INTELLIGENT MODELS in geotechnical engineering
INTELLIGENT MODELS in geotechnical engineering
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The objective of this book is to develop intelligent models [geostatistic, artificial neural network (ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The present book also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this book, liquefaction susceptibility is evaluated from Standard Penetration Test (SPT), Cone Penetration Test (CPT) and Shear Wave (Vs) data using ANN and SVM...
The objective of this book is to develop intelligent models [geostatistic, artificial neural network (ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The present book also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this book, liquefaction susceptibility is evaluated from Standard Penetration Test (SPT), Cone Penetration Test (CPT) and Shear Wave (Vs) data using ANN and SVM. In this book, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. SVM model outperforms all the available methods for OCR prediction.
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