Hasil untuk "Architectural drawing and design"

Tianjia Wang, Liang Zheng, Mengjiao Zhou et al.

As pioneers in the reform of higher education in China, China’s new-type universities, often referred to as the fourth generation of universities, play a crucial role in driving the iteration of educational concepts and innovation in planning and design through their campus construction. As an emerging campus type, existing research largely focuses on planning and design schemes and the static form of campus space, lacking a systematic exploration of its historical dynamic evolution and core influencing factors. This study uses Southern University of Science and Technology (SUSTech), which is a typical example of this new type of university, as a case study to analyze its spatial evolution characteristics, core driving factors, and spatial shaping mechanisms, considering the interactions among multiple stakeholders from the perspective of dynamic campus spatial development. It comprehensively utilizes literature and archive analysis, drawing and image comparison, and field research to systematically trace the entire lifecycle of SUSTech’s campus planning and construction. By combining cognitive maps and questionnaire surveys, it can explore the spatial imagery characteristics of the completed campus, analyze the key influencing factors of its spatial evolution, and propose critical thinking on related issues. It finds that SUSTech’s campus spatial form gradually took shape through a game of radical and eclectic ideas, exhibiting a dual characteristic of innovative pursuit and practical adaptation in terms of site attitude, innovative educational concepts, and planning and design concepts. Spatial evolution is the result of the combined effects of the demands of multiple stakeholders, changes in educational concepts, and the urban development context. This also reflects problems such as an imperfect consultation mechanism, inconsistent planning concepts, and insufficient functional adaptability of architectural images, which hinder the effective implementation of strategies for optimizing campus spaces in the context of China’s higher education transformation. This study reveals the inherent laws governing the dynamic evolution of new university campus spaces during the historical stage of China’s higher education transformation, providing theoretical and practical support for the planning, construction, and operational optimization of similar campuses.

Silvia Mastrolembo Ventura

The European Union (EU) has identified the digitalisation of the building permit (DBP) process as strategic to the twin digital and green transition of the construction ecosystem, enhancing transparency and collaboration along the entire value chain by a data-driven approach. In 2021, under the Horizon Europe programme, three innovation actions (DigiChecks, ACCORD, CHEK) were funded to investigate the synergy between processes, human resources, and advanced technology in DBP, providing system prototype demonstrations in operational environments. The research outlines the objectives of the three projects, contextualising them within the international literature through a bibliometric analysis, and highlighting the main emerging research topics.

Walter Didimo, Giuseppe Liotta, Giacomo Ortali et al.

A planar orthogonal drawing Γ of a connected planar graph G is a geometric representation of G such that the vertices are drawn as distinct points of the plane, the edges are drawn as chains of horizontal and vertical segments, and no two edges intersect except at common end-points. A bend of Γ is a point of an edge where a horizontal and a vertical segment meet. Drawing Γ is bend-minimum if it has the minimum number of bends over all possible planar orthogonal drawings of G. Its curve complexity is the maximum number of bends per edge. In this paper we present a linear-time algorithm for the computation of planar orthogonal drawings of 3-graphs (i.e., graphs with vertex-degree at most three), that minimizes both the total number of bends and the curve complexity. The algorithm works in the so-called variable embedding setting, that is, it can choose among the exponentially many planar embeddings of the input graph. While the time complexity of minimizing the total number of bends of a planar orthogonal drawing of a 3-graph in the variable embedding settings is a long standing, widely studied, open question, the existence of an orthogonal drawing that is optimal both in the total number of bends and in the curve complexity was previously unknown. Our result combines several graph decomposition techniques, novel data-structures, and efficient approaches to re-rooting decomposition trees.

