Page 61 - 《爆炸与冲击》2026年第11期

第 46 卷 王帅帅，等： 数据驱动的箍筋加密钢筋混凝土柱爆炸损伤预测与评估模型 第 11 期

predict the damage indices of RC columns under explosive loading. To train the model, a comprehensive database comprising
3 133 samples was established by integrating 259 samples collected from the literature and 2 874 supplementary samples
generated through numerical simulations. The database includes both RC columns with uniformly spaced stirrups and those
with densified stirrups satisfying seismic design requirements. Eleven key parameters were selected as input features, with the
RC column damage index taken as the output. Six ML models were employed to predict the damage indices under explosive
loading, and their predictive performance was comparatively evaluated using four regression metrics. The results indicate that
the tabular prior-data fitted network (TabPFN) achieved the best predictive accuracy and generalization performance, with a
coefficient of determination of 0.989 on the test set and a mean absolute error and a root mean square error of 0.018 and 0.030,
respectively. The interpretability of the TabPFN model was further analyzed using SHAP (Shapley additive explanations). The
results show that charge mass, standoff distance, and column cross-sectional depth are the key features governing the damage
severity of RC columns. Notably, the volumetric stirrup reinforcement ratio contributes more significantly to improving the
blast resistance of RC columns than the longitudinal reinforcement ratio. Finally, comparison with independent finite element
simulation results confirmed that the TabPFN model exhibits good predictive stability as a surrogate model under unseen
loading conditions. In terms of computational efficiency, the proposed model achieves a single-prediction inference time of
approximately 0.5 s, representing an efficiency improvement of nearly four orders of magnitude compared with conventional
numerical simulation methods. The established model enables rapid surrogate prediction of RC column damage under explosive
loading and provides guidance for preliminary parameter analysis in blast-resistant structural optimization design.
Keywords: reinforced concrete columns; explosive loading; machine learning; damage assessment

近几十年来，由于恐怖袭击和意外爆炸的严重威胁，工程结构的损伤评估受到了广泛的关注。钢筋
混凝土（reinforced concrete，RC）柱是工程结构中的核心承重构件，在爆炸荷载作用下可能发生破坏，甚至
引起结构的整体倒塌 [1-3] 。快速、可靠地量化 RC 柱的损伤程度，不仅是判断结构整体安全性的关键手
段，更是支撑灾后应急救援与修复决策的迫切需求。因此，探索爆炸荷载作用下 RC 柱损伤的快速、准
确预测方法，对于提升重要基础设施的抗爆性能及灾后快速评估具有重要意义。
目前，针对 RC 柱在爆炸冲击环境下的动力响应及失效演化机制，主要通过理论分析、爆炸试验
及数值模拟这 3 种途径展开探讨。其中，理论分析通过构建特定边界下的运动微分方程并求取精确解，
凭借其变量简练、运算高效等显著优势，被应用于大规模 RC 柱损伤的快速预估中。Li 等 [4] 利用量纲分
析法推导了基于等效单自由度模型的无量纲 p-I 曲线（其中 p 和 I 分别代表无量纲化后的超压与冲量），
并通过建立最大位移与破坏准则的关联，实现了对结构损伤程度的快速评估。Yu 等 [5] 采用 Timoshenko
梁理论，分别定义了确定 RC 柱典型弯曲破坏、斜向破坏和直剪破坏模式的破坏准则，编制了有效程
序，引入了改进的非线性循环法来拟合 p-I 曲线，实现了对 RC 柱在爆炸荷载作用下破坏模式的快速评
估。尽管理论解析在计算效能上具备优势且处于不断完善之中，但面对真实的爆炸冲击环境，其局限
性依然显著。由于实际工程中涉及材料动态本构以及边界约束条件的复杂性，而理论推导往往依赖
于诸多理想化的简化假设，难以精准涵盖这些复杂的物理过程，致使该方法的适用范围和计算精度受到
制约。
爆炸试验通过对 RC 柱实施真实的爆炸加载，不仅能够直接采集到冲击波荷载数据，还能直观地揭
示 RC 柱的最终失效特征。Yuan 等 [6] 开展了 RC 桥墩在接触爆炸下的现场试验，对比分析了圆截面和方
截面柱的动力响应与失效特征，揭示了爆炸载荷下的损伤演化机理。Dua 等 针对不同横截面宽度的矩
[7]
形 RC 柱开展了接触爆炸试验，旨在探究截面尺寸变化对结构动力响应特征的影响规律。Shi 等 [8] 对
RC 柱开展了近距离爆炸荷载和轴向荷载作用的试验研究，探讨了比例距离和轴向荷载对 RC 柱在爆炸
荷载作用下损伤程度的影响，发现轴压比的增大使 RC 柱在一定的比例距离和轴向荷载组合工况下由低
损伤向中损伤转变。尽管实爆测试对于揭示构件失效机理具有不可替代的价值，但受限于高昂成本与
场地条件，难以积累充足样本以满足多工况下的结构损伤量化评估需求。

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