Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization
https://orcid.org/http://orcid.org/0000-0002-0597-3763
Liquefaction-induced settlement in saturated loose soils is a commonly geotechnical phenomenon resulted from the loss of its shear strength under moderate or high seismicity. This can have detrimental effects on building, infrastructure, and even human life. Therefore, predicting the settlement of ground caused by liquefaction plays a critical role in the geotechnical field. As the limited data for liquefaction-induced settlement obtained, an effective method should be applied to provide highly accurate prediction. The aim of this study is to propose a machine learning approach, namely multilayer perceptron (MLP), with a wide range of hyperparameters optimized by the Bayesian optimization method to predict liquefaction-induced settlement due to the Pohang earthquake in South Korea. The ground settlement and its correlation with different soil conditions were taken into account including unit weight, soil layer depth, standard penetration test blow count, and cyclic stress ratio. Moreover, other five well-known models, namely linear regression, support vector machine, robust regression, and polynomial regression, were performed to facilitate a comparison study. The experimental result indicates that while the high influence of all considerable variables on the ground settlement was obtained from the correlation coefficients, the soil layer depth factor had the highest one. Based on the Bayesian optimization, a greater prediction accuracy was observed from the proposed MLP model compared to other methods by using the evaluation metric of R-squared value. Furthermore, the developed model outperforms other machine learning methods proposed by previous studies in terms of predicting the soil settlement caused by liquefaction.
Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization
https://orcid.org/http://orcid.org/0000-0002-0597-3763
Liquefaction-induced settlement in saturated loose soils is a commonly geotechnical phenomenon resulted from the loss of its shear strength under moderate or high seismicity. This can have detrimental effects on building, infrastructure, and even human life. Therefore, predicting the settlement of ground caused by liquefaction plays a critical role in the geotechnical field. As the limited data for liquefaction-induced settlement obtained, an effective method should be applied to provide highly accurate prediction. The aim of this study is to propose a machine learning approach, namely multilayer perceptron (MLP), with a wide range of hyperparameters optimized by the Bayesian optimization method to predict liquefaction-induced settlement due to the Pohang earthquake in South Korea. The ground settlement and its correlation with different soil conditions were taken into account including unit weight, soil layer depth, standard penetration test blow count, and cyclic stress ratio. Moreover, other five well-known models, namely linear regression, support vector machine, robust regression, and polynomial regression, were performed to facilitate a comparison study. The experimental result indicates that while the high influence of all considerable variables on the ground settlement was obtained from the correlation coefficients, the soil layer depth factor had the highest one. Based on the Bayesian optimization, a greater prediction accuracy was observed from the proposed MLP model compared to other methods by using the evaluation metric of R-squared value. Furthermore, the developed model outperforms other machine learning methods proposed by previous studies in terms of predicting the soil settlement caused by liquefaction.
Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization
https://orcid.org/http://orcid.org/0000-0002-0597-3763
Liquefaction-induced settlement in saturated loose soils is a commonly geotechnical phenomenon resulted from the loss of its shear strength under moderate or high seismicity. This can have detrimental effects on building, infrastructure, and even human life. Therefore, predicting the settlement of ground caused by liquefaction plays a critical role in the geotechnical field. As the limited data for liquefaction-induced settlement obtained, an effective method should be applied to provide highly accurate prediction. The aim of this study is to propose a machine learning approach, namely multilayer perceptron (MLP), with a wide range of hyperparameters optimized by the Bayesian optimization method to predict liquefaction-induced settlement due to the Pohang earthquake in South Korea. The ground settlement and its correlation with different soil conditions were taken into account including unit weight, soil layer depth, standard penetration test blow count, and cyclic stress ratio. Moreover, other five well-known models, namely linear regression, support vector machine, robust regression, and polynomial regression, were performed to facilitate a comparison study. The experimental result indicates that while the high influence of all considerable variables on the ground settlement was obtained from the correlation coefficients, the soil layer depth factor had the highest one. Based on the Bayesian optimization, a greater prediction accuracy was observed from the proposed MLP model compared to other methods by using the evaluation metric of R-squared value. Furthermore, the developed model outperforms other machine learning methods proposed by previous studies in terms of predicting the soil settlement caused by liquefaction.
Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization
Indian Geotech J
Van Nguyen, Nhi (Autor:in) / Van Le, Luong (Autor:in) / Nguyen, Tan-No (Autor:in) / Park, Sung-Sik (Autor:in) / Tran, Thanh Danh (Autor:in)
Indian Geotechnical Journal ; 54 ; 2443-2453
01.12.2024
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Prediction of Liquefied Soil Settlement Using Multilayer Perceptron with Bayesian Optimization
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