Automated structural design of shear wall structures based on modified genetic algorithm and prior knowledge

Zhou, Xuhong; Wang, Lufeng; Liu, Jiepeng; Cheng, Guozhong; Chen, Dong; Yu, Peng

doi:10.1016/j.autcon.2022.104318

Cited by 24 publications

(12 citation statements)

References 21 publications

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“…It can be observed that the proposed Graph‐GEN is remarkably advantageous (an increase of 500 times), over YJK, which calculates the CMQs of each structural component after detailed structural analysis and reinforcement design. When using genetic algorithms for structural optimization, hundreds of times of CMQ estimation are typically needed (Orhan & Taşkın, 2021; X. Zhou et al., 2022). For example, a population size of 20 and an iteration number of 50 will result in 1000 possible schemes and 1000 times of CMQ estimation.…”

Section: Case Studymentioning

confidence: 99%

“…Category 1: Simplified formulas can be used to estimate CMQ (Sharafi et al, 2012;Y. Zhang & Mueller, 2017;X. Zhou et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Category 1: Simplified formulas can be used to estimate CMQ (Sharafi et al., 2012; Y. Zhang & Mueller, 2017; X. Zhou et al., 2022). In this category, the methods are highly efficient; however, they typically either display low accuracy or are only applicable to simple structural systems, such as frame structures.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Case Studymentioning

confidence: 99%

“…Category 1: Simplified formulas can be used to estimate CMQ (Sharafi et al, 2012;Y. Zhang & Mueller, 2017;X. Zhou et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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show abstract

“…X. Zhou et al (2022) proposed a modified genetic algorithm to generate the shear wall layout based on an architectural floor plan using a segmentation and graph representation of rooms. Zhao et al (2022) introduced an intelligent beam and slab design method for shear wall structures.…”

Section: Introductionmentioning

confidence: 99%

“…(3) The optimization space is narrow and simple. Many recent studies focused on intelligent placement of shear walls (Liao et al, 2021;X. Zhou et al, 2022), wherein the optimization space was limited in existing wall lines in architectural layouts.…”

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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Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Fei,

Liao,

Zhao

et al. 2024

Earthq Engng Struct Dyn

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To address the issue of costly computational expenditure related to high‐fidelity numerical models, surrogate models have been widely used in various engineering tasks, including design optimization. Despite the successful application of the existing surrogate models, physics‐based models depend largely on simplifications and assumptions, which render parameter calibration challenging; whereas data‐driven models require substantial data to reach their full potential, with their performance often being constrained in tasks when obtaining massive data is difficult. In this study, a hybrid surrogate model is proposed combining physics‐based and data‐driven models to rapidly estimate building seismic responses. The application of this model is exemplified through effective estimation of inter‐story drift ratios (IDRs), being a critical factor in shear‐wall structure design. Initially, a data augmentation technique and a parametric modeling procedure are introduced to significantly enhance the dataset diversity. Subsequently, a task decomposition strategy is proposed to effectively integrate a data‐driven graph neural network (GNN) and a physics‐based flexural‐shear model. Additionally, the output layer and the loss function of the GNN are modified to enhance the estimation accuracy by eliminating fundamental errors. Results of numerical experiments indicate that the proposed hybrid model can complete IDR estimations in an average time of 0.56 s, with a mean absolute percentage error of 12.7%. This performance significantly surpasses that of existing purely data‐driven and physics‐based models. A case study shows that the efficiency of the proposed hybrid model is approximately 100 times greater than that of conventional finite element software. This enables an accurate assessment of the design compliance with code requirements. The results of this study can be applied to the design optimization of seismic‐resistant building structures.

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Automated structural design of shear wall structures based on modified genetic algorithm and prior knowledge

Cited by 24 publications

References 21 publications

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

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

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

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