Learning decision boundaries for cone penetration test classification-Reference-Cited by-同舟云学术

Learning decision boundaries for cone penetration test classification

Published:2021-02-15 Issue:4 Volume:36 Page:489-503
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer‐Aided Civil and Infrastructure Engineering

Author:

Erharter Georg H.¹,Oberhollenzer Simon²,Fankhauser Anna²,Marte Roman²,Marcher Thomas¹

Affiliation:

1. Institute of Rock Mechanics and Tunnelling Graz University of Technology Graz Styria 8010 Austria

2. Institute of Soil Mechanics, Foundation Engineering and Computational Geotechnics Graz University of Technology Rechbauerstraße 12, Graz Styria 8010 Austria

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12662

Reference42 articles.

1. Abadi M. Agarwal A. Barham P. Brevdo E. Chen Z. Citro C. …Zheng X. (2015).TensorFlow: Large‐scale machine learning on heterogeneous systems. Retrieved fromhttps://www.tensorflow.org/

2. Enhanced probabilistic neural network with local decision circles: A robust classifier

3. A dynamic ensemble learning algorithm for neural networks

4. Halden research site: geotechnical characterization of a post glacial silt

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine Learning–Based Organic Soil Classification Using Cone Penetrometer Tests;Journal of Geotechnical and Geoenvironmental Engineering;2024-09

2. Stabilization of Expansive Clays with Basalt Fibers and Prediction of Strength by Machine Learning;Arabian Journal for Science and Engineering;2024-02-19

3. Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques;Computer-Aided Civil and Infrastructure Engineering;2023-11-14

4. Herausforderung Bodenklassifizierung – Möglichkeiten und Vorteile der Drucksondierung;geotechnik;2023-06

5. Locally Specified CPT Soil Classification Based on Machine Learning Techniques;Sustainability;2023-02-06