地连墙变形的准确预测对保障深基坑施工安全至关重要。地连墙变形具有显著的时间和空间特征,传统预测方法在处理多步预测任务时面临稳定性和精度不足的问题。本文提出了一种基于全卷积网络(FCN)的地连墙变形动态更新模型,针对复杂变形数据的时空特征提取和多步动态预测进行了创新设计。所提出的模型通过利用全卷积架构有效保留空间信息,并引入跳跃连接策略增强了网络的特征传递能力,从而提高了模型在多步预测中的稳定性与精度。基于南京地铁11号线某车站基坑的实测数据,验证了所提出模型的有效性。结果表明,模型在捕捉地连墙变形趋势和最大变形位置方面具有显著优势,为复杂时序数据的预测提供了新思路。
Accurate prediction of diaphragm wall deformation is critical for ensuring the safety of deep foundation pit excavation. Diaphragm wall deformation exhibits significant temporal and spatial characteristics, and traditional prediction methods often face problems in stability and accuracy when handling multi-step prediction tasks. This paper proposes a dynamic updating model for diaphragm wall deformation based on a fully convolutional network (FCN), featuring an innovative design for spatiotemporal feature extraction and multi-step dynamic prediction of complex deformation data. The proposed model effectively preserves spatial information through its fully convolutional architecture and enhances feature transmission by incorporating skip connection strategies, thereby improving stability and accuracy in multi-step predictions. The model's effectiveness was validated using measured data from a foundation pit project of a metro station on Nanjing metro line 11. The results demonstrate that the model has significant advantages in capturing diaphragm wall deformation trends and identifying maximum deformation locations, providing a novel insight for the prediction of complex time-series data.
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