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Application of Artificial Intelligence to Cluster Soil Behaviour from CPTu Data
In this study, the authors used many artificial intelligence algorithms to cluster soil behaviour from CPTu data, that includes cone resistance (qc), frictional resistance (fs), dynamic pore pressure (u2), corrected cone resistance (qt) and friction ratio (Rf). The soil behavior type is following Robertson (1986). There are four model are built in this study include: i. Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf; ii. Supervised learning with SVM algorithm by qt and Rf; iii. Unsupervised learning with Kmeans algorithm by qt and Rf with three clusters; and Unsupervised learning with Kmeans algorithm by qt and Rf with nine clusters. To satisfy “Imbalanced data” and Roberson’s chart shape, the raw data are being preprocessing within two steps before clustering with KMeans or continue to divide to 3 minor set for SVM algorithm that includes: training set - 50%, validation set - 20% and test set - rest. The result indicated the Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf is the best model, while unsupervised learning with KMeans does not meet the requirements.
Application of Artificial Intelligence to Cluster Soil Behaviour from CPTu Data
In this study, the authors used many artificial intelligence algorithms to cluster soil behaviour from CPTu data, that includes cone resistance (qc), frictional resistance (fs), dynamic pore pressure (u2), corrected cone resistance (qt) and friction ratio (Rf). The soil behavior type is following Robertson (1986). There are four model are built in this study include: i. Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf; ii. Supervised learning with SVM algorithm by qt and Rf; iii. Unsupervised learning with Kmeans algorithm by qt and Rf with three clusters; and Unsupervised learning with Kmeans algorithm by qt and Rf with nine clusters. To satisfy “Imbalanced data” and Roberson’s chart shape, the raw data are being preprocessing within two steps before clustering with KMeans or continue to divide to 3 minor set for SVM algorithm that includes: training set - 50%, validation set - 20% and test set - rest. The result indicated the Supervised learning with SVM algorithm by qc, fs, u2, qt and Rf is the best model, while unsupervised learning with KMeans does not meet the requirements.
Application of Artificial Intelligence to Cluster Soil Behaviour from CPTu Data
Lecture Notes in Civil Engineering
Reddy, J. N. (editor) / Wang, Chien Ming (editor) / Luong, Van Hai (editor) / Le, Anh Tuan (editor) / Phu, Nhat Truyen (author) / Le, Pham Thanh Hieu (author) / Le, Ba Vinh (author) / Vo, Dai Nhat (author)
The International Conference on Sustainable Civil Engineering and Architecture ; 2023 ; Da Nang City, Vietnam
Proceedings of the Third International Conference on Sustainable Civil Engineering and Architecture ; Chapter: 108 ; 1031-1038
2023-12-12
8 pages
Article/Chapter (Book)
Electronic Resource
English
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