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<p>How to realize the unity of safety, comfort, and economy of building structures has been a hot spot of concern in the field of construction engineering. This paper searches for optimal particles using a hybrid optimization strategy and optimizes the weights of each index in the fitness function into the same interval. Then dynamic inertia weights are used to improve the performance of the algorithm, and an enhanced adaptive particle swarm algorithm is obtained. After selecting the optimization variables for the building design, the objective function and constraints are designed, and the improved particle swarm algorithm is used to solve the optimal design of the intelligent building. The total weight of the target building structure was reduced after optimization, and 47.04% of the building materials were saved. The outer diameter of the steel pipe concrete at the lowest level of the building increases from 1.73m to 2.06m after optimization, which fulfills the law of column change in building design. It has also been found that the wind resistance of the optimized building structure has improved. This paper provides a reliable basis for the application of adaptive algorithms in building design optimization, and the proposed method also provides an effective reference for the field of construction engineering.</p>
</abstract>

How to realize the unity of safety, comfort, and economy of building structures has been a hot spot of concern in the field of construction engineering. This paper searches for optimal particles using a hybrid optimization strategy and optimizes the weights of each index in the fitness function into the same interval. Then dynamic inertia weights are used to improve the performance of the algorithm, and an enhanced adaptive particle swarm algorithm is obtained. After selecting the optimization variables for the building design, the objective function and constraints are designed, and the improved particle swarm algorithm is used to solve the optimal design of the intelligent building. The total weight of the target building structure was reduced after optimization, and 47.04% of the building materials were saved. The outer diameter of the steel pipe concrete at the lowest level of the building increases from 1.73m to 2.06m after optimization, which fulfills the law of column change in building design. It has also been found that the wind resistance of the optimized building structure has improved. This paper provides a reliable basis for the application of adaptive algorithms in building design optimization, and the proposed method also provides an effective reference for the field of construction engineering.

Application of Adaptive Algorithms in Smart Building Design Optimisation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Application of Adaptive Algorithms in Smart Building Design Optimisation"}

How to realize the unity of safety, comfort, and economy of building structures has been a hot spot of concern in the field of construction engineering. This...

Application of Adaptive Algorithms in Smart Building Design Optimisation

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1452

Schlüsselwörter
Particle swarm algorithm, Optimal particles, Fitness function, Inertia weights, Building design

© 2024 Qiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of Adaptive Algorithms in Smart Building Design Optimisation

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1452

SchlüsselwörterParticle swarm algorithm, Optimal particles, Fitness function, Inertia weights, Building design

© 2024 Qiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Particle swarm algorithm, Optimal particles, Fitness function, Inertia weights, Building design