多项式混沌
克里金
替代模型
Sobol序列
参数统计
稳健性(进化)
算法
多项式的
灵敏度(控制系统)
工程类
计算机科学
数学优化
结构工程
数学
蒙特卡罗方法
机器学习
统计
生物化学
基因
电子工程
数学分析
化学
作者
Haotian Zheng,Michael A. Mooney,Marte Gutierrez
标识
DOI:10.1016/j.compgeo.2022.105142
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 surrogate 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI