Journal ArticleDOI
Pipejacking clogging detection in soft alluvial deposits using machine learning algorithms
Reads0
Chats0
TLDR
A baseline assessment of clogging in slurry-supported pipejacking is performed using a combination of TBM parameters and semi-empirical diagrams proposed in the literature, and the potential for one-class support vector machines (OCSVM), isolation forest and robust covariance (Robcov) to assess the tendency for clogging is explored.About:
This article is published in Tunnelling and Underground Space Technology.The article was published on 2021-07-01. It has received 34 citations till now. The article focuses on the topics: Clogging.read more
Citations
More filters
Journal ArticleDOI
Discrete Element Simulation and Monitoring Analysis of Different Construction Methods of the Shallow Buried Bias Tunnel
TL;DR: In this article , the authors used the discrete element method to simulate the construction steps of the three-step method and the single side heading method with and without systematic bolt supports taking Qijiazhuang tunnel as the research object.
Journal ArticleDOI
Dynamic cooperation and mutual feedback network for shield machine
TL;DR: In this paper , a dynamic cooperation and mutual feedback network based on the Internet of Things (IoT) is proposed to integrate shield machines, geological information, and control terminals for rate prediction and anomaly detection.
Journal ArticleDOI
Dynamic Interactional And Cooperative Network For Shield Machine
TL;DR: Wang et al. as mentioned in this paper investigated the relationship among shield machines, geological information, and control terminals, and established models for the control terminal tasks, including SM rate prediction and SM anomaly detection.
Journal ArticleDOI
Researches on the Excavation Disturbance of Shield Tunnel in Sandy Cobble Ground
Wei Wang,Jingjing Liu,Jian Chen +2 more
TL;DR: In this article , the authors carried out field tests based on the project of Luoyang urban rail transit and provided a calculation method for predicting the soil disturbance in the earth pressure balance shield construction in a sandy cobble ground.
References
More filters
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Williamson, estimating the support of a high-dimensional distribution
TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
Journal ArticleDOI
Estimating the Support of a High-Dimensional Distribution
TL;DR: In this paper, the authors propose a method to estimate a function f that is positive on S and negative on the complement of S. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space.