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
2016 (English)In: Geotechnical and Geological Engineering, ISSN 0960-3182, E-ISSN 1573-1529, 1-14 p.Article in journal (Refereed) PublishedText
Application of artificial neural networks (ANN) in various aspects of geotechnical engineering problems such as site characterization due to have difficulty to solve or interrupt through conventional approaches has demonstrated some degree of success. In the current paper a developed and optimized five layer feed-forward back-propagation neural network with 4-4-4-3-1 topology, network error of 0.00201 and R2 = 0.941 under the conjugate gradient descent ANN training algorithm was introduce to predict the clay sensitivity parameter in a specified area in southwest of Sweden. The close relation of this parameter to occurred landslides in Sweden was the main reason why this study is focused on. For this purpose, the information of 70 piezocone penetration test (CPTu) points was used to model the variations of clay sensitivity and the influences of direct or indirect related parameters to CPTu has been taken into account and discussed in detail. Applied operation process to find the optimized ANN model using various training algorithms as well as different activation functions was the main advantage of this paper. The performance and feasibility of proposed optimized model has been examined and evaluated using various statistical and analytical criteria as well as regression analyses and then compared to in situ field tests and laboratory investigation results. The sensitivity analysis of this study showed that the depth and pore pressure are the two most and cone tip resistance is the least effective factor on prediction of clay sensitivity.
Place, publisher, year, edition, pages
Springer, 2016. 1-14 p.
Artificial neural network model, Clay sensitivity, Landslide, Piezocone penetration test, Backpropagation, Backpropagation algorithms, Forecasting, Geotechnical engineering, Landslides, Neural networks, Optimization, Regression analysis, Soil testing, Artificial neural network modeling, Conjugate gradient descents, Feed-forward back-propagation neural networks, Laboratory investigations, Landslide-prone areas, Piezocone penetration tests, Site characterization, Sensitivity analysis
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:kth:diva-188334DOI: 10.1007/s10706-016-9976-yScopusID: 2-s2.0-84961285924OAI: oai:DiVA.org:kth-188334DiVA: diva2:934833
QC 201606092016-06-092016-06-092016-06-09Bibliographically approved