Numerical study of the effects of groundwater drawdown on ground settlement for excavation in residual soils

被引:99
|
作者
Goh, A. T. C. [3 ]
Zhang, R. H. [1 ,2 ]
Wang, W. [2 ]
Wang, L. [2 ]
Liu, H. L. [1 ,2 ]
Zhang, W. G. [1 ,2 ]
机构
[1] Chongqing Univ, Key Lab New Technol Construct Cities Mt Area, Chongqing, Peoples R China
[2] Chongqing Univ, Sch Civil Engn, Chongqing, Peoples R China
[3] Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore, Singapore
基金
中国博士后科学基金;
关键词
Braced excavation; Finite element analysis; Ground settlements; Groundwater drawdown; Neural networks; Residual soils; ARTIFICIAL NEURAL-NETWORK; BRACED EXCAVATION; PREDICTION; PARAMETERS; PERFORMANCE; ALGORITHMS; STABILITY; MODEL; WALL;
D O I
10.1007/s11440-019-00843-5
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
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 N-60 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.
引用
收藏
页码:1259 / 1272
页数:14
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