Rafner

This paper describes the AI Drawing Partner, which is a co-creative drawing agent that also serves as a research platform to model co-creation. The AI Drawing Partner is an early example of a quantified co-creative AI system that automatically models the co-creation that happens on the system. The method the system uses to capture this data is based on a new cognitive science framework called co-creative sense-making (CCSM). The CCSM is based on the cognitive theory of enaction, which describes how meaning emerges through interaction with the environment and other people in that environment in a process of sense-making. The CCSM quantifies elements of interaction dynamics to identify sense-making patterns and interaction trends. This paper describes a new technique for modeling the interaction and collaboration dynamics of co-creative AI systems with the co-creative sense-making (CCSM) framework. A case study is conducted of ten co-creative drawing sessions between a human user and the co-creative agent. The analysis includes showing the artworks produced, the quantified data from the AI Drawing Partner, the curves describing interaction dynamics, and a visualization of interaction trend sequences. The primary contribution of this paper is presenting the AI Drawing Partner, which is a unique co-creative AI system and research platform that collaborates with the user in addition to quantifying, modeling, and visualizing the co-creative process using the CCSM framework.

Yiran Zhang, Ruiyin Li, Peng Liang et al.

Architecture design is a critical step in software development. However, creating a high-quality architecture is often costly due to the significant need for human expertise and manual effort. Recently, agents built upon Large Language Models (LLMs) have achieved remarkable success in various software engineering tasks. Despite this progress, the use of agents to automate the architecture design process remains largely unexplored. To address this gap, we envision a Knowledge-based Multi-Agent Architecture Design (MAAD) framework. MAAD uses agents to simulate human roles in the traditional software architecture design process, thereby automating the design process. To empower these agents, MAAD incorporates knowledge extracted from three key sources: 1) existing system designs, 2) authoritative literature, and 3) architecture experts. By envisioning the MAAD framework, we aim to advance the full automation of application-level system development.

Jinchen He, Xiao-Yong Jin, James C. Osborn et al.

Critical slowing down, where autocorrelation grows rapidly near the continuum limit due to Hybrid Monte Carlo (HMC) moving through configuration space inefficiently, still challenges lattice gauge theory simulations. Combining neural field transformations with HMC (NTHMC) can reshape the energy landscape and accelerate sampling, but the choice of neural architectures has yet to be studied systematically. We evaluate NTHMC on a two-dimensional U(1) gauge theory, analyzing how it scales and transfers to larger volumes and smaller lattice spacing. Controlled comparisons let us isolate architectural contributions to sampling efficiency. Good designs can reduce autocorrelation and boost topological tunneling while maintaining favorable scaling. More broadly, our study highlights emerging design guides, such as wider receptive fields and channel-dependent activations, paving the way for systematic extensions to four-dimensional SU(3) gauge theory.

Sucharit Sarkar

In this short note, we exhibit a draw in the game of Philosopher's Phutball. We construct a position on a 12 x 10 Phutball board from where either player has a drawing strategy, and then generalize it to an m x n board with m-2 >= n >= 10.

Weida Zhai, Dongwang Tao, Yuequan Bao

Ruoxi Wang, Xueting Zhao, N. Jia et al.

Superwetting membranes with opposite wettability to oil and water are drawing intense attention in recent years for oil/water separation. Superhydrophilic and underwater superoleophobic membranes have shown unique advantages in the efficient treatment of oily wastewater containing oil-in-water emulsions. Facile interfacial engineering and microstructural design of the hierarchical architectures and the hydrophilic chemistry is of significance but still challenging. In this study, a hydrophilic hierarchical hybrid layer derived from the metal-phenolic networks (MPNs)/metal-organic framework (MOFs) synergy is constructed on membrane surface via a proposed coordination-directed alternating assembly strategy. The assembly of the MPN multilayers provides hydrophilic chemical basis, and the assembly of MOF nanocrystals provides hierarchical structural basis. Notably, the coordination interfacial interaction enables the formation of well-defined hydrophilic hierarchical architectures. The obtained membrane is thus endowed with robust superhydrophilicity, underwater superoleophobicity and anti-oil-adhesion capability, which make it capable to highly efficient oil-water separation with high water permeance (above 6300 L/m2h), high oil rejection (above 99.4%) and recyclable antifouling property. The high-performance of the developed superwetting membrane makes it a competitive candidate towards oil/water separation. Additionally, the demonstrated MPN/MOF assembly strategy may offer new prospects for the facile and versatile design of other superwetting materials.