Yahya Hassanzadeh-Nazarabadi, Sanaz Taheri-Boshrooyeh

Zero-knowledge Ethereum Virtual Machines (zkEVMs) must reconcile a fundamental contradiction: the Ethereum Virtual Machine was designed for transparent sequential execution, while zero-knowledge proofs require algebraic circuit representations. This survey provides the first systematic analysis of how existing major production zkEVM implementations resolve this tension through distinct constraint engineering strategies. We develop a comparative framework that maps the design space across three architectural dimensions. First, arithmetization schemes reveal stark trade-offs: R1CS requires compositional gadget libraries, PLONKish achieves elegance through custom gates that capture complex EVM opcodes in single constraints, while the homogeneous structure of AIR fundamentally mismatches the irregular instruction set of EVM. Second, dispatch mechanisms determine constraint activation patterns: selector-based systems waste trace width on inactive constraints, while ROM-based approaches trade memory lookups for execution flexibility. Third, the Type 1-4 spectrum quantifies an inescapable trade-off: the bit-level EVM compatibility of Type 1 demands significantly higher constraint complexity than the custom instruction sets of Type 4. Beyond cataloging implementations, we identify critical open problems across multiple domains: performance barriers preventing sub-second proving, absence of formal verification for constraint-to-EVM semantic equivalence, lack of standardized benchmarking frameworks, and architectural gaps in hybrid zkEVM/zkVM designs, decentralized prover coordination, privacy preservation, and interoperability.

Haiwen Dai, Matthew J. McDermott, Andy Paul Chen et al.

Data-driven methodologies hold the promise of revolutionizing inorganic materials discovery, but they often face challenges due to discrepancies between theoretical predictions and experimental validation. In this work, we present an end-to-end discovery framework that leverages synthesizability, oxidation state probability, and reaction pathway calculations to guide the exploration of transition metal oxide spaces. Two previously unsynthesized target compositions, ZnVO3 and YMoO3, passed preliminary computational evaluation and were considered for ultrafast synthesis. Comprehensive structural and compositional analysis confirmed the successful synthesis ZnVO3 in a partially disordered spinel structure, validated via Density Functional Theory (DFT). Exploration of YMoO3 led to YMoO3-x with elemental composition close to 1:1:3; the structure was subsequently identified to be Y4Mo4O11 through micro-electron diffraction (microED) analysis. Our framework effectively integrates multi-aspect physics-based filtration with in-depth characterization, demonstrating the feasibility of designing, testing, synthesizing, and analyzing (DTMA) novel material candidates, marking a significant advancement towards inorganic materials by design.

Ryosuke Kohita, Akira Kasuga

Cloud architecture design presents significant challenges due to the necessity of clarifying ambiguous requirements and systematically addressing complex trade-offs, especially for novice engineers with limited cloud experience. While recent advances in the use of AI tools have broadened available options, system-driven approaches that offer explicit guidance and step-by-step information management may be especially effective in supporting novices during the design process. This study qualitatively examines the experiences of 60 novice engineers using such a system-driven cloud design support tool. The findings indicate that structured and proactive system guidance helps novices engage more effectively in architectural design, especially when addressing tasks where knowledge and experience gaps are most critical. For example, participants found it easier to create initial architectures and did not need to craft prompts themselves. In addition, participants reported that the ability to simulate and compare multiple architecture options enabled them to deepen their understanding of cloud design principles and trade-offs, demonstrating the educational value of system-driven support. The study also identifies areas for improvement, including more adaptive information delivery tailored to user expertise, mechanisms for validating system outputs, and better integration with implementation workflows such as infrastructure-as-code generation and deployment guidance. Addressing these aspects can further enhance the educational and practical value of system-driven support tools for cloud architecture design.

Jingfei Huang, Alexandros Haridis

Recent advancements in multimodal Generative AI have the potential to democratize specialized architectural tasks, such as interpreting technical drawings and creating 3D CAD models, which traditionally require expert knowledge. This paper presents a comparative evaluation of two systems: GPT-4o and Claude 3.5, in the task of architectural 3D synthesis. We conduct a case study on two buildings from Palladio's Four Books of Architecture (1965): Villa Rotonda and Palazzo Porto. High-level architectural models and drawings of these buildings were prepared, inspired by Palladio's original texts and drawings. Through sequential text and image prompting, we assess the systems' abilities in (1) interpreting 2D and 3D representations of buildings from drawings, (2) encoding the buildings into a CAD software script, and (3) self-improving based on outputs. While both systems successfully generate individual parts, they struggle to accurately assemble these parts into the desired spatial relationships, with Claude 3.5 demonstrating better performance, particularly in self-correcting its output. This study contributes to ongoing research on benchmarking the strengths and weaknesses of off-the-shelf AI systems in performing intelligent human tasks that require discipline-specific knowledge. The findings highlight the potential of language-enabled AI systems to act as collaborative technical assistants in the architectural design process.

