Optimal Architecture Design Research Articles

Climate and performance-driven architectural floorplan optimization using deep graph networks

PurposeThis study introduces a novel approach to generating and optimizing energy-efficient and climate-responsive architectural floorplans.Design/methodology/approachThe DGraph-cGAN model utilizes advanced deep-learning techniques to produce diverse, realistic layouts that meet specific design constraints and functional requirements.FindingsThe results show significant energy savings (32.1% overall) across different building types and climate conditions, with reductions in energy use intensity, CO2 emissions and annual energy costs. Case studies demonstrate notable improvements in energy savings, CO2 emission reduction, daylight autonomy, thermal comfort and cost savings.Practical implicationsThe DGraph-cGAN model has great potential for advancing architectural design optimization, with opportunities for further refinement and application in various contexts.Originality/valueThis study contributes to developing a novel approach to optimizing architectural floorplans using deep learning techniques. It provides a valuable tool for architects and designers to create energy-efficient, climate-responsive buildings.

Integration of Engineering Optimization in Architectural Design

Integration of Engineering Optimization in Architectural Design

The Impact of Using Advanced Technologies in Sustainable Design to Enhance Usability and Achieve Optimal Architectural Design

The Impact of Using Advanced Technologies in Sustainable Design to Enhance Usability and Achieve Optimal Architectural Design

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Advancing AutoML: A Deep Dive into Neural Architecture Search (NAS) and Its Optimization Techniques

Abstract: Neural Architecture Search (NAS) is a pivotal technique in the field of automated machine learning (AutoML), enabling the automatic design of optimal neural network architectures. As deep learning models grow in complexity, NAS offers a scalable approach to improving model performance by exploring vast search spaces of potential architectures. In our research, we investigate the mathematical foundations and algorithms underpinning NAS, focusing on reinforcement learning-based, evolutionary, and gradient-based approaches. We provide mathematical proofs of convergence and efficiency for each method and analyze real-world applications, such as image classification and natural language processing (NLP). Through a comprehensive exploration of NAS, we aim to highlight its impact on AutoML and its potential to automate neural network design effectively while addressing challenges in computational cost and generalization.

Mechanics of Materials: Multiscale Design of Advanced Materials and Structures

Materials can now be designed and architectured like structural components for targeted mechanical and physical properties. Structures and microstructures should not be studied independently and their design will benefit from a multiscale approach combining nonlinear continuum mechanics approaches and physical descriptions of elasticity, viscoplasticity, phase transformations and damage of microstructures, at various scales. The aim of the workshop was to gather outstanding junior and senior researchers in the various branches of mathematics, physics and engineering sciences suited to address the question of design of materials and structures by means of multiscale discrete and continuum approaches to their constitutive behavior. Examples include atomic or macroscopic lattices, random or periodic cellular materials, smart materials like shape memory alloys, 3D woven composites, acoustic and electromagnetic metamaterials, etc. Modern continuum mechanics relies on sophisticated constitutive laws for anisotropic materials exhibiting elastoviscoplastic behavior, still a field of intense research with new mathematical concepts. In particular size-dependent properties are addressed by resorting to generalized continua such as gradient or micromorphic and phase field models. The latter are attractive for the simulation of microstructure evolution coupled with mechanics, due to thermodynamic and metallurgical processes and damage. Scale transition and homogenization methods for continuous and discrete systems are required for the determination of effective material and structural behavior. Metamaterials are architectured materials specifically designed to achieve certain propagation and dispersion properties of elastic and plastic waves. Optimization strategies for the design of optimal architectures are involved in the design process. Target functions for optimization are now based on multicriteria (stiffness, strength, thermal expansion, transport properties, anisotropy etc.).

Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology

Recent advancements in deep learning have popularized Generative Adversarial Networks for image generation. This study investigates integrating Generative Adversarial Networks technology into architectural design to empower architects in creating diverse, innovative, and practical designs. By analyzing architectural research, deep learning theory, and practical Generative Adversarial Networks applications, we substantiate the feasibility of using Generative Adversarial Networks for architectural design optimization. The generated architectural images exhibit significant diversity, innovation, and practicality, inspiring architects with numerous design possibilities. Overall, Generative Adversarial Networks technology not only expands design methodologies but also stimulates groundbreaking innovation in architectural practice. As technology progresses, Generative Adversarial Networks-based architectural design optimization shows promising potential for widespread adoption, heralding a new era of creativity and efficiency in architecture.

Parametric Design Method of Buildings Based on Convolutional and Deep Neural Networks

The application of AI technology in architectural software has brought various innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them, and analyzes the application of these technologies in architectural image recognition, generation, design optimization and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design. The application of AI technology in architectural software has brought numerous innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them; it also analyzes the application of these technologies in architectural image recognition, generation, design optimization, and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design.

Research on the Architectural Design and Performance Optimization of Large Scale Real-Time Chat Systems

With the rapid development of internet technology, large-scale real-time chat systems have become an essential tool for connecting users worldwide. These systems not only support the core functions of social communication but also serve as key platforms for business, education, and remote collaboration. However, the surge in user numbers and the explosive growth in message volume pose unprecedented technical challenges to real-time chat systems, including high concurrency processing, low latency communication, data consistency assurance, and system scalability. This study aims to explore and implement an efficient, stable, and scalable architecture for large-scale real-time chat systems and propose corresponding performance optimization strategies. The research employs literature review, system analysis, prototype design, and performance evaluation methods. It first analyzes the design limitations and performance bottlenecks of existing real-time chat systems. Based on this analysis, a microservices-based system design scheme is proposed, which enhances system performance and reliability through componentized services, message queues, load balancing, and caching mechanisms. In terms of performance optimization, this paper focuses on key technologies such as load balancing algorithms, asynchronous processing mechanisms of message queues, caching strategies, and data storage optimization. Experimental evaluations have confirmed the significant effects of the proposed scheme in handling high-concurrency requests, reducing system latency, and improving data throughput.

