ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology Article Swipe
Kai Rong
,
Yongsheng Jia
,
Yingkang Yao
,
Jinshan Sun
,
Qi Yu
,
Hongliang Tang
,
Jun Yang
,
Xianqi Xie
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/buildings15132351
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/buildings15132351
The drilling and blasting method is commonly employed for the rapid demolition of outdated buildings by destroying key structural components and inducing progressive collapse. The residual bearing capacity of these components is governed by the deformation morphology of the longitudinal reinforcement, characterized by bending deflection and exposed height. This study develops and validates a finite element (FE) model of a reinforced concrete (RC) column subjected to demolition blasting. By varying concrete compressive strength, the yield strength of longitudinal reinforcement, the longitudinal reinforcement ratio, and the shear reinforcement ratio, 45 FE models are established to simulate the post-blast morphology of longitudinal reinforcement. Two databases are created: one containing 45 original simulation cases, and an augmented version with 225 cases generated through data augmentation. To predict bending deflection and the exposed height of longitudinal reinforcement, artificial neural network (ANN) and random forest (RF) models are optimized using the hunter–prey optimization (HPO) algorithm. Results show that the HPO-optimized RF model trained on the augmented database achieves the best performance, with MSE, MAE, and R2 values of 0.004, 0.041, and 0.931 on the training set, and 0.007, 0.057, and 0.865 on the testing set, respectively. Sensitivity analysis reveals that the yield strength of longitudinal reinforcement has the most significant impact, while the shear reinforcement ratio has the least influence on both output variables. The partial dependence plot (PDP) analysis indicates that the ratio of shear reinforcement has the most significant impact on the deformation of longitudinal reinforcement.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/buildings15132351
- https://www.mdpi.com/2075-5309/15/13/2351/pdf?version=1751633962
- OA Status
- gold
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412019886
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4412019886Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/buildings15132351Digital Object Identifier
- Title
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ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column MorphologyWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-04Full publication date if available
- Authors
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Kai Rong, Yongsheng Jia, Yingkang Yao, Jinshan Sun, Qi Yu, Hongliang Tang, Jun Yang, Xianqi XieList of authors in order
- Landing page
-
https://doi.org/10.3390/buildings15132351Publisher landing page
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https://www.mdpi.com/2075-5309/15/13/2351/pdf?version=1751633962Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2075-5309/15/13/2351/pdf?version=1751633962Direct OA link when available
- Concepts
-
Morphology (biology), Column (typography), Materials science, Algorithm, Computer science, Structural engineering, Engineering, Biology, Zoology, Connection (principal bundle)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-07-04 |
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