The continuous monitoring of long-term performance of tunnels constructed in soft rock masses shows that the rock mass deformations continue after construction, albeit at a rate that reduces with time. This is in contrast with NATM postulates which assume deformation stabilizes shortly after tunnel construction. This paper proposes the prediction of long-term vertical settlement performance of a tunnel in soft rock mass, through the inclusion of a Burger’s creep viscous-plastic constitutive law to model post-construction deformations. To overcome issues related to the complex characterization of this constitutive model, a neural network NetRHEO is developed and trained on a numerically obtained dataset. A particle swarm algorithm is then employed to estimate the most probable rheological parameter set, by utilizing the long-term in-situ monitoring data from several observation points on a real tunnel. The paper demonstrates the potential of the proposed methodology, using displacement measurements of two adjacent tunnels in karstic rock mass in Croatia. The complex interaction of a railway tunnel Brajdica and a road tunnel Pećine, conditioned by the character of the surrounding rock mass as well by the chronology of their construction, was evaluated to predict the future behavior of these tunnels.

对软岩体中隧道的长期性能进行连续监测表明，岩体变形在施工后仍在继续，尽管其速率随时间降低。这与NATM假设相反，后者假设在隧道施工后不久变形就稳定了。本文通过包含伯格蠕变粘塑性本构模型来模拟软岩体中隧道的长期竖向沉降性能，从而对施工后的变形进行建模。为了克服与本构模型的复杂表征相关的问题，开发了神经网络NetRHEO并在数值获得的数据集上进行了训练。然后采用粒子群算法估算最可能的流变参数集，利用真实隧道中几个观测点的长期现场监测数据。本文利用克罗地亚岩溶岩体中两个相邻隧道的位移测量结果证明了该方法的潜力。评价了铁路隧道Brajdica和公路隧道Pećine的复杂相互作用，该相互作用受周围岩体的特性以及其建造年代的影响，可以预测这些隧道的未来行为。