基于时空特性和深度学习的地铁基坑周边建筑物沉降估算模型

doi:10.3724/1000-6915.jrme.2023.1095

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摘要地铁基坑开挖必然引发周边地表和建筑物沉降，准确、实时的地表和建筑物沉降预警至关重要。为深入挖掘地铁基坑引发的周边建筑物沉降的时空特性并提高建筑物沉降估算精度，基于深度学习提出一种建筑物沉降估算的卷积门控循环单元神经网络(ConvGRU)模型。根据目标监测点及其邻近监测点的建筑物沉降数据，构造建筑物沉降时空矩阵。使用卷积神经网络捕捉建筑物沉降数据的空间特性，利用门控循环单元神经网络捕捉建筑物沉降数据的时间特性，从而分析建筑物沉降数据的时空特性。选取深圳地铁基坑周边建筑物的沉降数据，对比ConvGRU模型与历史平均模型以及4种现有深度学习模型的估算性能。结果显示，ConvGRU模型的平均估算误差相比5种现有模型降低12.86%～39.00%。研究表明，ConvGRU模型的准确性较高且泛化性较好，可为地铁建设提供高质量的周边建筑物沉降估算数据，进而做好地铁基坑周边地表和建筑物沉降预警。

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	姚荣涵1
	张文松2
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	贾磊2
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	4
	王立冰1

关键词 ：基础工程, 地铁基坑, 建筑物沉降, 深度学习, 时空特性, 组合模型

Abstract：The excavation of subway foundation pits inevitably leads to the settlement of the surrounding ground surface and buildings. Therefore，real-time and accurate monitoring of ground surface and building settlement is crucial. To thoroughly investigate the spatiotemporal characteristics of building settlement resulting from subway foundation pit excavations and enhance the precision of settlement estimations，a convolutional gated recurrent unit neural network(ConvGRU) model for the building settlement estimation was proposed based on deep learning. The spatiotemporal matrix of the building settlement was built using the building settlement data obtained from the subject and adjacent monitoring points. The convolutional neural network(CNN) was employed to capture the spatial patterns of the building settlement data，while the gated recurrent unit(GRU) neural network was utilized to extract the temporal patterns. This dual approach allows for a comprehensive analysis of the spatiotemporal characteristics of the building settlement data. The estimation performance of the ConvGRU model was compared with that of the history average model and those of four existing deep learning models using the data of the building settlement around a subway foundation pit in Shenzhen city of Guangdong Province of China. The results indicate that the estimation error of the ConvGRU model is reduced by 12.86% to 39.00% when compared to the five existing models. This research demonstrates that the ConvGRU model achieves higher accuracy and better generalization，providing high-quality building settlement estimation data for subway construction and serving as a warning mechanism for ground surface and building settlement associated with subway foundation pits.

Key words： foundation engineering subway foundation pit building settlement deep learning spatiotemporal characteristics hybrid model

引用本文:

姚荣涵1，张文松2，3，4，贾磊2，3，4，王立冰1. 基于时空特性和深度学习的地铁基坑周边建筑物沉降估算模型[J]. 岩石力学与工程学报, 2025, 44(S1): 196-205.
YAO Ronghan1，ZHANG Wensong2，3，4，JIA Lei2，3，4，WANG Libing1. Estimation model for building settlement around subway foundation pit based on spatiotemporal characteristics and deep learning. , 2025, 44(S1): 196-205.

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https://rockmech.whrsm.ac.cn/CN/10.3724/1000-6915.jrme.2023.1095 或 https://rockmech.whrsm.ac.cn/CN/Y2025/V44/IS1/196

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