Arianna Dragoni, Alessandro Margara

Today, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate at which it changes, and the heterogeneity of formats. To address this challenge, software architects resort to build complex data-intensive architectures that integrate highly heterogeneous software systems, each offering vertically specialized functionalities. Designing a suitable architecture for the application at hand is crucial to enable high quality of service and efficient exploitation of resources. However, the design process entails a series of decisions that demand technical expertise and in-depth knowledge of individual systems and their synergies. To assist software architects in this task, this paper introduces a development methodology for data-intensive architectures, which guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application. To do so, the methodology grounds on (1) a language to precisely define an application scenario in terms of characteristics of data and requirements of stakeholders; (2) an architecture description language for data-intensive architectures; (3) a classification of systems based on the functionalities they offer and their performance trade-offs. We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners, and we validate our methodology by applying it to real-world case studies documented in literature.

Saltuk Buğra Selçuklu, Beyza Akyol, Atabek Movlyanov

This study addresses energy consumption and climate change challenges in university campus buildings, focusing on Erciyes University. The research develops a multi-objective optimization model using GAMS software and NEOS Server to enhance campus energy efficiency. The model evaluates various energy-saving measures and their investment costs. Findings indicate that building envelope insulation can reduce heating energy consumption by 35%, while efficient hot water systems and energy-saving technologies can achieve savings up to 75.5%. Model calculations using SCIP and LINDO solvers demonstrate high accuracy, with results differing by only 0.2%. This research provides valuable guidance for university decision-makers in implementing targeted interventions for significant primary energy savings.

Şefika Ergin, Ayşe Biçen Çelik, Murat Dal et al.

The durability of stone is important for traditional buildings to survive today. However, as a result of environmental and atmospheric factors, physical, chemical, biological and anthropogenic degradation occurs on stone surfaces. Detecting, classifying and taking appropriate measures against these degradations on stone surfaces is one of the factors that play a role in the survival of stone structures for a longer period. In this study, the deterioration of the facades of Kasımiye Madrasah in Mardin was analyzed. The deterioration of the building was classified and analyzed. Visual, mapping, and X-Ray Fluorescence Spectroscopy (XRF chemical analysis method) were used as analysis methods. The deterioration of the building was analyzed and classified by visual analysis. The type of deterioration and its ratio to the façade were examined using the mapping method. With the XRF analysis method on the deteriorated stone surfaces, the chemical components on the stone surface were examined and the effect causing the deterioration was examined. The study aims to determine the types and causes of deterioration in the building, to offer solutions, and to ensure that the building is transferred to future generations without losing its originality for many years in the light of this information.

Zhibin Zhou, Junnan Yu

Through systematically analyzing the literature on designing AI-based technologies, we extracted design implications and synthesized them into a generic human-centered design framework for AI technologies to better support human needs and mitigate their concerns. When adapting the framework to children's context, understanding their specific needs, behaviors, experiences, and social environments is needed. Therefore, we are working on projects to explore tailored design considerations for children, such as through investigating children's use of existing AI-based toys and learning technologies. By participating in the ACM CHI 2023 Workshop on "Child-Centred AI Design: Definition, Operation, and Considerations," we hope to learn more about how other researchers in this field approach designing child-centered AI technologies, exchange ideas on the research landscape of children and AI, and explore the possibility to develop a practical child-centered design framework of AI technologies for technology designers and developers.

Andrea Facchetti

As noted by Mouffe (2005) and Ranciére (2004), the new millennium inaugurated a post-political phase where the idea of consensus replaced that of conflict: the political, understood in its agonistic dimension, is reduced to a series of administrative procedures, in which moments of tension and conflict lose their critical potential and are resolved through a technical and rational calculation. One of the most common expressions with which the post-political phase of consensus has manifested itself, especially in the European context, is the idea of shared memory. In the last twenty years, the development of digital media has radically affected the way collective memory can be constructed, represented, narrated, and disseminated (Blom, 2016). While the intrinsic risks of the digital revolution have been widely discussed (in terms of fake news, historical revisionism, and in general post-truth), the possibility to understand and to approach historical documents and materials as living matter opens up new research trajectories and design approaches able to question the idea of shared memory, and to move closer to the image of entangled memory and mnemography (Feindt et al., 2014). This paper analyses and discusses some projects and practices in the field of infographic design and counter-forensic that could lead to a conflictual or adversarial understanding of collective memory as a contested terrain.

Andrea Tartaglia, Giovanni Castaldo, Adolfo F.L. Baratta

In the face of the challenges of effectiveness and efficiency of resource expenditure imposed by the Piano Nazionale di Ripresa e Resilienza (PNRR) and by the Next Generation EU (NGEU), the solutions introduced in Italy mainly appear to be of an emergency nature, with the adoption of extraordinary procedures and an overall “containment” of the Public Procurement Code. This essay, recognising the current scenario as an opportunity for structurally redefining methods and processes with particular attention to the dimension of the feasibility and environmental sustainability of the interventions, aims to investigate the possible contributions of the technological area for the innovation of practices and tools in the field of planning-design-construction-management of public works in Italy.

Partha S. Dey, Aditya Gopalan

Emerging applications of blockchains, such as grocery supply chains, require frequent updates to the data structure. This is in contrast with typical analyses of the Bitcoin blockchain, in which updates occur infrequently. With more frequent updates, the spread of blocks among participants in the blockchain protocol becomes complicated; thus, the structure of the blockchain data structure itself can differ significantly from the structure without the presence of network delays. In addition, emerging blockchain applications such as internet-of-things or supply chain warrant different architectures of the blockchain data structure, and so one needs a general understanding of how the data structure works rather than focusing on the specific architecture of Bitcoin. In this paper, we develop a new model to study the dynamics of the blockchain data structure in the presence of i.i.d.~network delays. Specifically, we consider an asymptotic design criterion called one-endedness, which should be satisfied by all blockchain architectures. We develop techniques to show that the one-endedness property holds for some of the leading blockchain architectures.

Andrei Paleyes, Henry B. Moss, Victor Picheny et al.

We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources. Multi-Objective Bayesian Optimisation (MOBO) is a very efficient tool for tackling expensive black-box problems. However, most MOBO algorithms are designed as purely sequential strategies, and existing batch approaches are prohibitively expensive for all but the smallest of batch sizes. We show that by encouraging batch diversity through penalising evaluations with similar predicted objective values, HIPPO is able to cheaply build large batches of informative points. Our extensive experimental validation demonstrates that HIPPO is at least as efficient as existing alternatives whilst incurring an order of magnitude lower computational overhead and scaling easily to batch sizes considerably higher than currently supported in the literature. Additionally, we demonstrate the application of HIPPO to a challenging heat exchanger design problem, stressing the real-world utility of our highly parallelisable approach to MOBO.

Chelsea Edmonds, Lawrence Paulson

Combinatorial design theory studies set systems with certain balance and symmetry properties and has applications to computer science and elsewhere. This paper presents a modular approach to formalising designs for the first time using Isabelle and assesses the usability of a locale-centric approach to formalisations of mathematical structures. We demonstrate how locales can be used to specify numerous types of designs and their hierarchy. The resulting library, which is concise and adaptable, includes formal definitions and proofs for many key properties, operations, and theorems on the construction and existence of designs.

George Philipp

In essence, a neural network is an arbitrary differentiable, parametrized function. Choosing a neural network architecture for any task is as complex as searching the space of those functions. For the last few years, 'neural architecture design' has been largely synonymous with 'neural architecture search' (NAS), i.e. brute-force, large-scale search. NAS has yielded significant gains on practical tasks. However, NAS methods end up searching for a local optimum in architecture space in a small neighborhood around architectures that often go back decades, based on CNN or LSTM. In this work, we present a different and complementary approach to architecture design, which we term 'zero-shot architecture design' (ZSAD). We develop methods that can predict, without any training, whether an architecture will achieve a relatively high test or training error on a task after training. We then go on to explain the error in terms of the architecture definition itself and develop tools for modifying the architecture based on this explanation. This confers an unprecedented level of control on the deep learning practitioner. They can make informed design decisions before the first line of code is written, even for tasks for which no prior art exists. Our first major contribution is to show that the 'degree of nonlinearity' of a neural architecture is a key causal driver behind its performance, and a primary aspect of the architecture's model complexity. We introduce the 'nonlinearity coefficient' (NLC), a scalar metric for measuring nonlinearity. Via extensive empirical study, we show that the value of the NLC in the architecture's randomly initialized state before training is a powerful predictor of test error after training and that attaining a right-sized NLC is essential for attaining an optimal test error. The NLC is also conceptually simple, well-defined for any feedforward network, easy and cheap to compute, has extensive theoretical, empirical and conceptual grounding, follows instructively from the architecture definition, and can be easily controlled via our 'nonlinearity normalization' algorithm. We argue that the NLC is the most powerful scalar statistic for architecture design specifically and neural network analysis in general. Our analysis is fueled by mean field theory, which we use to uncover the 'meta-distribution' of layers. Beyond the NLC, we uncover and flesh out a range of metrics and properties that have a significant explanatory influence on test and training error. We go on to explain the majority of the error variation across a wide range of randomly generated architectures with these metrics and properties. We compile our insights into a practical guide for architecture designers, which we argue can significantly shorten the trial-and-error phase of deep learning deployment. Our results are grounded in an experimental protocol that exceeds that of the vast majority of other deep learning studies in terms of carefulness and rigor. We study the impact of e.g. dataset, learning rate, floating-point precision, loss function, statistical estimation error and batch inter-dependency on performance and other key properties. We promote research practices that we believe can significantly accelerate progress in architecture design research.

