A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering
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TLDR
The proposed technique combines the capacity of a process-oriented 3D simulation model for mechanized tunnelling with the computational efficiency of surrogate (or meta) models based on artificial neural networks to accurately describe the complex geological and mechanical interactions of the Tunnelling process.About:
This article is published in Tunnelling and Underground Space Technology.The article was published on 2017-03-01 and is currently open access. It has received 46 citations till now. The article focuses on the topics: Surrogate model & Computational steering.read more
Citations
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Journal ArticleDOI
Enhancing the Practicality of Tools to Estimate the Whole Life Embodied Carbon of Building Structures via Machine Learning Models
TL;DR: In this article, a real-time decision support tool for building design that leverages machine learning (ML) methods from computer science to speed up the computationally expensive process of finite element analysis (FEA) is presented.
Book ChapterDOI
Einsatzmöglichkeiten von Building Information Modelling (BIM) in der Planungs‐und Bauphase im maschinellen Schildvortrieb
Journal ArticleDOI
A TIM-based Method of Automatic Generation of FE Models for Pit Engineering
TL;DR: This paper presents a TIM (Tunnel Information Modeling)-based on digital solution, which is successfully applied in the pit project of Haikou Wenmingdong Tunnel, shortens the modeling time, and improves the modeling quality and the integration level of TIM/FEM.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Journal ArticleDOI
Soil mechanics and plastic analysis or limit design
D. C. Drucker,W. Prager +1 more
Journal ArticleDOI
Characterization of geotechnical variability
Kok-Kwang Phoon,Fred H. Kulhawy +1 more
TL;DR: In this paper, the three primary sources of geotechnical uncertainties are inherent variability, measurem, and measurem uncertainties, and the three main sources of variability are measurem and inherent variability.
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