华中科技大学学报（自然科学版）2026，Vol.54Issue(8)：85-91,101,8.DOI:10.13245/j.hust.250447
基于迁移学习的GCN-Transformer-DANN盾构隧道地表沉降预测模型
Surface settlement prediction model for shield tunnels based on GCN-Transformer-DANN with transfer learning
摘要
Abstract
To address the issue of insufficient prediction accuracy in shield tunnel surface settlement caused by variations in geological and working conditions,a GCN-Transformer-DANN settlement prediction model based on transfer learning was proposed.The model constructs a spatiotemporal feature extractor centered on graph convolutional networks(GCN)and Transformer,mining subsidence features from both source and target domains.A domain-adversarial neural network(DANN)mechanism was introduced to learn domain-invariant features through an adversarial game between the feature extractor and the domain discriminator.Additionally,multiple kernel maximum mean discrepancy and joint maximum mean discrepancy were employed to align shallow parameters and deep semantic distributions,respectively,thereby mitigating domain shift.Validation was conducted based on three sections with significant geological differences along Beijing Subway Line 17.The results show that under the few-shot scenario where target domain training samples account for only 20％,the model's predicted mean absolute error decreases by approximately 40％compared to the baseline model without transfer learning strategy,exhibiting stable error reduction across all validation intervals and improving cross-domain prediction accuracy.关键词
盾构隧道/地表沉降/迁移学习/预测模型/GCN-Transformer-DANNKey words
shield tunnel/surface settlement/transfer learning/prediction model/GCN-Transformer-DANN分类
建筑与水利引用本文复制引用
舒佳军,李涛,陈前..基于迁移学习的GCN-Transformer-DANN盾构隧道地表沉降预测模型[J].华中科技大学学报（自然科学版）,2026,54(8):85-91,101,8.基金项目
新疆维吾尔自治区重大科技专项(2024A01003) （2024A01003）
国家自然科学基金联合基金重点支持项目(U24B2039) （U24B2039）
中国矿业大学(北京)内蒙古研究院基金资助项目(IMRI23009) （北京）
中央高校基本科研业务费专项资金资助项目(2023ZKPYLJ01) （2023ZKPYLJ01）
贵州省黔科合支撑一般项目(2025-344). （2025-344）