Angel Villar-Corrales, Franziska Schirrmacher, Christian Riess

Recent advances in deep learning have led to significant improvements in single image super-resolution (SR) research. However, due to the amplification of noise during the upsampling steps, state-of-the-art methods often fail at reconstructing high-resolution images from noisy versions of their low-resolution counterparts. However, this is especially important for images from unknown cameras with unseen types of image degradation. In this work, we propose to jointly perform denoising and super-resolution. To this end, we investigate two architectural designs: "in-network" combines both tasks at feature level, while "pre-network" first performs denoising and then super-resolution. Our experiments show that both variants have specific advantages: The in-network design obtains the strongest results when the type of image corruption is aligned in the training and testing dataset, for any choice of denoiser. The pre-network design exhibits superior performance on unseen types of image corruption, which is a pathological failure case of existing super-resolution models. We hope that these findings help to enable super-resolution also in less constrained scenarios where source camera or imaging conditions are not well controlled. Source code and pretrained models are available at https://github.com/ angelvillar96/super-resolution-noisy-images.

Nevnihal Erdoğan

Taraklı, Türkiye'de Kocaeli sınırına yakın bir Sakarya ilçesidir. Modern pastoral zihniyet ve mimari koruma ilkeleri ile öne çıkan bir yerdir. Halen geleneksel mimarisi korunmuş, restore edilmiş, restorasyona hazır tipik bir Anadolu kasabası özelliğini göstermektedir. Makalenin amacı, sakin şehir sertifikasına sahip olunması için aranan standartlar, özellikle kentsel ve mimari kriterlere odaklanarak değerlendirilecektir. Yöntem olarak; Cittaslow hareket kriterlerinden geleneksel yapıların korunması kriterleri incelendi. Taraklı’nın mevcut üç geleneksel mahallesi, 18 geleneksel konutu, 8 sivil mimari örneklerinin harita ve mimari dokümanları (ölçekli rölöve ve restitüsyon projeleri, fotoğrafları, illüstrasyonları) elde edilerek görsel mimari analizler yapıldı. Taraklı’da geleneksel dokuya dair korunmuş/değerlere seçilen “geleneksel yapıların korunması” Cittaslow kriterlerine göre belirli örnek binalar üzerinden tarihi binaların korunması değerlendirilmiştir Cittaslow standartlarından mimari mirasın Taraklı’nın sahip olduğu en güçlü unsur olduğunu söylenebilir. Mimari mirasın tümünü, doğal çevreyi, yüzyıllar içinde birikmiş kültürü, ananeyi, yaşam biçimini ve yerleşmenin ruhunu kavrayabilmek; bütüncül bir korumayı ve yaşam anlayışını Taraklı’da başarılmıştır.

Damla GÜLER AKYÜZ, Banu MANAV

As suggested by literature, architecture and music has similarities. This study is planned to discuss the importance of music in space planning. The study is conducted at an experimental setting with automatic control lighting system where light levels, temperature et cetera were kept constant.  In the study, intimacy-presence, liveness-reverbe, warmth, clarity, balance, uniformity is tested. The importance of music on people and its importance on space planning is investigated. SPSS 21.0 Statistical Analysis Program is used at data analysis. Pearson Ki-Square Test is used. Test results suggested that there is a relation between sound-space interaction and space perception. Music is also affective on intimacy, clarity, texture, brilliance, space integrity et cetera. There is no relation between the gender of the participants and space perception. It can also be argued that people can feel themselves in different moods at the same setting in regard to music. This mood setting shall be in integration with the function of the space.