盾构法隧道施工地表变形的小样本智能预测

Intelligent prediction of ground deformation by small samples in the shield tunnel construction

以盾构施工监测所得地表位移资料为学习样本,通过选择合适的人工智能神经网络结构及其相关参数建立预测模型,在小样本训练的情况下,预测下一步后续施工的地表变形位移。通过时间窗滚动多步预测,可以获得盾构工作面前、后方测点在近数日之内任意一天的地表变形位移,并自动绘制盾构推进中沿隧道纵轴方向的隆/沉历时曲线。以上海市地铁明珠二期南浦大桥站附近交叠隧道上行区间盾构推进施工期间的工程实例验证,表明按本文建议方法的预测结果与实际的施工监测值吻合良好。

Through learning from the observation of ground deformation in shield tunnel construction, the prediction model was established by choosing a suitable structure of ANN and relevant parameters. Small samples was trained for predicting behavior during subsequent construction. By using multi-step scrolling technology, the curve of ground deformation in recent days was obtained. The curves were drawn to reflect hogging or sagging condition in the tunnel direction. The method was used to the regional tunnel between Pudong South Station and Nanpu Bridge Station of the second project of rail transit “Bright Pearl Line” in Shanghai. It shows that the predicted results are in well accordance with the field observations according to the suggested method.