Antonio Magarò, Massimo Mariani, Luca Trulli

The paper presents the results of a research carried out by the Department of Architecture of Roma Tre University about user-driven re-design of public spaces, with the aim of transforming them into accessible and inclusive public spaces. Activities are related to the case of Schuster Park in Rome, whose re-functionalisation in terms of accessibility and inclusiveness is currently being planned. With this target, the research team, during the support phase for a preliminary feasibility study, experimented with a recursive participatory model, which can also be applied to other contexts related to public spaces.

Amany Saker

Amidst rapid urbanization and escalating environmental concerns, exploring biomimicry and sustainable future architecture has gained significant prominence. This research focuses on elucidating the pivotal role of biomimicry as a transformative design approach for constructing ecologically responsible and energy-efficient architecture. The study initiates a comprehensive exploration of biomimicry as a design philosophy, drawing inspiration from nature's proven solutions. It delves into integrating biomimetic principles into architectural concepts, emphasizing their contribution to shaping a sustainable built environment. Through a detailed analysis, the Eden Project serves as a compelling biomimicry case study, illustrating how this approach addresses diverse challenges architects face. The research concludes by advocating a profound paradigm shift in conceiving and constructing future architecture. By embracing the holistic concept of biomimicry, this approach offers a promising avenue for creating architecture that seamlessly coexists with the natural world, ensuring energy efficiency, thermal comfort, functionality, and resilience for its inhabitants. في ظل التطور الحضري السريع والمخاوف البيئية المتصاعدة، اكتسب استكشاف التقليد البيولوجي والعمارة المستدامة المستقبلية أهمية كبيرة. تركز هذه البحث على توضيح الدور الحيوي للتقليد البيولوجي كنهج تصميمي محوري لإنشاء عمارة مسؤولة بيئيًا وفعالة من حيث الطاقة. يبدأ الدراسة استكشافًا شاملاً للتقليد البيولوجي كفلسفة تصميمية، مستلهمًا من الحلول المثبتة من قبل الطبيعة. وتتناول الدراسة دمج مبادئ التقليد البيولوجي في مفاهيم العمارة، مؤكدة إسهامها في تشكيل بيئة بنائية مستدامة. من خلال تحليل مفصل، يعتبر مشروع إيدين (Eden Project) دراسة حالة متقنة للتقليد البيولوجي، موضحًا كيفية معالجة هذا النهج للتحديات المتنوعة التي يواجهها المهندسون المعماريون. تختتم البحث بالدعوة إلى تحول نمطي عميق في تصور وإنشاء العمارة المستقبلية. من خلال تبني مفهوم التقليد البيولوجي الشامل، يقدم هذا النهج مساراً واعداً لخلق عمارة تتعايش بسلاسة مع العالم الطبيعي، مضمونًا ليس فقط كفاءة الطاقة والراحة الحرارية، ولكن أيضًا الوظيفية والمرونة لسكانها.

Akshat Ramachandran, Zishen Wan, Geonhwa Jeong et al.

Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training. In this study, we introduce Logarithmic Posits (LP), an adaptive, hardware-friendly data type inspired by posits that dynamically adapts to DNN weight/activation distributions by parameterizing LP bit fields. We also develop a novel genetic-algorithm based framework, LP Quantization (LPQ), to find optimal layer-wise LP parameters while reducing representational divergence between quantized and full-precision models through a novel global-local contrastive objective. Additionally, we design a unified mixed-precision LP accelerator (LPA) architecture comprising of processing elements (PEs) incorporating LP in the computational datapath. Our algorithm-hardware co-design demonstrates on average <1% drop in top-1 accuracy across various CNN and ViT models. It also achieves ~ 2x improvements in performance per unit area and 2.2x gains in energy efficiency compared to state-of-the-art quantization accelerators using different data types.

Petr Hliněný, Lili Ködmön

The k-planar graphs, which are (usually with small values of k such as 1, 2, 3) subject to recent intense research, admit a drawing in which edges are allowed to cross, but each one edge is allowed to carry at most k crossings. In recently introduced [Binucci et al., GD 2023] min-k-planar drawings of graphs, edges may possibly carry more than k crossings, but in any two crossing edges, at least one of the two must have at most k crossings. In both concepts, one may consider general drawings or a popular restricted concept of drawings called simple. In a simple drawing, every two edges are allowed to cross at most once, and any two edges which share a vertex are forbidden to cross. While, regarding the former concept, it is for k<=3 known (but perhaps not widely known) that every general k-planar graph admits a simple k-planar drawing and this ceases to be true for any k>=4, the difference between general and simple drawings in the latter concept is more striking. We prove that there exist graphs with a min-2-planar drawing, or with a min-3-planar drawing avoiding crossings of adjacent edges, which have no simple min-k-planar drawings for arbitrarily large fixed k.

Oswin Aichholzer, Joachim Orthaber, Birgit Vogtenhuber

Generalizing pseudospherical drawings, we introduce a new class of simple drawings, which we call separable drawings. In a separable drawing, every edge can be closed to a simple curve that intersects each other edge at most once. Curves of different edges might interact arbitrarily. Most notably, we show that (1) every separable drawing of any graph on $n$ vertices in the plane can be extended to a simple drawing of the complete graph $K_{n}$, (2) every separable drawing of $K_{n}$ contains a crossing-free Hamiltonian cycle and is plane Hamiltonian connected, and (3) every generalized convex drawing and every 2-page book drawing is separable. Further, the class of separable drawings is a proper superclass of the union of generalized convex and 2-page book drawings. Hence, our results on plane Hamiltonicity extend recent work on generalized convex drawings by Bergold et al. (SoCG 2024).

Kanga Marius N’Gatta, H. Belaid, Joelle El Hayek et al.

Cellulose nanocrystals (CNC) are drawing increasing attention in the fields of biomedicine and healthcare owing to their durability, biocompatibility, biodegradability and excellent mechanical properties. Herein, we fabricated using fused deposition modelling technology 3D composite scaffolds from polylactic acid (PLA) and CNC extracted from Ficus thonningii. Scanning electron microscopy revealed that the printed scaffolds exhibit interconnected pores with an estimated average pore size of approximately 400 µm. Incorporating 3% (w/w) of CNC into the composite improved PLA mechanical properties (Young's modulus increased by ~ 30%) and wettability (water contact angle decreased by ~ 17%). The mineralization process of printed scaffolds using simulated body fluid was validated and nucleation of hydroxyapatite confirmed. Additionally, cytocompatibility tests revealed that PLA and CNC-based PLA scaffolds are non-toxic and compatible with bone cells. Our design, based on rapid 3D printing of PLA/CNC composites, combines the ability to control the architecture and provide improved mechanical and biological properties of the scaffolds, which opens perspectives for applications in bone tissue engineering and in regenerative medicine.

Bin Li, Zesong Fei, Yan Zhang et al.

