ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology
The drilling and blasting method is commonly employed for the rapid demolition of outdated buildings by destroying key structural components and inducing progressive collapse.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 reinforcemen...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/13/2351 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849429001734455296 |
|---|---|
| author | Kai Rong Yongsheng Jia Yingkang Yao Jinshan Sun Qi Yu Hongliang Tang Jun Yang Xianqi Xie |
| author_facet | Kai Rong Yongsheng Jia Yingkang Yao Jinshan Sun Qi Yu Hongliang Tang Jun Yang Xianqi Xie |
| author_sort | Kai Rong |
| collection | DOAJ |
| description | The drilling and blasting method is commonly employed for the rapid demolition of outdated buildings by destroying key structural components and inducing progressive collapse.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 R<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-905465284ee14f76a75ded22702de484 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-905465284ee14f76a75ded22702de4842025-08-20T03:28:29ZengMDPI AGBuildings2075-53092025-07-011513235110.3390/buildings15132351ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column MorphologyKai Rong0Yongsheng Jia1Yingkang Yao2Jinshan Sun3Qi Yu4Hongliang Tang5Jun Yang6Xianqi Xie7State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, ChinaState Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, ChinaState Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, ChinaState Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, ChinaChina Safety Technology Research Academy of Ordnance Industry, Beijing 100053, ChinaChina Safety Technology Research Academy of Ordnance Industry, Beijing 100053, ChinaState Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, ChinaState Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, ChinaThe 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 R<sup>2</sup> 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.https://www.mdpi.com/2075-5309/15/13/2351demolition blastingreinforced concrete columnpost-blast deformation morphologyresidual bearing capacitynumerical simulation |
| spellingShingle | Kai Rong Yongsheng Jia Yingkang Yao Jinshan Sun Qi Yu Hongliang Tang Jun Yang Xianqi Xie ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology Buildings demolition blasting reinforced concrete column post-blast deformation morphology residual bearing capacity numerical simulation |
| title | ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology |
| title_full | ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology |
| title_fullStr | ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology |
| title_full_unstemmed | ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology |
| title_short | ANN and RF Optimized by Hunter–Prey Algorithm for Predicting Post-Blast RC Column Morphology |
| title_sort | ann and rf optimized by hunter prey algorithm for predicting post blast rc column morphology |
| topic | demolition blasting reinforced concrete column post-blast deformation morphology residual bearing capacity numerical simulation |
| url | https://www.mdpi.com/2075-5309/15/13/2351 |
| work_keys_str_mv | AT kairong annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT yongshengjia annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT yingkangyao annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT jinshansun annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT qiyu annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT hongliangtang annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT junyang annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology AT xianqixie annandrfoptimizedbyhunterpreyalgorithmforpredictingpostblastrccolumnmorphology |