A nodal‐based evolutionary optimization algorithm for frame structures

Zhang, Xuyu; Xie, Yi Min; Zhou, Shiwei

doi:10.1111/mice.12834

Cited by 10 publications

(5 citation statements)

References 70 publications

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“…In this section, we apply the proposed approach for the member-type problem to the conceptual design of a lowprofile electricity transmission tower (Zhang et al 2022). The case description is shown in Fig.…”

Section: Electricity Transmission Tower Examplementioning

confidence: 99%

Reducing the number of different members in truss layout optimization

Xie

2023

Struct Multidisc Optim

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Despite the long history of the truss layout optimization approach, its practical applications have been limited, partly due to high manufacturing costs associated with complex optimized structures consisting of members with different cross-sectional areas and member lengths. To address this issue, this study considers optimizing truss structures comprising limited types of members. On this topic, two distinct problems are considered, wherein the first problem, members of the same type share the same cross-sectional area (i.e., section-type problem); and in the second problem, members of the same type share the same cross-sectional area and length (i.e., member-type problem). A novel post-processing approach is proposed to tackle the target problems. In this approach, the optimized structures from the traditional layout and geometry optimization approaches are used as the starting points, members of which are then separated into groups by the k-means clustering approach. Subsequently, the clustered structures are geometrically optimized to reduce the area and length deviations (i.e., the differences between member area/length values and the corresponding cluster means). Several 2D and 3D examples are presented to demonstrate the capability of the proposed approaches. For the section-type problem, the area deviations can be reduced to near 0 for any given cluster number. The member-type problem is relatively more complex, but by providing more clusters, the maximum length deviation can be reduced below the target thresholds. Through the proposed clustering approach, the number of different members in the optimized trusses can be substantially decreased, thereby significantly reducing manufacturing costs.

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Section: Electricity Transmission Tower Examplementioning

confidence: 99%

Reducing the number of different members in truss layout optimization

Xie

2023

Struct Multidisc Optim

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“…The possibility of finding the optimal solution by heuristic methods is positively related to the number of evaluated solutions in the design space. Typically, heuristic methods require hundreds of evaluations during iterations before finding a rational solution (Adeli & Sarma, 2006; Boscardin et al., 2019; X. Zhang et al., 2023). Another kind of emerging design technique is artificial intelligence (AI)‐based methods (Málaga‐Chuquitaype, 2022).…”

Section: Introductionmentioning

confidence: 99%

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

Fei,

Liao,

et al. 2023

Computer aided Civil Eng

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The construction material quantity (CMQ) is widely concerned in the structural design of reinforced concrete buildings and is often included among the objective functions of computer‐aided optimization design techniques. To minimize construction cost and carbon emissions, an accurate and efficient CMQ estimation method is timely required. In this study, a novel graph neural network (GNN) is proposed, whose architecture and loss function are specifically designed for CMQ estimation. With a heterogeneous feature fusion mechanism, the GNN can automatically extract features from all CMQ‐related information, in contrast to the existing data‐driven methods that rely heavily on manually selected features. By further incorporating a prior knowledge inclusion strategy, the GNN can avoid fundamental errors that might be encountered by purely data‐driven methods. To enrich the diversity of the CMQ dataset, a data augmentation method is proposed incorporating generative adversarial networks and parametric modeling. Numerical experiments and case studies show that the proposed CMQ estimation method is superior to the existing data‐driven methods in terms of accuracy and is 500 times faster than typical commercial structural design software. This study is anticipated to benefit the objective evaluation of computer‐aided design, thereby facilitating the promotion of low‐cost and low‐carbon‐emission building designs.

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“…Chang and Cheng (2020) used graphs to represent building structures and develop an end-to-end pipeline to propose optimal cross-sections for columns and beams by using alongside a pre-trained differentiable structural simulator. Zhang et al (2023) proposed a nodal-based evolutionary design optimization algorithm, demonstrating its efficacy in designing optimal crane arms, transmission towers, and aquatic domes. Song et al (2022) organized structural topologies and member configurations as graph data and performed structural analysis with GNN, achieving 99% accuracy and excellent computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al. (2023) proposed a nodal‐based evolutionary design optimization algorithm, demonstrating its efficacy in designing optimal crane arms, transmission towers, and aquatic domes. Song et al.…”

Section: Introductionmentioning

confidence: 99%

End‐to‐end generation of structural topology for complex architectural layouts with graph neural networks

Zhang,

Tao,

Wang

et al. 2023

Computer aided Civil Eng

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Current automated structural topology design methods can only deal with limited design spaces or simplified architectural layouts for lack of data or a proper representation of structure topology. To address this, the abundant information of manually designed architectural and structural layouts should be exploited to guide the topology design. To achieve automatic generation of structural topologies according to real‐world architectural layouts, this research introduces StrucTopo‐generative adversarial network (GAN), an end‐to‐end generative model with node and edge generation stages based on proper graph representation. Nodes are generated using an image‐to‐image translation model, and edges are generated with a GAN‐based approach. The model is trained and tested on a dataset of 300 complex architectural and structural layouts. Measured against the manually designed topologies, the results indicate that the proposed model can generate reasonable structural topologies, with a recall of 97% and an intersection‐over‐union of 80% in node generation, with a precision of 92% and a recall of 91% in edge generation. Additionally, the joint generation shows a graph similarity of 72%. The proposed model is the first of its kind to consider complex architectural layout constraints in the generation of structural topology, marking a step forward in applying artificial intelligence to practical structural design.

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A nodal‐based evolutionary optimization algorithm for frame structures

Cited by 10 publications

References 70 publications

Reducing the number of different members in truss layout optimization

Reducing the number of different members in truss layout optimization

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

End‐to‐end generation of structural topology for complex architectural layouts with graph neural networks

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