The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction

Zhang, Yan; Qiu, Junbo; Zhang, Yonggang; Xie, Yuanlun

doi:10.1007/s12665-021-09648-w

Cited by 42 publications

(6 citation statements)

References 35 publications

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“…A nonlinear transformation is used to translate the input space samples into a high-dimensional characteristic space, and then an optimum classification plane is found that divides the samples linearly within the characteristic space as the next step [59,60]. The incidence of soil liquefaction functions well with the features of the approach to overcome binary classification issues in the study of soil liquefaction and its risk assessment (e.g., [61]).…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…This study's findings compare favorably with those of many previous soil liquefaction studies that used different datasets. For example, accuracy values of 92.2% and 93.19% were obtained in the studies conducted by Zhang et al [61] and Hoang and Bui [24], respectively, to predict soil liquefaction by introducing grey wolf optimization (GWO)-SVM and kernel Fisher discriminant analysis (KFDA) with least square support vector machine (LSSVM) techniques. In other words, the developed BOSVM prediction model outperformed the other models in terms of accuracy.…”

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confidence: 99%

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Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Sabri

Al-Bahrani

et al. 2022

Sustainability

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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.

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Section: Support Vector Machinesmentioning

confidence: 99%

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confidence: 99%

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Sabri

Al-Bahrani

et al. 2022

Sustainability

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“…SVM is a statistical theory-based machine learning algorithm [16] that classifies data sets based on the structural risk minimization theory of statistical learning [17]. As a binary classification algorithm, the goal of the algorithm is to find an optimal hyperplane that distinguishes between two classes, with the best generalization ability and robustness.…”

Section: Svmmentioning

confidence: 99%

A review of key algorithms for network security situation awareness research

Yao¹,

Yu²,

Yang³

et al. 2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

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With the rapid development of Internet technology, network threats are also increasing rapidly. Traditional ways of dealing with network threats, such as intrusion detection technology and malicious code detection technology, reveal the shortcomings of poor quantitative analysis and poor prediction of future situations. In order to cope with today's complex and changing network situation, network security situation awareness technology has gained more attention. Network security situation awareness consists of three layers: situation element extraction, situation understanding and situation prediction, and a complete network security situation awareness model is constructed through the three parts. The paper introduces the concept and development of network security situation awareness, describes the current mainstream algorithms and their advantages and disadvantages for each layer, and finally summarizes and outlooks on the problems of current mainstream algorithms and the trends of future development.

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“…The stability of waste dumps is one of the critical factors for the safe production of an open-pit mine (Behera et al, 2016;Wang et al, 2017;Gong et al, 2021). As a joint product of geological processing and artificial landfilling, the slope stability of a waste dump is mainly affected by the mechanical properties of the foundation (Gao et al, 2021;Zhang Y et al, 2021), the shape of the slope, and the properties of the discharged materials (Han et al, 2016;Wang et al, 2019;Jiang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Bearing Capacity Calculation of Soft Foundation of Waste Dumps—A Case of Open-Pit Mine

et al. 2022

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For a waste dump with soft foundation, the foundation bearing capacity has an important impact on slope stability. According to the load distribution and stress characteristics of a waste dump, combining the gravity load of the triangular slope of the waste dump and the passive Earth pressure exerted by the foundation soil with an improved Plandtl formula, the foundation bearing capacity and the ultimate pile height of a waste dump are calculated and determined. The concept of foundation bearing capacity of a waste dump is redefined, that is, the ultimate pile height corresponding to a certain slope angle. A method for determining the ultimate pile height of a waste dump based on the slope angle of the waste dump is proposed, and the relation function between dump slope angle and waste height is established. The results show that the sliding moment increment (∆MS) caused by the gravity load of the triangular slope after waste increase is positively proportional to the pile height increment (∆H); the anti-sliding moment increment (∆MAS) is positively proportional to or positively correlated with the pile height increment (∆H); the slope angle of the waste dump decreases with the increase of the thickness of soft bedrocks, and the smaller thickness of soft bedrocks is more favorable to the ultimate pile height of the waste dump. The research results can provide reference for the calculation of the bearing capacity of soft foundation and the optimal design of slope shape of waste dumps.

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The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction

Cited by 42 publications

References 35 publications

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

A review of key algorithms for network security situation awareness research

Bearing Capacity Calculation of Soft Foundation of Waste Dumps—A Case of Open-Pit Mine

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