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Spatiotemporal deep learning for multi-attribute prediction of excavation-induced risk
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105964
Yue Pan, Wen He, Jin-Jian Chen

This paper presents a hybrid deep learning model named the Online Learning-based Multi-Attribute Spatial-Temporal Transformer Network (OMSTTN) to predict excavation-induced risks during foundation pit excavation. OMSTTN integrates a hybrid Transformer offline model with a parallel embedding layer to process diverse monitoring attributes and employs a Spatial-Temporal Transformer block to capture complex spatiotemporal correlations. An online learning mechanism enables dynamic adaptation to evolving conditions, enhancing prediction accuracy. Validated on a real-world XuZhou Rail Transit project, OMSTTN achieves strong prediction performance (MAE: 0.0461, RMSE: 0.0699, R2: 0.9441). Comparative experiments demonstrate its effectiveness in handling multi-attribute data, dynamic changes, and spatiotemporal patterns. In short, OMSTTN narrows the research gap by providing a spatiotemporal framework for accurate risk prediction, offering significant potential for early risk detection and proactive management in excavation engineering.

中文翻译:


用于挖掘诱发风险多属性预测的时空深度学习



本文提出了一种名为基于在线学习的多属性时空转换器网络 (OMSTTN) 的混合深度学习模型,用于预测基坑开挖过程中挖掘引起的风险。OMSTTN 将混合 Transformer 离线模型与并行嵌入层集成在一起,以处理不同的监控属性,并采用 Spacetial-Temporal Transformer 模块来捕获复杂的时空相关性。在线学习机制能够动态适应不断变化的条件,从而提高预测准确性。在徐州轨道交通的实际项目中进行了验证,OMSTTN 实现了强大的预测性能(MAE:0.0461,RMSE:0.0699,R2:0.9441)。比较实验证明了它在处理多属性数据、动态变化和时空模式方面的有效性。简而言之,OMSTTN 通过提供准确风险预测的时空框架来缩小研究差距,为挖掘工程的早期风险检测和主动管理提供了巨大的潜力。
更新日期:2025-01-17
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