Artificial intelligence design charts for predicting friction capacity of driven pile in clay
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3555-5
Abstract: In this study, five nonlinear prediction tools are used to model and predict the friction capacity of driven piles installed in clay including classical support vector machine (SVM) and two of its variants, namely regularized… read more here.
Keywords: friction; friction capacity; model; clay ... See more keywords
Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine
Sign Up to like & getrecommendations! Published in 2018 at "Advances in Civil Engineering"
DOI: 10.1155/2018/6490169
Abstract: This research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (ST-LSSVM), to accurately model the friction capacity of driven piles in cohesive soil. The hybrid approach uses LS-SVM as a… read more here.
Keywords: machine; capacity; friction; friction capacity ... See more keywords