Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

8Citations
Citations of this article
31Readers
Mendeley users who have this article in their library.

Abstract

Liquefaction has been responsible for several earthquake-related hazards in the past. An earthquake may cause liquefaction in saturated granular soils, which might lead to massive consequences. The ability to accurately anticipate soil liquefaction potential is thus critical, particularly in the context of civil engineering project planning. Support vector machines (SVMs) and Bayesian optimization (BO), a well-known optimization method, were used in this work to accurately forecast soil liquefaction potential. Before the development of the BOSVM model, an evolutionary random forest (ERF) model was used for input selection. From among the nine candidate inputs, the ERF selected six, including water table, effective vertical stress, peak acceleration at the ground surface, measured CPT tip resistance, cyclic stress ratio (CSR), and mean grain size, as the most important ones to predict the soil liquefaction. After the BOSVM model was developed using the six selected inputs, the performance of this model was evaluated using renowned performance criteria, including accuracy (%), receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). In addition, the performance of this model was compared with a standard SVM model and other machine learning models. The results of the BOSVM model showed that this model outperformed other models. The BOSVM model achieved an accuracy of 96.4% and 95.8% and an AUC of 0.93 and 0.98 for the training and testing phases, respectively. Our research suggests that BOSVM is a viable alternative to conventional soil liquefaction prediction methods. In addition, the findings of this research show that the BO method is successful in training the SVM model.

References Powered by Scopus

Support-Vector Networks

46320Citations
N/AReaders
Get full text

LIBSVM: A Library for support vector machines

28218Citations
N/AReaders
Get full text

A tutorial on support vector regression

9396Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

12Citations
N/AReaders
Get full text

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

9Citations
N/AReaders
Get full text

Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches

5Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Zhang, X., He, B., Sabri, M. M. S., Al-Bahrani, M., & Ulrikh, D. V. (2022). Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141911944

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 3

60%

Lecturer / Post doc 1

20%

Researcher 1

20%

Readers' Discipline

Tooltip

Engineering 7

64%

Economics, Econometrics and Finance 2

18%

Physics and Astronomy 1

9%

Computer Science 1

9%

Save time finding and organizing research with Mendeley

Sign up for free