For deep excavations in residual soils that are underlain by highly fissured or fractured rocks, it is common to observe the drawdown of the groundwater table behind the excavation, resulting in seepage-induced ground settlement. In this study, finite element analyses are firstly performed to assess the critical parameters that influence the ground settlement performance in residual soil deposits subjected to groundwater drawdown. The critical parameters that influence the ground settlement performance were identified as the excavation width, the excavation depth, the depth of groundwater drawdown, the thickness of the residual soil, the average SPT N₆₀ value of the residual soil, the location of the moderately weathered rock, and the wall system stiffness. Subsequently, an artificial neural network (ANN) model was developed to provide estimates of the maximum ground settlement. Validation of the performance of ANN model was carried out using additional data derived from finite element analyses as well as with measured data from a number of excavation sites.

中文翻译：

---

地下水位下降对剩余土开挖地面沉降影响的数值研究

对于在高度裂隙或破碎的岩石下面的残留土壤中的深基坑，通常要观察开挖后地下水位的下降，从而导致渗漏引起的地面沉降。在这项研究中，首先进行了有限元分析，以评估影响地下水沉降的残余土壤沉积物中影响地面沉降性能的关键参数。确定了影响地面沉降性能的关键参数，包括开挖宽度，开挖深度，地下水渗入深度，残余土壤厚度，平均SPT N ₆₀剩余土壤的价值，中等风化岩石的位置以及墙体系统的刚度。随后，开发了人工神经网络（ANN）模型以提供最大地面沉降的估计值。ANN模型性能的验证使用了来自有限元分析的附加数据以及来自多个挖掘现场的实测数据。