A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

Park, Sung-Sik; Ogunjinmi, Peter D.; Woo, Seung-Wook; Lee, Dong-Eun

doi:10.3390/su12104001

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A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

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Department of Civil Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea

Department of Architectural Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea

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Sustainability 2020, 12(10), 4001; https://doi.org/10.3390/su12104001

Received: 8 April 2020 / Revised: 9 May 2020 / Accepted: 9 May 2020 / Published: 13 May 2020

(This article belongs to the Special Issue Technology and Management for Sustainable Buildings and Infrastructures)

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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. View Full-Text

Keywords: settlement; artificial neural network; liquefaction

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Park, S.-S.; Ogunjinmi, P.D.; Woo, S.-W.; Lee, D.-E. A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network. Sustainability 2020, 12, 4001. https://doi.org/10.3390/su12104001

AMA Style

Park S-S, Ogunjinmi PD, Woo S-W, Lee D-E. A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network. Sustainability. 2020; 12(10):4001. https://doi.org/10.3390/su12104001

Chicago/Turabian Style

Park, Sung-Sik, Peter D. Ogunjinmi, Seung-Wook Woo, and Dong-Eun Lee. 2020. "A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network" Sustainability 12, no. 10: 4001. https://doi.org/10.3390/su12104001

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A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

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