Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Ma, Cong; Tan, Yuehu; Li, Erbing; Meng, Yunxiao; Duan, Ji’an

doi:10.3311/ppci.7732

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…After these data are transformed, they are projected into a high-dimensional space, and then a suitable linear function is found to solve the complex nonlinear problem [35,36]. As a result of the excellent performance of the SVM, it has been applied to deal with matters of landslide sensitivity analysis [37], slope stability research [38], and surrounding rock deformations estimation [39]. The principle of the SVM to deal with problems is as follows [35,40,41]:…”

Section: The Support Vector Machinementioning

confidence: 99%

Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

et al. 2020

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The development of agriculture and ecology, and the construction of water conservancy facilities are seriously hindered by the salinization of seasonal frozen soil. Unfrozen water exists in the freezing and thawing of frozen soil. This unfrozen water is the core and foundation for studying the process of seasonal frozen soil salinization. However, it is difficult to obtain the unfrozen water content (UW) in routine experiments, and it shows nonlinear characteristics under the action of the main factors contained: salt content, water content, and temperature. In this paper, a new model is proposed to predict the UW of saline soil based on the combined weighting method and the adaptive neuro-fuzzy inference system (ANFIS). Firstly, the distance function was used to combine the analytic hierarchy process (AHP) with the entropy weight method (the combined weighting method) to determine the importance of the influencing factors (temperature, initial water content, and salt content) on UW. On this basis, the AHP, entropy weight method, and adaptive neuro-fuzzy inference system (AHP-entropy-ANFIS) ensemble model was established. Secondly, the five-fold cross-validation method and statistical factors (coefficient of determination, mean squared error, mean absolute percent error, and mean absolute error) were applied to evaluate and compare the AHP-entropy-ANFIS ensemble model, the ANFIS model, the support vector machine (SVM) model, and the AHP, entropy weight method, and support vector machine (AHP-entropy-SVM) ensemble model. In addition, the prediction values of the four models and the experimental values were also compared. The results show that the AHP-entropy-ANFIS model had the strongest prediction capability and the best stability, and so is more suitable for predicting the UW of saline soil. This study provides useful guidance for preventing and mitigating salinization hazards in seasonally frozen areas.

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Section: The Support Vector Machinementioning

confidence: 99%

Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

et al. 2020

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“…On the other hand, data-driven techniques provide the opportunity to tackle such highly nonlinear prediction problems. These techniques have been interested in many fields and as well applied to civil engineering problems in general engineering such as [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], and especially in the concrete engineering such as [13,[24][25][26][27][28][29]. Data-driven techniques were also interested for predicting the properties of SCC.…”

Section: Introductionmentioning

confidence: 99%

M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

Kaveh

Bakhshpoori²,

Hamze‐Ziabari³

2017

Period. Polytech. Civil Eng.

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State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

Jong

Ong

2021

Tunnelling and Underground Space Technology

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Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Abstract: Abstract

Cited by 4 publications

References 11 publications

Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

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