Page 258 - 《爆炸与冲击》2026年第5期

第 46 卷 胡倩然，等： 基于人工神经网络的居民住宅燃气爆炸后果预测 第 5 期

的增大与空间布局的复杂化，最大超压与温度均呈递增趋势。客厅因空间开阔、泄压条件良好，始终表
现为最低水平超压；而卧室内未设窗口的墙体附近则易形成超压与温度的极值区域。此外，厨房和卧室
点火分别可导致室内产生最严重的超压和温度后果，反映出点火位置对爆炸后果的差异化影响规律。
本文中预测模型，属于机器学习在气体爆炸灾害预测领域中的初步探索，为具有典型分隔结构的居
民住宅爆炸后果快速评估提供了有效工具。为提升模型的泛化能力与工程普适性，未来工作应着重于：
引入可以量化障碍物分布与泄压条件的建筑拓扑参数作为显式输入，并构建涵盖更广泛户型的大规模
数据集，以驱动更精准、鲁棒性更强的智能预测模型发展。

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