Machine learning modelling for predicting soil liquefaction susceptibility

P Samui,T G Sitharam

doi:10.5194/nhess-11-1-2011

Abstract

Abstract. This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Highlights

Liquefaction is a phenomenon whereby a granular material transforms from a solid state to a liquefied state of the increase in pore water pressure
A procedure based on standard penetration test (SPT) and cyclic stress ratio (CSR) was developed by Seed and his colleagues (1967, 1971, 1983, 1984) based on the use of peck ground acceleration (PGA = amax/g) to assess the liquefaction potential of soil, and is in standard use around the world
Two machine learning techniques (ANN and support vector machine, Support Vector machine (SVM)) have been adopted to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake

Summary

Introduction

Liquefaction is a phenomenon whereby a granular material transforms from a solid state to a liquefied state of the increase in pore water pressure. There are three types of damage occurring during liquefaction. Second is that sand blows and ground cracks are the surface manifestations of liquefaction in soil. The Damages attributed to the earthquake-induced liquefaction phenomenon have cost society hundreds of millions of US dollars (Seed and Idriss, 1982). The liquefaction susceptibility of soil depends on the earthquake parameter and soil parameter. A procedure based on standard penetration test (SPT) and cyclic stress ratio (CSR) was developed by Seed and his colleagues (1967, 1971, 1983, 1984) based on the use of peck ground acceleration (PGA = amax/g) to assess the liquefaction potential of soil, and is in standard use around the world. Goh (1994) successfully applied Artificial Neural Network (ANN) for the determination of liquefaction susceptibility of soil. ANN models have some limitations such as the black box approach, arriving at local minima, slow convergence speed and over fitting problems (Park and Rilett, 1999; Kecman, 2001)

Methods

Results

Conclusion