Blast damage assessment model of PC slabs based on XGBoost

ZHAO Chunfeng; WU Yixiu; XIANG Siqi; LI Xiaojie

doi:10.11883/bzycj-2025-0250

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ZHAO Chunfeng, WU Yixiu, XIANG Siqi, LI Xiaojie. Blast damage assessment model of PC slabs based on XGBoost[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0250

Citation:

ZHAO Chunfeng, WU Yixiu, XIANG Siqi, LI Xiaojie. Blast damage assessment model of PC slabs based on XGBoost[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0250

ZHAO Chunfeng, WU Yixiu, XIANG Siqi, LI Xiaojie. Blast damage assessment model of PC slabs based on XGBoost[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0250

Citation:

ZHAO Chunfeng, WU Yixiu, XIANG Siqi, LI Xiaojie. Blast damage assessment model of PC slabs based on XGBoost[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0250

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Blast damage assessment model of PC slabs based on XGBoost

doi: 10.11883/bzycj-2025-0250

ZHAO Chunfeng^{1, 2, 3
,},
WU Yixiu²,
XIANG Siqi⁴,
LI Xiaojie³

1.
Anhui Provincial Key Laboratory of Civil Engineering Structures and Materials, Hefei University of Technology, Hefei 230009, Anhui, China
2.
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China
3.
State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, Liaoning, China
4.
College of Water Resources and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China

Received Date: 2025-08-10
Rev Recd Date: 2025-09-25

Available Online: 2025-09-30

Abstract

Abstract

Prefabricated building structures have been widely applied in civil engineering due to their advantages of energy conservation, environmental protection, controllable quality, and efficient construction. As the core load-bearing components of prefabricated building structures, precast reinforced concrete (PC) slabs are vulnerable to threats from gas explosions, industrial explosions, and terrorist attacks. To accurately assess the damage state of PC slabs under explosion, enhance structural blast resistance, and reduce casualties, an explosion response dataset of PC slabs was constructed. Six geometric parameters (slab thickness/length/width, steel reinforcement ratio, compressive strength of concrete, etc.) and two explosion load parameters (explosive weight and explosive distance) were selected as input features. Three machine learning algorithms (GPR, RF, and XGBoost) were used to predict the maximum displacement of PC slabs, and their prediction accuracies are compared by root mean square error, coefficient of determination, mean absolute error, scattering index, and comprehensive performance objective function. Furthermore, a damage classification evaluation model based on the support rotation angle damage criterion is proposed. The performance differences of the model under three criteria are analyzed by confusion matrix and five classification indices (accuracy, precision, recall, F₁-score, and Kappa coefficient), and compared with simplified models and empirical prediction methods. The research results indicate that in terms of maximum displacement prediction for PC slabs under explosion loads, the XGBoost model demonstrates the best performance among the three machine learning models (GPR、RF and XGBoost). Specifically, the fitting degree of XGBoost is superior to those of GPR and RF models. Meanwhile, and the XGBoost shows the most outstanding comprehensive performance, with a damage recognition accuracy of 92.5%, which demonstrates its high-efficiency in identifying different damage types. The XGBoost-based damage classification evaluation model for PC slabs under explosion loads exhibits powerful performance, providing important references for structural blast resistance design and rapid post-blast damage assessment.
- precast reinforced concrete slab,
- machine learning,
- extreme gradient boosting,
- damage assessment

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References(34)

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