Analysis of Internal Explosion Dynamic Response of RC Frame-masonry Infill Wall Structure and Machine Learning Prediction Model

Zhun BAI; Ye ZHOU; Rongyue ZHENG; Yutao HU; Weihua QI; Qijie CUI; Yi LI

doi:10.12382/bgxb.2025.0762

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Analysis of Internal Explosion Dynamic Response of RC Frame-masonry Infill Wall Structure and Machine Learning Prediction Model

更新时间：2026-02-10

- Analysis of Internal Explosion Dynamic Response of RC Frame-masonry Infill Wall Structure and Machine Learning Prediction Model
- Acta Armamentarii Vol. 46, Issue S2, Pages: 250762(2025)
- 作者机构：
  
  1. 西北核技术研究所, 陕西西安 710024
  2. 宁波大学土木工程与地理环境学院, 浙江宁波 315211
  3. 北京工业大学工程抗震与结构诊治北京市重点实验室, 北京 100124
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0762
  CLC：
- Received：20 August 2025，
  
  Online First：03 February 2026，
  
  Published：2025
- 稿件说明：
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Zhun BAI, Ye ZHOU, Rongyue ZHENG, et al. Analysis of Internal Explosion Dynamic Response of RC Frame-masonry Infill Wall Structure and Machine Learning Prediction Model[J]. Acta Armamentarii, 2025, 46(S2): 250762.
DOI：

Zhun BAI, Ye ZHOU, Rongyue ZHENG, et al. Analysis of Internal Explosion Dynamic Response of RC Frame-masonry Infill Wall Structure and Machine Learning Prediction Model[J]. Acta Armamentarii, 2025, 46(S2): 250762. DOI： 10.12382/bgxb.2025.0762.

摘要

为探究内部爆炸场景下钢筋混凝土框架-砌体填充墙结构的动力响应和破坏机理

基于一栋2层双向3跨的RC框架-砌体填充墙结构的原型建筑

建造1/3缩尺模型并开展内爆试验

获取墙体表面的超压-时程曲线和破坏模式等毁伤数据。建立相应的仿真模型

模拟框架结构的破坏过程

据此分析房间长跨比、剪力墙配置数量、钢筋屈服强度等关键参数对框架结构动力响应的影响规律。引入机器学习方法

建立框架结构的构件跨中峰值位移预测模型

以实现对框架结构毁伤效应的快速预测。试验结果表明:TNT质量为0.249kg时

四面填充墙均出现不同程度的脆性破坏或倒塌

楼板底部出现多道裂缝且部分保护层剥落

梁和柱仅出现少量弯曲裂缝;拉结筋可提高填充墙的整体性和变形能力。数值分析结果表明:楼板和支撑梁的动力响应对房间长宽比较为敏感

而支撑柱并未表现出显著差异;剪力墙配置面数增加时

内爆炸冲击波的泄压过程受到抑制导致楼板跨中峰值位移增加

支撑柱因剪力墙的连接而发生协同变形

支撑梁约束条件的改善降低了其动力响应;钢筋强度对结构抗爆性能的提升效果随爆炸当量的增大而增强

且随着比例距离的减小

增幅越明显。基于机器学习的预测模型计算结果表明:支持向量回归模型和极端梯度提升模型可以较好地预测框架结构的最大峰值位移

为RC框架-砌体填充墙结构的毁伤效应快速评估提供了有力手段。

Abstract

The dynamic response and failure mechanism of reinforced concrete (RC) frame-masonry infill wall structure in internal explosion scenarios are studied.A 1/3 scale model is constructed based on a prototype building with a two-story bidirectional three-span RC frame-masonry infill wall structure

and the implosion test is carried out to obtain the overpressure-time curve and failure mode of wall surface.A corresponding simulation model is established to simulate the failure process of the test structure.Based on this

the influences of key parameters such as the room length-span ratio

the number of shear wall configurations

and the yield strength of reinforcing bars on the dynamic response of frame structure are analyzed.A mid-span peak displacement prediction model of the frame structure components is established by introducing the machine learning method

rapidly predicting the damage effect of frame structure.The test results show that

when the mass of TNT is 0.249 kg

varying degrees of brittle failures or collapses occur on all the four-sided infill walls

multiple cracks appear at the bottom of the floor slab and some protective layers peel off

and only the beams and columns show a few bending cracks.Tie bars can be use to enhance the integrity and deformability of infill walls.The numerical analysis results show that the dynamic responses of floor slab and support beam are sensitive to the length-width ratio of the room

and the support columns do not any significant differences.When the number of shear wall configuration surfaces increases

the pressure relief process of the internal explosion shock wave is suppressed to result in an increase in the mid-span peak displacement of floor slab.The support columns undergo cooperative deformation due to the connection of the shear walls

and the improvement in the constraint conditions of the support beams reduces their dynamic response.The improvement effect of steel bar strength on the anti-explosion performance of the structure increases with the increase of the explosion equivalent

and the increase in the improvement effect becomes more obvious as the proportional distance decreases.The calculated result of the machine learning prediction model shows that the support vector machine model and extreme gradient boosting model can predict the maximum peak displacement of the frame structure

providing a powerful means for rapidly evaluating the damage effect of RC frame-masonry infill wall structure.

关键词

Keywords

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