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Optimization Method for Spatial Element Layout of Urban Landscape Architecture Based on generative algorithm

Procedia Computer Science 2025年 261卷 345-353页

作者： Di Zhu Xi’an FanYi University Xi’an 710105 Shaanxi China

In urban landscape design, the optimization of spatial layout is very important because it directly affects the functionality, ecological benefits and aesthetic effects of the landscape. This paper introduces a generative algorithm to optimize the layout of garden space elements. It aims to improve the rationality, aesthetics and functionality of the layout scheme through automation and intelligence, so as to better adapt to different environments and user needs. First, a geometric model of the urban landscape space is constructed, and the functional areas, green spaces, water bodies and other elements of the garden are converted into design constraints and objective functions suitable for generative algorithm processing. Then, using genetic algorithm as an optimization tool, through the iterative evolution process of the population, the position, form and combination of spatial elements are gradually adjusted to achieve the predetermined design goals. Finally, the fitness function is introduced to evaluate the pros and cons of each generation of layout schemes, including the openness of the landscape view, the coherence of the functional areas and the aesthetic evaluation. Through multiple iterations, the algorithm gradually converges to the optimal solution and provides a garden layout scheme that meets specific needs. In the layout optimization under complex terrain, the space utilization rate is between 75.0% and 77.8%. The landscape view is distributed between 66.3% and 68.0%, which is relatively evenly distributed. The urban landscape garden space element layout optimization method based on the generative algorithm introduced in this paper not only improves the design efficiency and accuracy but also provides new ideas for urban garden planning.

关键词： generative algorithm Urban Landscape Architecture Spatial Element Layout Optimization Landscape Vision

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Modular coordination-based generative algorithm to optimize construction waste

Modular coordination-based generative algorithm to optimize ...

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International High-Performance Built Environment Conference (iHBE)

作者： Banihashemi, Saeed Tabadkani, Amir Hosseini, M. Reza Univ Technol Sydney Sch Built Environm 15 Broadway Ultimo 2007 Australia Politecn Milan Dept Bldg & Architectural Engn I-20133 Milan Italy Deakin Univ Sch Architecture & Built Environm Geelong Vic 3220 Australia

In response to the growing attention to sustainable built environment, this study aims at introducing an approach in construction waste optimization through integrating parametric design with offsite construction methodology. To this end, a generative algorithm was developed within the integrated platform of Rhino and Grasshopper software based on modular coordination rules and ASTM international standards as the design constraints in modules array. Two sets of horizontal and vertical modules were obtained from a prototype model while an evolutionary solver function was employed for optimizing the generated waste. This resulted in developing different modular design variants which generate the minimum amount of waste while being fully compliant with international standards. This study contributes to the field by presenting one of the first studies in its kind focusing on the integration of parametric design into offsite construction methodology through the lenses of construction waste optimization. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Offsite construction parametric design generative algorithm modular coordination waste optimization

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Beyond Single-Protein de novo Design: A generative algorithm for the NTF2-like Superfamily

Beyond Single-Protein de novo Design: A Generative Algorithm...

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作者： Basanta, Benjamin University of Washington

学位级别：Ph.D.

Natural proteins evolved over billions of years to regulate cellular growth, ward off infection and capture and store solar energy. Proteins thus serve as the molecular basis for life. The promise of protein design is to use nature’s favorite toolbox to solve modern human problems without having to wait for the long and meandering path of natural selection. Protein structure determines function, so it is not surprising proteins sample a great variety of structures. Thus, it is reasonable to expect that designing proteins with functions not seen in nature would require access to comparable structural diversity, more specifically, diversity of active site structure. Despite the advances in de novo protein design, the systematic generation of proteins containing pockets that can harbor substrates has been lacking. The use of a natural small alpha-beta fold, the Nuclear Transport Factor 2-like (NTF2-like) fold, to design a high affinity small-molecule binding protein, and the diversity observed in that family, have posed the idea that significant pocket structural diversity could be derived from this relatively simple, small, alpha-beta fold. To explore this idea, we analyzed the structures of proteins belonging to the NTF2-like superfamily and other proteins with similar characteristics to understand the determinants of their structural diversity. The most salient feature of the NTF2-like superfamily is the curved beta-sheet that forms most of their pockets in its concave face. Curved beta sheets depart from the classic beta pleated structure displaying bulges, tight kinks and irregular bending and twisting. The first step towards de novo design of a large variety of NTF2-like proteins is devising principles for designing curved beta sheets. In this work, we demonstrate we can generate a number of different curved sheets in the context of NTF2-like proteins. We then use these principles, along with additional information from native NTF2-like proteins, to create a gene

