Generative design of truss systems by the integration of topology and shape optimisation

Watson, Marcus; Leary, Martin; Brandt, Milan

doi:10.1007/s00170-021-07943-1

Cited by 10 publications

(5 citation statements)

References 73 publications

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“…Generative design (GD) [28][29][30][31] is the process of utilizing artificial intelligence (AI) to generate meaningful heuristic results when either traditional methods fail, or a single solution cannot be obtained (e.g., no single solution exists that satisfies all objectives simultaneously). In such cases, many good solutions (in the case of multi-objective optimization, Pareto front) are generated to solve the optimization problem [32,33].…”

Section: Design Optimization For Sustainabilitymentioning

confidence: 99%

Renovation or Redevelopment: The Case of Smart Decision-Support in Aging Buildings

Maalek

2023

Smart Cities

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In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on a strategy for the renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with lifecycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and sustainability. It was further observed that optimizing the building design with the goal of reducing embodied energy and carbon in compliance with modern energy standards was crucial to improving overall energy performance. This work demonstrated that the BIM-based framework developed to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

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Section: Design Optimization For Sustainabilitymentioning

confidence: 99%

Renovation or Redevelopment: The Case of Smart Decision-Support in Aging Buildings

Maalek

2023

Smart Cities

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“…Instead of adding an extra constraint, Deng and To (2021) presented a parametric level set method using deep learning for TO and generated different designs by changing the parameters. Similarly, Watson et al (2021) generated different topologyoptimized designs by changing the TO settings (e.g., volume fraction), but the results were not comparable (e.g., different volumes), and the number of permutations was small. He et al (2020) integrated genetic algorithms (GA) into TO, altering the initial and the intermediate structures during optimization.…”

Section: Diversity In Tomentioning

confidence: 99%

Improving the diversity of topology-optimized designs by swarm intelligence

Kwok¹

2022

Struct Multidisc Optim

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Although additive manufacturing can produce nearly any geometry, users have limited choices in the designs. Topology optimization can create complex shapes, but it provides only one solution for one problem, and existing design exploration methods are ineffective when the design space is huge and high-dimensional. Therefore, this paper develops a new generative design method to improve the diversity of topology-optimized designs. Based on the observation that topology optimization places materials along the principal directions to maximize stiffness, this paper creates a rule of principal direction and applies it to swarm intelligence for form-finding. The shapes got by the swarming process possess both randomness and optimality. After they are further optimized, the final designs have high diversity. This is the first time integrating structural stiffness as a swarm principle to influence the collective behavior of decentralized, self-organized systems. The experimental results show that this method can generate interesting designs that have not been seen in the literature. Some results are even better than those got by the original topology optimization method, especially when the problem is more complex. This work not only allows users to choose unique designs according to their preference, but also helps users find better designs for their application.

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“…The Flexible Neural Tree (FNT) structure can better describe the relationship between office furniture and its configuration, thereby achieving more efficient and accurate design search and optimization. Through this method, designers can not only quickly explore design solutions that meet specific needs, but also continuously explore and optimize creativity based on algorithm feedback [5][6]. Therefore, this study proposes a parameterized design method for office furniture partitions that combines tree structure Interactive Differential Evolution (IDE) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Parametric Design of Office Furniture Partition Space Integrated with the Interactive Evolution Algorithm of FNT and Tree Structure

Chen,

Guan

2024

SCPE

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Office furniture and its spatial layout design are playing an increasingly important role in improving work efficiency and employee comfort. However, the technology still faces some challenges. For instance, accurately simulating and evaluating the behavior and feelings of people in the office environment is difficult due to the high complexity of furniture spacing space design. It is important to address these issues. The study aims to explore the key technology and practical application of the parametric design of office furniture partition space based on the interactive evolution algorithm of tree structure. This paper proposes an improved version of the flexible neural tree model and corresponding algorithm. It also presents a design method based on the interactive differential evolution algorithm to optimize the automatic balance effect between global exploration and local development in the average shortening of the difference vector based on individual distribution. The results showed that all indexes were larger than or equal to other algorithms on 46 datasets. According to the Wilcoxon signed-rank test, the P-value was all less than 0.05, which is a significant advantage. Median, mean, and quartiles indicated that the overall performance of the algorithm was higher than the others. Furthermore, similarity evaluation-based Flexible Neural Tree algorithm had no outliers in the selected dataset, which also indicates the stability of the performance. The research results will support innovation and development in the field of office furniture design. This will promote intelligence, efficiency, and personalization in the design process, and meet the diverse needs of modern office environments.

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Generative design of truss systems by the integration of topology and shape optimisation

Cited by 10 publications

References 73 publications

Renovation or Redevelopment: The Case of Smart Decision-Support in Aging Buildings

Renovation or Redevelopment: The Case of Smart Decision-Support in Aging Buildings

Improving the diversity of topology-optimized designs by swarm intelligence

Parametric Design of Office Furniture Partition Space Integrated with the Interactive Evolution Algorithm of FNT and Tree Structure

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