对采用人工神经网络技术预测隧道地表沉降模型中进行了研究。
This paper researches a method to predict tunneling-induced ground subsidence with artificial neural network.
地表沉降是判断浅埋隧道地层稳定的一个重要指标。
Ground surface settlement is an important factor to evaluate surrounding rock stability of shallow tunnel.
软土隧道地表长期沉降特性与隧道的渗透性以及周围土体的时效特性密切相关。
The drainage condition combining with the time-dependent properties of the surrounding soil played an important role in the evolution of long-term surface settlement over tunnels in soft soils.
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