关键词： Computational Biology generative algorithm generative design High-throughput screening Protein design Biochemistry Biological chemistry

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Modular Coordination-based generative algorithm to Optimize Construction Waste

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Procedia Engineering 2017年 180卷 631-639页

作者： Saeed Banihashemi Amir Tabadkani M. Reza Hosseini School of built environment University of Technology Sydney (UTS) 15 Broadway Ultimo 2007 Australia Department of building and architectural engineering Politecnico di Milano Milano 20133 Italy School of architecture & built environment Deakin University Geelong 3220 Australia

关键词： Offsite construction parametric design generative algorithm modular coordination waste optimization

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Bi-Mode Inverse Design of 3D Structures for MEMS Resonators

IEEE ELECTRON DEVICE LETTERS

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IEEE ELECTRON DEVICE LETTERS 2025年第5期46卷 841-844页

作者： Zuo, Binzhou Wu, Zeyu Zhao, Junyuan Niu, Bo Lei, Yumo Cao, Lixin Zhu, Yinfang Yang, Jinling Inst Semicond Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Inst Phys Beijing 100190 Peoples R China

This work presents an automated algorithm for MEMS resonator structure generation based on inverse design, integrating deep learning and neural networks to predict key physical properties, including resonance frequency (f), quality factor of thermoelastic damping (Q(TED)), and motional impedance (R-x). Unlike traditional methods relying on finite element analysis (FEA), this approach leverages a database-driven deep learning model, achieving prediction speeds 9,740 times faster than the conventional FEA software with an average accuracy of 97.5%, 96.5%, 96.4 for f, Q(TED) and R-x,respectively. The algorithm supports flexural and Lame modes and could generate resonators with a broad frequency range from similar to 8 to similar to 63 MHz, significantly surpassing existing methods. By efficiently predicting seed structures, the method guides the inverse design process, generating high Q, and low R-x resonator structures within 10 minutes. The generated devices exhibit deviations of less than 3% from target performance metrics. Simulations and experimental results validate the feasibility and effectiveness of the proposed algorithm, highlighting its potential for accelerating MEMS design with enhanced performance and precision.

关键词： Resonant frequency Genetic algorithms Deep learning Micromechanical devices Accuracy Training Three-dimensional displays Prediction algorithms Neural networks Generators MEMS resonator generative algorithm deep learning inverse design

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Architectural modelling of an olive tree. generative tools for the scientific visualization of morphology and radiation relationships

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ECOLOGICAL INFORMATICS 2016年 36卷 84-93页

作者： Filippucci, Marco Rinchi, Gabriele Brunori, Antonio Nasini, Luigi Regni, Luca Proietti, Primo Univ Perugia Dept Civil & Environm Engn Via G Duranti 93 I-06125 Perugia Italy Univ Perugia Dept Agr Food & Environm Sci Via Borgo 20 Giugno 74 I-06121 Perugia Italy

This research integrates the agricultural study of trees with the sciences of representation, in order to describe the architectural form of an olive tree and to show a scientific visualization of the relationship between morphology and light interception in the canopy. The representation of plant architecture, manipulated with pruning operation for agricultural purposes of light optimization, describes the action of sunlight in the tree, testing the potential of digital design tools - especially generative modelling. Through the design of a specific algorithm, the tree is interpreted like a fragmented photovoltaic panel, analysed using 14,000 control points corresponding to the leaves. The possibility to select these classes of elements becomes the instrument to explain the canopy structure, finding categories that describe and simulate the annual radiance and illuminance. The developed modelling process and its purely theoretical significance constitute the basis for a variety of applications in data analysis and comparison between different models, evaluations, theories, and operations. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Trees digital modelling generative algorithm Sun lighting analysis Scientific application

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A Machine Learning generative Method for Automating Antenna Design and Optimization

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2022年 7卷 285-295页

作者： Zhong, Yang Renner, Peter Dou, Weiping Ye, Geng Zhu, Jiang Liu, Qing Huo Meta Real Lab Sunnyvale CA 94087 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Meta Real Lab Sunnyvale NC 27708 USA

To facilitate the antenna design with the aid of computer, one of the practices in consumer electronic industry is to model and optimize antenna performances with a simplified antenna geometric scheme. The ease of handling multi-dimensional optimization problems and the less dependence on the engineers' knowledge and experience are the key to achieve the popularity of simulation-driven antenna design and optimization for the industry. In this paper, we introduce a flexible geometric scheme with the concept of mesh network that can form any arbitrary shape by connecting different nodes. For such problems with high dimensional parameters, we propose a machine learning based generative method to assist the searching of optimal solutions. It consists of discriminators and generators. The discriminators are used to predict the performance of geometric models, and the generators to create new candidates that will pass the discriminators. Moreover, an evolutionary criterion approach is proposed for further improving the efficiency of our method. Finally, not only optimal solutions can be found, but also the well trained generators can be used to automate future antenna design and optimization. For a dual resonance antenna design, our proposed method is better than the other mature algorithms.

