Surrogate model for 3D ground and structural deformations in tunneling by the sequential excavation method

被引:12
作者
Zheng, Haotian [1 ]
Mooney, Michael [1 ]
Gutierrez, Marte [1 ]
机构
[1] Colorado Sch Mines, Dept Civil & Environm Engn, 1500 Illinois St, Golden, CO 80401 USA
关键词
Sequential excavation method; Surrogate modeling; 3D finite -difference modeling; Polynomial-Chaos-Kriging; Sensitivity analysis; UNCERTAINTY QUANTIFICATION; SENSITIVITY-ANALYSIS; FINITE-ELEMENT; DESIGN; OPTIMIZATION; SIMULATION; METAMODEL;
D O I
10.1016/j.compgeo.2022.105142
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Ground deformation control is one of the key challenges faced during urban tunneling when using the sequential excavation method (SEM). Three-dimensional (3D) numerical analysis is a powerful and essential tool to assess SEM-induced ground and structural deformations. The main challenge in numerical modeling is the uncertainties in data needed to build a model. Various analyses are needed, such as parametric and back-analysis. However, such analyses require thousands to millions of repeated model evaluations making 3D numerical simulations expensive. One technique to make the modeling more manageable is reducing the model size by using a sur-rogate model that captures the main elements of a full 3D model. This paper examines the capability of four surrogate modeling methods, namely the Polynomial Chaos Expansion (PCE), Kriging, sequential Polynomial-Chaos-Kriging (PCK-SEQ), and optimal Polynomial-Chaos-Kriging (PCK-OPT), to accurately and efficiently capture the ground and structural deformations induced by SEM tunneling. A 3D finite-difference model using FLAC3D was developed to simulate an actual SEM project's excavation and initial support process. A sensitivity analysis was performed to determine the most influential geotechnical input parameters. The Sobol sequence sampling strategy was utilized to conduct a design of experiments. The overall and individual output accuracy of surrogate models, the robustness of training and testing, and the accuracy distribution throughout the input parameter space were evaluated and compared.
引用
收藏
页数:16
相关论文
共 69 条
[11]   Polynomial chaos expansion for sensitivity analysis [J].
Crestaux, Thierry ;
Le Maitre, Olivier ;
Martinez, Jean-Marc .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2009, 94 (07) :1161-1172
[12]   Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment [J].
Dey, S. ;
Mukhopadhyay, T. ;
Adhikari, S. .
COMPOSITE STRUCTURES, 2017, 171 :227-250
[13]  
Forrester A., 2008, ENG DESIGN VIA SURRO, DOI DOI 10.1002/9780470770801
[14]   Probabilistic assessment of tunneling-induced building damage [J].
Franco, Victor H. ;
Gitirana Jr, Gilson de F. N. ;
de Assis, Andre P. .
COMPUTERS AND GEOTECHNICS, 2019, 113
[15]   State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging [J].
Fuhg, Jan N. ;
Fau, Amelie ;
Nackenhorst, Udo .
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2021, 28 (04) :2689-2747
[16]   A simple estimation model for 3D braced excavation wall deflection [J].
Goh, A. T. C. ;
Zhang Fan ;
Zhang Wengang ;
Zhang Yanmei ;
Liu Hanlong .
COMPUTERS AND GEOTECHNICS, 2017, 83 :106-113
[17]   A REVIEW OF TECHNIQUES FOR PARAMETER SENSITIVITY ANALYSIS OF ENVIRONMENTAL-MODELS [J].
HAMBY, DM .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 1994, 32 (02) :135-154
[18]   Completely derandomized self-adaptation in evolution strategies [J].
Hansen, N ;
Ostermeier, A .
EVOLUTIONARY COMPUTATION, 2001, 9 (02) :159-195
[19]  
Herranz Carlos, 2016, Geotechnical and Structural Engineering Congress 2016, P2043
[20]   Global optimization of benchmark aerodynamic cases using physics-based surrogate models [J].
Iuliano, Emiliano .
AEROSPACE SCIENCE AND TECHNOLOGY, 2017, 67 :273-286