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
Effect of moisture on concrete damage and aggregate recycling under microwave irradiation
TL;DR: In this article, the influence of moisture content on the heating process of concrete as well as its strength under microwave irradiation was explored through uniaxial compression experiment, the compressive strength of test blocks with different moisture contents is gained after microwave-assisted concrete aggregate recycling.
Journal ArticleDOI
Intelligent technologies for construction machinery using data-driven methods
TL;DR: In this article , a wide range of research works reported in the literature which realized few-manned or unattended construction sites were surveyed and three aspects were discussed in detail: PHM applications based on experiment environments, environment perception systems in terms of information mediums, and automation control methods according to training mechanisms.
Journal ArticleDOI
Experimental study on the water retention properties of sulfate saline soils during the cooling process
TL;DR: In this paper , the free water content of sulfate saline soils during cooling process and the amount of salt crystallization were derived based on the law of conservation of mass and the reliability of the derived calculation equation was verified, which laid the foundation for the subsequent processing of the test data for matrix suction determination.
Journal ArticleDOI
Thermo-Hydro-Mechanical Coupling Model of Unsaturated Soil Based on Modified VG Model and Numerical Analysis
TL;DR: Based on the wet-thermal elasticity theory and mixture theory, coupled thermo-hydro-mechanical (THM) equations for moisture migration, heat transfer, and deformation in unsaturated soil are derived as discussed by the authors .
Journal ArticleDOI
Learning from explainable data-driven tunneling graphs: A spatio-temporal graph convolutional network for clogging detection
TL;DR: In this article , an explainable spatiotemporal graph convolutional network for clogging detection is proposed to judge the risk level of clogging for each ring in each ring.
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.