Application of genetic algorithm-based support vector machines for prediction of soil liquefaction

Xue, Xin; Xiao, Ming

doi:10.1007/s12665-016-5673-7

Cited by 31 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to the geological survey report, a tailings pond is located in a 7-degree fortification zone with a preset seismic acceleration of 0.10 g and a characteristic period of 0.45 s. The equivalent shear wave velocity is greater than 284 m/s, which is a medium hard site [34]. A Traft wave is an actual strong earthquake record with a characteristic period 0.44 s, suitable for the medium hard site.…”

Section: Test Loading Schemementioning

confidence: 99%

“…Erzin and Ecemis [33] proposed different ANN models to predict cone penetration and liquefaction resistance. Xue and Xiao [34] proposed a hybrid genetic algorithm (GA) and support vector machine (SVM) to predict soil liquefaction potential by using CPT-based field data from large earthquakes from 1964 to 1983. Xue and Liu [35] proposed two optimization techniques, a genetic algorithm (GA) and particle swarm optimization (PSO), to improve the performance of a CPT-based neural network model for the prediction of soil liquefaction sensitivity based on field data of large earthquakes from 1964 to 1983.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

et al. 2022

View full text Add to dashboard Cite

The liquefaction of tailings sand caused by seismic loads is a major problem in ensuring the safety of tailings ponds. Liquefaction may cause uncontrolled fluidized failure of the dam body, causing considerable damage to the lives, property and environment of people downstream. In this paper, a prototype tailings sand is used as the material to consider the main factors affecting liquefaction (i.e., dynamic load, soil quality, burial and static conditions). By embedding acceleration, pore pressure and earth pressure sensors in the rigid design of the self-designed rigid model box, different types of seismic waves of different ground motion amplitudes (PGA) were induced in a shaking table test of tailings sand liquefaction. The seismic intensity, waveform (class II, III and IV seismic waves) and active earth pressure of the PGA characterizing dynamic factors were obtained, and the static factors were characterized. The dynamic shear stress ratio, the peak acceleration of the earthquake, the pore pressure of the drainage factor and the buried depth (overlying effective pressure) characterize the soil conditions. SPSS software was used to analyze the factor dimension reduction, and the most suitable factors for factor analysis were obtained. Particle Swarm Optimization (PSO) was used to optimize the parameters, and the improved PSO-SVM algorithm was compared with the existing genetic algorithm (GA) and grid node search (GS). The algorithm used in this paper is fast and has a relatively high accuracy rate of 92.7%. The established threshold model method is of great significance to predict the liquefaction of tailings sand soil under the action of ground motions and to carry out safety managemenin advance, which can provide a certain reference for the project.

show abstract

Section: Test Loading Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

et al. 2022

View full text Add to dashboard Cite

show abstract

“…e internal kernel function K(x i , x j ) � ψ(x i ) × ψ(x j ) can be obtained when the kernel function satisfies the condition of Mercer [30]. At the same time, Lagrange changes are introduced to get equation (4) [31]:…”

Section: Basic Principle Of Support Vector Machine Regressionmentioning

confidence: 99%

A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

Wang

Zhao

Fang

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

show abstract

“…During an earthquake, the buildup of pore water pressure in saturated sandy soils leads to a reduction in effective stress, which causes the soil matrix to undergo a transition from a solid to a liquid state (Xue et al, 2013;Schmidt & Moss, 2021;Ni et al, 2022). These changes in soil texture can have signi cant impacts on the stability of structures, as demonstrated by the structural damage caused by liquefaction in earthquake incidents such as the 1964 Alaska, 1985 Mexico City, 1994 Kobe, and 2011 Japan earthquakes (Xue & Xiao, 2016;Alberto-Hernandez & Towhata, 2017). The soil liquefaction potential (SLP) is in uenced by various geotechnical factors, including geological history, topography, groundwater level, and seismicity (Ni et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Soil Liquefaction Potential Estimation using Machine Learning and Web-Based Platform

Vasegh

Dehghanbanadaki

Motamedi

2023

Preprint

View full text Add to dashboard Cite

In this study, a new web-based platform was developed for fast estimation of soil liquefaction potential (SLP). The geotechnical results from 47 boreholes in the north of Iran were collected over three years to create an estimator model. The dataset included information on SPT, soil type, strength parameters, and water content. Python libraries Pycaret and Gradio were used to develop the model for SLP. A set of pipeline codes were applied to base classifiers, including 13 different machine learning models such as the Ada boost classifier (ad), decision tree classifier (dt), gradient boosting classifier (gb), the k-neighbors classifier (knn), light gradient boosting machine (lightgbm) and random forest classifier (rf). The results show that the lightgbm model outperformed the other applied machine learning classifiers with accuracy = 0.946, AUC = 0.982, and F1-score = 0.9. The proposed model was then used as the primary element of the web-based application, providing a helpful tool for geotechnical engineers to determine SLP.

show abstract

Application of genetic algorithm-based support vector machines for prediction of soil liquefaction

Cited by 31 publications

References 35 publications

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

Enhanced Soil Liquefaction Potential Estimation using Machine Learning and Web-Based Platform

Contact Info

Product

Resources

About