Optimization of load-bearing parameters for anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures

As the global construction industry develops, prefabricated buildings are gradually emerging and widely used. However, the bearing capacity of anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures remains a technical challenge that restricts their widespread application. Therefore, the study improves the quality information model for prefabricated nodes and the dragonfly algorithm by introducing principal component dimensionality reduction methods and multiple strategies through data modeling. Finally, a quality control model for anisotropic nodes and an optimization model for the load-bearing parameters of prefabricated wall panels are proposed. The experimental results showed that the control error of the nodal quality control model was as low as 0.9 mm. The displacement angle was as low as 0.037 rad. The maximum shear strength was 7.6 MPa. The minimum number of iterations of the parametric optimization model was 160 and the number of optimal solution strategies generated was 4500. The ratio of anti-overturning moment under wind and earthquake loads decreased by 0.45 and 0.13 compared with before optimization, respectively. Therefore, the proposed model can improve the load-bearing capacity and energy consumption capacity of nodes, and reduce control errors. The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. This offers a scientific foundation for designing and constructing prefabricated shear walls.

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Publication Details

Journal
PLoS ONE
Published
2025-03-17
DOI
https://doi.org/10.1371/journal.pone.0318521
Primary Topic
Structural Engineering and Vibration Analysis
Type
article
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article

Optimization of load-bearing parameters for anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures

Libo Wang, Tianshou Ma, Jun Zhao
PLoS ONE
Structural Engineering and Vibration Analysis
article

Optimization of load-bearing parameters for anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures

Libo Wang, Tianshou Ma, Jun Zhao
article en

Abstract

As the global construction industry develops, prefabricated buildings are gradually emerging and widely used. However, the bearing capacity of anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures remains a technical challenge that restricts their widespread application. Therefore, the study improves the quality information model for prefabricated nodes and the dragonfly algorithm by introducing principal component dimensionality reduction methods and multiple strategies through data modeling. Finally, a quality control model for anisotropic nodes and an optimization model for the load-bearing parameters of prefabricated wall panels are proposed. The experimental results showed that the control error of the nodal quality control model was as low as 0.9 mm. The displacement angle was as low as 0.037 rad. The maximum shear strength was 7.6 MPa. The minimum number of iterations of the parametric optimization model was 160 and the number of optimal solution strategies generated was 4500. The ratio of anti-overturning moment under wind and earthquake loads decreased by 0.45 and 0.13 compared with before optimization, respectively. Therefore, the proposed model can improve the load-bearing capacity and energy consumption capacity of nodes, and reduce control errors. The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. This offers a scientific foundation for designing and constructing prefabricated shear walls.

PLoS ONEVol. 20(3)
Anyang Institute of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 95%
Structural Engineering and Vibration Analysis
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Optimization of load-bearing parameters for anisotropic nodes and prefabricated wall panels in prefabricated shear wall structures — Libo Wang, Tianshou Ma, et al. · PLoS ONE (2025) | TGRS Research Map | TGRS