Journal ArticleDOI
A support vector regression model for predicting tunnel boring machine penetration rates
TLDR
The proposed regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR) is said to be a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists.About:
This article is published in International Journal of Rock Mechanics and Mining Sciences.The article was published on 2014-12-01. It has received 181 citations till now.read more
Citations
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Journal ArticleDOI
State-of-the-art review of soft computing applications in underground excavations
Wengang Zhang,Runhong Zhang,Chongzhi Wu,Anthony T. C. Goh,Suzanne Lacasse,Zhongqiang Liu,Hanlong Liu +6 more
TL;DR: An overview of some soft computing techniques as well as their applications in underground excavations is presented and a case study is adopted to compare the predictive performances ofsoft computing techniques including eXtreme Gradient Boosting, Multivariate Adaptive Regression Splines, and Support Vector Machine in estimating the maximum lateral wall deflection induced by braced excavation.
Journal ArticleDOI
Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
Danial Jahed Armaghani,Edy Tonnizam Mohamad,Mogana Sundaram Narayanasamy,Nobuya Narita,Saffet Yagiz +4 more
TL;DR: In this article, the authors developed new intelligent prediction models for estimating the tunnel boring machine performance (TBM) by means of the rate pf penetration (PR) of the Pahang-Selangor Raw Water Transfer (PSRWT) tunnel in Malaysia.
Journal ArticleDOI
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
Jian Zhou,Yingui Qiu,Shuangli Zhu,Danial Jahed Armaghani,Chuanqi Li,Hoang Nguyen,Saffet Yagiz +6 more
TL;DR: Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models, confirming that this hybrid S VM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.
Journal ArticleDOI
Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
TL;DR: KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study, and it can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR.
Journal ArticleDOI
Recurrent neural networks for real-time prediction of TBM operating parameters
TL;DR: The feasibility of RNNs for the real-time prediction of TBM operating parameters indicates thatRNNs can afford the analysis and the forecasting of the time-continuous in-situ data collected from various construction equipments.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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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?
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Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
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Prediction of hard rock TBM penetration rate using particle swarm optimization
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