关键词： Antennas Generators Optimization Computational modeling Measurement Geometric modeling Machine learning Antenna modeling antenna optimization machine learning generative algorithm evolutionary approach

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A generative Approach to Chinese Shanshui Painting

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2017年第1期37卷 15-19页

作者： Shi, Weili Parsons Sch Design New York NY 10011 USA Bluecadet Philadelphia PA 19125 USA

The Shan Shui in the World project recreated Manhattan, New York, in the style of traditional Chinese shanshui paintings by using a generative algorithm to transform its buildings into mountains. This project revisits the ideas implicit in Chinese literati paintings of shan shui: the relationship between urban life and people's yearning for nature and between social responsibility and spiritual purity. Shan Shui in the World was made possible by the growing ubiquitousness of data and the development of data visualization techniques, especially generative art. [ABSTRACT FROM AUTHOR]

关键词： Art art-science collaborations Buildings computer graphics data visualization generative algorithm nonphotorealistic rendering Painting Rendering (computer graphics) shanshui paintings Shape Urban areas

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Generation Process and Analysis of Innovative Timberfabric Vaults

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STRUCTURAL ENGINEERING INTERNATIONAL 2014年第2期24卷 254-264页

作者： Albenque, Etienne Hudert, Markus Humbert, Laurent Weinand, Yves Ecole Polytech Fed Lausanne Lab Timber Construct IBOIS Lausanne Switzerland

Timberfabric structures, developed at IBOIS, are a new family of timber constructions based on the logic and principles of man-made fabrics. Modular elements called "timberfabric modules" are generated by curving and connecting two thin wooden panels. This paper concerns the study of vaults obtained by assembling together several timberfabric modules. A parametric tool is presented which can generate automatically a three-dimensional (3D) finite element model of a structure for a given set of initial parameters. This tool assists the architectural design of such structures, providing insights into their geometry as well as into the construction stresses and the kinematic constraints between different constitutive elements.

关键词： timberfabric vaults innovative structures Python script generative algorithm assembly process

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Experimental investigation and numerical modeling of oxidative pyrolysis mechanism of mining PVC cable sheath

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2022年第24期147卷 14479-14490页

作者： Zhao, Jinlong Ma, Hanchao Huang, Hong Zhang, Jianping Chen, Youliang China Univ Min & Technol State Key Lab Coal Resources & Safe Min Beijing Peoples R China China Univ Min & Technol Sch Emergency Management & Safety Engn Beijing Peoples R China Tsinghua Univ Inst Publ Safety Res Dept Engn Phys Beijing Peoples R China Ulster Univ Belfast Sch Architecture & Built Environm Newtownabbey North Ireland China Acad Safety Sci & Technol Beijing Peoples R China

Cables modified with flame retardants are widely used in the coal mining process to enhance their fire safety performance. This paper investigates experimentally and numerically the thermo-oxidative degradation process of PVC-based mining cable sheath, which consists of PVC resin and plasticizers (Phthalate esters), fillers (Calcium carbonate), and flame retardant (Antimony trioxide) as additives. Thermogravimetry coupled with Fourier transform infrared spectroscopy (TG-FTIR) experiments were performed at different heating rates in an air atmosphere. TG results showed that the decomposition processes of mining PVC cable sheath are more complicated than those of pure PVC with additional degradation steps, which was further confirmed by FTIR gas analysis. A deconvolution method was used to distinguish the independent reactions from the overlapped derivative thermogravimetric (DTG) peaks. It was found that the whole degradation process can be divided into seven steps, based on which an oxidative pyrolysis model was developed: pyrolysis of plasticizers (steps 1 and 2), dehydrogenation (step 3), emission and combustion of volatile fraction (step 4), carbon combustion (step 5), oxidative pyrolysis of complicated additives (step 6), and decomposition of residues (step 7). The kinetic triplets (activation energy, pre-exponential factor, and reaction model function) for each reaction were firstly calculated using three commonly used model-free methods and then further optimized using the genetic algorithm (GA). Based on the optimized parameters, the reaction mechanisms and their associated kinetic parameters were determined. The findings of this study are important in understanding the oxidative pyrolysis process of PVC cable sheath, and the obtained kinetic parameters can also be used for its pyrolysis and fire modeling, waste recycling, and risk assessment.

关键词： PVC cable sheath Oxidative pyrolysis Reaction kinetics Deconvolution generative algorithm TG-FTIR

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