A Comparative Analysis of Predicting Seismic Liquefaction Susceptibility of Dhaka Subway Project with a Machine Learning  Approach.

Mannan, Raiyan; Seebtaien, Omor; Mukarroma, Fahmida

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dc.contributor.author	Mannan, Raiyan
dc.contributor.author	Seebtaien, Omor
dc.contributor.author	Mukarroma, Fahmida
dc.date.accessioned	2023-01-18T06:49:44Z
dc.date.available	2023-01-18T06:49:44Z
dc.date.issued	2022-05-30
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Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers and Geotechnics, 35(1), 33–46. Cetin, K. O., Seed, R. B., Der Kiureghian, A., Tokimatsu, K., Harder, L. F., Kayen, R. E., & Moss, R. E. S. (2004). Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential. Journal of Geotechnical and Geoenvironmental Engineering, 130(12), 1314–1340. https://doi.org/10.1061/(asce)1090-0241(2004)130:12(1314) Juang, C. H., Yuan, H., Lee, D.-H., & Lin, P.-S. (2003). Simplified cone penetration testbased method for evaluating liquefaction resistance of soils. Journal of Geotechnical and Geoenvironmental Engineering, 129(1), 66–80.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1652
dc.description	Supervised by Dr. Hossain Md. Shahin Head of Department, Department of Civil and Environmental Engineering (CEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Civil and Environmental Engineering, 2022	en_US
dc.description.abstract	Seismic soil liquefaction is a dangerous phenomenon that occurs during seismic loading due to earthquakes. In this study, empirical formulas are used to assess liquefaction triggering based on the standard penetration test (SPT) data from the Dhaka Subway Project. After that three machine learning algorithms are applied to predict seismic liquefaction triggering of the obtained dataset. The first machine learning algorithm, Logistic regression, is a linear classification model. It implements the sigmoid function to generate binary outputs. The second machine learning algorithm is the Support Vector Machine (SVM) which represents supervised learning and is widely used as a classification and outliers detection algorithm. The third machine learning algorithm is the Artificial Neural Network (ANN) based on the Multi-layer Perceptron (MLP) theory, which uses a training algorithm called Levenberg-Marquardt backpropagation. Furthermore, this study also highlights the correlation between different soil parameters in triggering soil liquefaction. The developed models are then evaluated with confusion matrices which are later used to find out Overall Accuracy, Precision, Sensitivity, Recall (Specificity), F1 score, RMSE, and MAE. ROC curves are also used to evaluate these models and establish which model is the most effective.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Civil and Environmental Engineering (CEE), Islamic University of Technology(IUT)	en_US
dc.subject	Noise, Noise pollution, Traffic induced Noise, Traffic police, Health, Safety.	en_US
dc.title	A Comparative Analysis of Predicting Seismic Liquefaction Susceptibility of Dhaka Subway Project with a Machine Learning Approach.	en_US
dc.type	Thesis	en_US