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Prediction of Liquefaction-Induced Settlement Using Artificial Neural Network

Hoang, Dung V. / Bui, Phuoc T. H. / Phan, An T. T. / Nguyen, Tan-No

This study aims to propose a machine-learning algorithm for predicting the ground settlement caused by liquefaction. An artificial neural network (ANN) approach was used. The properties of soil layers, namely unit weight (γ), soil layer depth (d), standard penetration test blow count (N₁₍₆₀₎), cyclic stress ratio (CSR), and corresponding settlements were selected to train, validate, and test the proposed model. Using the R-squared, the proposed model was compared to other machine learning models like linear regression, elastic net regression, polynomial regression, and support vector machine. For the comparison between the real and predicted settlements, the experimental results show that while the lowest R² value of 0.322 was found from elastic net regression, the highest accuracy of 0.871 was obtained from the proposed ANN model. It concluded the effectiveness of the machine learning method, particularly in the ANN model, in predicting the soil characteristics.

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Prediction of Liquefaction-Induced Settlement Using Artificial Neural Network

Hoang, Dung V. / Bui, Phuoc T. H. / Phan, An T. T. / Nguyen, Tan-No

This study aims to propose a machine-learning algorithm for predicting the ground settlement caused by liquefaction. An artificial neural network (ANN) approach was used. The properties of soil layers, namely unit weight (γ), soil layer depth (d), standard penetration test blow count (N₁₍₆₀₎), cyclic stress ratio (CSR), and corresponding settlements were selected to train, validate, and test the proposed model. Using the R-squared, the proposed model was compared to other machine learning models like linear regression, elastic net regression, polynomial regression, and support vector machine. For the comparison between the real and predicted settlements, the experimental results show that while the lowest R² value of 0.322 was found from elastic net regression, the highest accuracy of 0.871 was obtained from the proposed ANN model. It concluded the effectiveness of the machine learning method, particularly in the ANN model, in predicting the soil characteristics.

Prediction of Liquefaction-Induced Settlement Using Artificial Neural Network

Hoang, Dung V. / Bui, Phuoc T. H. / Phan, An T. T. / Nguyen, Tan-No

This study aims to propose a machine-learning algorithm for predicting the ground settlement caused by liquefaction. An artificial neural network (ANN) approach was used. The properties of soil layers, namely unit weight (γ), soil layer depth (d), standard penetration test blow count (N₁₍₆₀₎), cyclic stress ratio (CSR), and corresponding settlements were selected to train, validate, and test the proposed model. Using the R-squared, the proposed model was compared to other machine learning models like linear regression, elastic net regression, polynomial regression, and support vector machine. For the comparison between the real and predicted settlements, the experimental results show that while the lowest R² value of 0.322 was found from elastic net regression, the highest accuracy of 0.871 was obtained from the proposed ANN model. It concluded the effectiveness of the machine learning method, particularly in the ANN model, in predicting the soil characteristics.

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Titel:

Prediction of Liquefaction-Induced Settlement Using Artificial Neural Network

Weitere Titelangaben:

Lecture Notes in Civil Engineering

Beteiligte:

Ha-Minh, Cuong (Herausgeber:in) / Pham, Cao Hung (Herausgeber:in) / Vu, Hanh T. H. (Herausgeber:in) / Huynh, Dat Vu Khoa (Herausgeber:in) / Hoang, Dung V. (Autor:in) / Bui, Phuoc T. H. (Autor:in) / Phan, An T. T. (Autor:in) / Nguyen, Tan-No (Autor:in)

Kongress:

International Conference series on Geotechnics, Civil Engineering and Structures ; 2024 ; Ho Chi Minh City, Vietnam

Erschienen in:

Proceedings of the 7th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2024, 4-5 April, Ho Chi Minh City, Vietnam ; Kapitel: 100 ; 893-900

Lecture Notes in Civil Engineering ; 482

Erscheinungsdatum:

01.06.2024

Format / Umfang:

8 pages

ISBN:

978-981-97-1972-3 , 978-981-97-1971-6

ISSN:

2366-2565 , 2366-2557

DOI:

https://doi.org/10.1007/978-981-97-1972-3_100

Medientyp:

Aufsatz/Kapitel (Buch)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

Liquefaction , settlement , artificial neural network , machine learning Engineering , Geoengineering, Foundations, Hydraulics , Sustainable Development , Sustainable Architecture/Green Buildings , Cyber-physical systems, IoT , Professional Computing , Structural Materials

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