Abstract
Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.
Highlights
The Pohang earthquake (Mw = 5.4) that struck the Heunghae Basin, around Pohang City, on 15 November 2017, had a damaging effect, leading to liquefaction and lateral spreading
Little attention has been paid to the settlement resulting from the liquefaction
This study tried to predict the liquefaction-induced settlement of Pohang by applying a machine learning algorithm to a standard penetration test (SPT) data and proposes a liquefaction settlement chart based on the results
Summary
The Pohang earthquake (Mw = 5.4) that struck the Heunghae Basin, around Pohang City, on 15 November 2017, had a damaging effect, leading to liquefaction and lateral spreading. Several attempts have been made to study the post-earthquake damage [1,2,3,4,5]. This study tried to predict the liquefaction-induced settlement of Pohang by applying a machine learning algorithm to a standard penetration test (SPT) data and proposes a liquefaction settlement chart based on the results. Before constructing a structure on the ground, design is performed based on the ground investigation results. Many sites, including Pohang, have a lot of SPT data. The SPT is a common method to get ground investigation data
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