Optimization-based design exploration of building massing typologies—EvoMass and a typology-oriented computational design optimization method for early-stage performance-based building massing design

In the past decade, there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration. However, it remains challenging for designers to apply such optimization-based design explorations in practice. To address this issue, this paper introduces a design tool, called EvoMass, and an associated design method that facilitates design exploration for building massing typologies in performance-based design tasks. EvoMass is capable of offering architects design options reflecting performance-related building massing typologies for the design task, without necessitating advanced computational design skills. More importantly, it can provide architects with insights into the underlying performance implications, thereby enhancing early-stage performance-based design exploration. EvoMass and its associated design method overcome the limitation in the conventional typology-first-optimization-second design procedure adopted by most existing tools, and it promotes a typology-oriented design exploration method of using computational optimization in performance-based architectural design. To demonstrate the efficacy of EvoMass, case studies derived from architectural design studio tasks, incorporating daylighting, solar exposure, and subjective design intents, and the result of a user survey are presented, which highlights how EvoMass and the performance-based design optimization and exploration can enable architects to achieve a more performance-aware design.

Visual analysis of fitness landscapes in architectural design optimization

In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.

Using automated design appraisal to model building-specific devaluation risk due to land-use change

Uncertainty of spillover effects – including property devaluation - from proposed land-use change elicits opposition to local development. This hinders cities’ ability to implement land-use policy aimed at housing affordability and environmental sustainability. The uncertainty arises from the limitation of current methods to quantify both the physical exposure and financial sensitivity to changes in urban environmental characteristics and amenities. Hence, this paper introduces an integrated approach to quantify the direct and indirect effects of a proposed urban development, using parametric modeling, building performance simulation and automated design appraisal (ADA). ADA, a novel aspect of this study, predicts the market price of a generated building design concept within a local urban context. To demonstrate its practical utility, this paper examines property value at risk due to nearby development associated with changes to visual landscape quality. The results shed light on the importance and spatial variability of the price-amenity gradient, identifying properties that are financially at risk to the visual impacts of local development. Beyond building-specific risk modeling for the development planning stage, this work serves as a blueprint for architectural design optimization procedures, in which economic performance is evaluated based on learned preferences derived from financial market data.

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

Optimized model architectures for deep learning on genomic data

The success of deep learning in various applications depends on task-specific architecture design choices, including the types, hyperparameters, and number of layers. In computational biology, there is no consensus on the optimal architecture design, and decisions are often made using insights from more well-established fields such as computer vision. These may not consider the domain-specific characteristics of genome sequences, potentially limiting performance. Here, we present GenomeNet-Architect, a neural architecture design framework that automatically optimizes deep learning models for genome sequence data. It optimizes the overall layout of the architecture, with a search space specifically designed for genomics. Additionally, it optimizes hyperparameters of individual layers and the model training procedure. On a viral classification task, GenomeNet-Architect reduced the read-level misclassification rate by 19%, with 67% faster inference and 83% fewer parameters, and achieved similar contig-level accuracy with ~100 times fewer parameters compared to the best-performing deep learning baselines.

Realtime optimization and management system (ROAM): A decision support system for digital agriculture systems

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

Architectural design and robust optimization of adaptive hydraulic engine mount

Architectural design and robust optimization of adaptive hydraulic engine mount

Retracted: Architectural Interior Design and Space Layout Optimization Method Based on VR and 5G Technology

Retracted: Architectural Interior Design and Space Layout Optimization Method Based on VR and 5G Technology

МЕТАОБУЧЕНИЕ ДЛЯ АВТОМАТИЧЕСКОГО ПРОЕКТИРОВАНИЯ ОПТИМАЛЬНЫХ АРХИТЕКТУР НЕЙРОСЕТЕЙ

The article discusses the use of meta-learning for automatic design of optimal neural network architectures. An approach based on two-level optimization is proposed, where meta-learning is used to search for universal parameters and strategies that accelerate the process of generating effective architectures. It is shown that meta-learning reduces computational costs and provides high adaptability to new problems, which makes it preferable to traditional methods such as manual design and network architecture search (NAS). Experimental results confirming the advantage of the approach are presented, and the main limitations are identified, including the requirements for computational resources and challenges in generalizing to new problems. Promising directions of development are considered, including integration with transformers, hybrid methods, and adaptation for specialized areas such as medicine and finance. The proposed approach contributes to the acceleration of the development of neural networks, improving their performance and availability for application in a wide range of problems.

Renaturing the City in Architectural Design Through Camouflage Strategy

The urban environment is built on the natural environment. The presence of architectural design dominates the urban environment through materials. The solid nature of the material causes the urban environment to be unable to absorb rainwater optimally. Decreased soil quality can have a negative impact on humans. Therefore, naturalization of the urban environment is needed to reduce the negative impact of the environment on humans. This research explores the optimal form of architectural design to channel rainwater into the ground. This research is qualitative in nature. The research method used is experimental. Experiments were carried out to find the optimal architectural design form through small-scale models. The research results are in the form of a full-slooping architectural design that can maximize water absorption into the soil. This is caused by the application of a camouflage strategy in finding the most optimal shape. This allows renaturalizing the urban environment to reduce the risk of negative impacts on humans.

ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images.

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models.