An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)-Reference-Cited by-同舟云学术

An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)

Published:2018-10-15 Issue:6 Volume:78 Page:4579-4588
ISSN:1435-9529
Container-title:Bulletin of Engineering Geology and the Environment
language:en
Short-container-title:Bull Eng Geol Environ

Author:

Ghaderi Abdolvahed,Abbaszadeh Shahri Abbas,Larsson Stefan

Funder

Royal Institute of Technology

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Link

http://link.springer.com/content/pdf/10.1007/s10064-018-1400-9.pdf

Reference60 articles.

1. Abbaszadeh Shahri A (2016) An optimized artificial neural network structure to predict clay sensitivity in a high landslide prone area using piezocone penetration test (CPTu) data: a case study in southwest of Sweden. Geotech Geol Eng 34(2):745–758

2. Abbaszadeh Shahri A, Malehmir A, Juhlin C (2015a) Soil classification analysis based on piezocone penetration test data a case study from a quick-clay landslide site in southwestern Sweden. Eng Geol 189:32–47

3. Abbaszadeh Shahri A, Larsson S, Johansson F (2015b) CPT-SPT correlations using artificial neural network approach—a case study in Sweden. Electron J Geotech Eng 20(28):13439–13460

4. Albuquerque VH, Alexandria AR, Cortez PC, Tavares JM (2009) Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E Int 42(7):644–651

5. Arel E (2012) Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data. J Comput Geosci 43:90–100

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