Wireless communications can leverage UAVs to provide ubiquitous connectivity to different device types. Recently, integrating UAVs into a macro cell network is drawing unprecedented interest for supplementing terrestrial cellular networks. Compared with communications with fixed infrastructure, a UAV has salient attributes, such as easy-to-deploy, higher capacity due to dominant LoS communication links, and additional design degree-of-freedom with the controlled mobility. While UAV communication offers numerous benefits, it also faces security challenges due to the broadcasting nature of the wireless medium. Thus, information security is one of the fundamental requirements. In this article, we first consider two application cases of UAVs (i.e., a UAV as a flying base station and a UAV as an aerial node) in conjunction with safeguarding the exchange of confidential messages. Then, we demonstrate physical layer security mechanisms via two case studies to ensure security, and numerically show superior performance gains. Finally, we shed light on new opportunities in the emerging network architecture that can serve as a guide for future research directions.

Jiaben Chen, Renrui Zhang, Dongze Lian et al.

Current audiovisual separation methods share a standard architecture design where an audio encoder-decoder network is fused with visual encoding features at the encoder bottleneck. This design confounds the learning of multimodal feature encoding with robust sound decoding for audio separation. To generalize to a new instrument, one must finetune the entire visual and audio network for all musical instruments. We re-formulate the visual-sound separation task and propose Instruments as Queries (iQuery) with a flexible query expansion mechanism. Our approach ensures cross-modal consistency and cross-instrument disentanglement. We utilize “visually named” queries to initiate the learning of audio queries and use cross-modal attention to remove potential sound source interference at the estimated waveforms. To generalize to a new instrument or event class, drawing inspiration from the text-prompt design, we insert additional queries as audio prompts while freezing the attention mechanism. Experimental results on three benchmarks demonstrate that our iQuery improves audiovisual sound source separation performance. Code is available at https://github.com/JiabenChen/iQuery.

Mary Frances Teresa Rodríguez Van Gort, Oscar Rivera

Los fenómenos naturales aquejan diferencialmente a los asentamientos humanos, afectando especialmente a los sectores más vulnerables. Es importante evidenciar diversas problemáticas que existen en múltiples zonas de la Ciudad de México (CDMX), puntualizando que ciertas características geomorfológicas potencian deslizamientos de tierra, que perjudican año tras año a la población. El cambio antrópico existente en áreas naturales que han sido modificadas con el paso de los años, causa efectos adversos según la pendiente, al establecer nula filtración debido a los materiales de construcción utilizados. Esta cuestión fomenta diversos deslizamientos de tierra. En ese sentido, la incidencia en áreas de riesgo donde se insertan tipologías residenciales debe ser atendido con base en la seguridad que existe en el entorno del espacio geográfico. Por lo anterior, se elaboró un modelo preventivo, estableciendo parámetros cuantitativos de vulnerabilidad según afectaciones en la vivienda con el objetivo de alertar y acompañar a la población para establecer una posible reubicación o alerta temprana, ante posibles eventos de deslizamiento de tierra debido a precipitaciones extraordinarias.

Ángel J. Fernández-Álvarez, Vicente López-Chao

In recent years, architecture has undergone a significant transformation with the introduction of digital tools and techniques. Three prominent techniques in this context are drawing, scripting, and prompting, offering architects novel approaches to the design process. Drawing remains a vital tool for idea exploration and design development. Conversely, scripting and prompting are digital methods that enable a more algorithmic and data-driven design approach. This paper explores the intersection of these techniques and their potential to enhance the design process. By using them together, architects can foster creativity, efficiency, and innovation while allowing more time for conceptual development. The paper analyzes the framework posed by these techniques, including the impact of new digital technologies like generative and algorithmic design, and artificial intelligence. The potential of scripting to automate graphic tasks, such as parametric design and generative drawing, is addressed. Furthermore, the paper delves into the new frontier of prompting and its application in architectural graphics, outlining the competencies architects need to effectively utilize this technology. In conclusion, this paper critically evaluates the current state of architectural graphics, exploring the fusion of traditional and digital methods, and how new technologies can transform the design process.