Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Liu, C. Y.; Wang, Y.; Hu, Xinyu; Han, Yalu; Zhang, Xiaopu; Du, Lizhi

doi:10.1155/2021/5595277

Cited by 38 publications

(38 citation statements)

References 55 publications

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“…After analyzing and comparing the respective performance of IAWTD, C-SVM and EEMD in turn, in order to further verify the compensation effect of IAWTD-C-SVM-EEMD, we compared it with IAWTD-RBF, GA-BP and CEEMDAN-ELM [26][27][28], the comparison results are given in Figure 17, and it can be seen that IAWTD-C-SVM-EEMD has the best effect. Allan variance analysis is a method which can evaluate the performance of different types gyroscopes such as MEMS gyroscope, Fiber optic gyroscope, Laser gyroscopes.…”

Section: B Analysis Of Experimental Resultsmentioning

confidence: 99%

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

Cao

Liu

Liu³

et al. 2021

IEEE Access

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To eliminate the humidity drift of gyroscope, a new fusion algorithm (IAWTD-CSVM-EEMD) based on Improved Adaptive Wavelet Threshold De-noising (IAWTD), C-means Support Vector Machine (CSVM) and Ensemble Empirical Mode Decomposition (EEMD) is proposed in this paper. In the new fusion algorithm, the humidity drift component is obtained by denoising the output signal of MEMS gyroscope by IAWTD. Then, the humidity drift compensation model is established, the relative humidity, the change rate of relative humidity and the humidity drift component are taken as the input of the compensation model, and the final gyroscope output can be obtained by using EEMD to denoise the output of compensation model. To verify the compensation effect of the fusion algorithm, the gyroscope outputs are collected and analyzed with the relative humidity ranged from 40% to 90% based on the temperature varying from 20℃ to 60℃. The results show that the IAWTD-CSVM-EEMD method significantly reduces the influence of relative humidity drift on the gyroscope output, according to the quantitative analysis of Allan variance, the quantization noise of the gyroscope output decreases by 87.78%, 96.37%, 97.77%, 99.17% and 92.62% respectively under the relative humidity ranging from 40% to 90%, as the temperature rose from 20 ℃ to 60 ℃ at intervals of 10 ℃, In addition, the bias stability decreases by 96.9%, 99.41%, 99.1%, 99.46%, and 99.78% respectively and the angle random walk decreases by 88.16%, 96.54%, 98.16%, 94.43%, and 92.05% respectively at different temperatures. It is worth mentioning that, to further verify the applicability of the fusion algorithm, a group of comparative experiments are added to consider the influence of temperature changes on the gyroscope output under different relative humidity. The experimental results show that the quantization noise, bias stability and angle random walk of the MEMS gyroscope are significantly reduced compared with the original output after processing by IAWTD-CSVM-EEMD. Therefore, the method proposed in this paper is beneficial to reduce the humidity drift in the MEMS gyroscope output.

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Section: B Analysis Of Experimental Resultsmentioning

confidence: 99%

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

Cao

Liu

Liu³

et al. 2021

IEEE Access

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“…Brittleness is the key parameter describing the rock failure and is a fundamental parameter in various fields, e.g., mining engineering, rock mechanics, unconventional oil, and hydraulic fracturing [1][2][3][4][5][6][7][8][9][10]. Nowadays, the definition for rock brittleness is proposed in different engineering fields.…”

Section: Introductionmentioning

confidence: 99%

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Liu

Zhang

et al. 2021

Lithosphere

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Brittleness is a crucial parameter of rock mass and the key indicator in rock engineerings, such as rockburst prediction, tunnelling machine borehole drilling, and hydraulic fracturing. To solve the problem of using present brittleness indexes, the existing rock brittleness indexes were firstly summarised in this paper. Then, a brittleness index (BL), which considers the ratio of stress drop rate and stress increase rate and the peak stress, was proposed. This new index has the advantage of simplifying the acquisition of key parameters and avoiding dimensional problems, as well as taking the complete stress-strain curves into account. While applying the BL, the peak strain is used to describe the difficulty of brittle failure before the peak point, and the ratio of stress drop to strain increase can reflect the stress drop rate without dimension problem. In order to verify the applicability of BL, through the PFC2D, the microparameters and confining pressure were changed to model different types of rock numerical specimens and different stress condition. The results show that the BL can well reflect and classify the brittleness characteristics of different rock types and characterise the constraint of confining pressure on rock brittleness. Moreover, the influence of microparameter on macroparameter was studied. In order to further verify the reliability of the brittleness index (BL), this study conducted uniaxial and triaxial compression tests (30 MPa) on marble, sandstone, limestone, and granite under different confining pressure.

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“…Slope stability is affected by many uncertain factors, such as natural and human factors, and there is a complex nonlinear relationship between them. How to establish an accurate rock slope stability evaluation model considering multiple factors has always been the focus of engineering [3][4][5][6][7][8]. At present, the research on slope stability mainly focuses on numerical simulation, theoretical analysis, and experimental research.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example

Huang

Mao

Zhu³

et al. 2021

Geofluids

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Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is used to reduce the dimension of several influencing factors, and four independent principal component variables are selected. With the help of RVM mapping the nonlinear relationship between the safety factor of slope stability and the principal component variables, the prediction model of rock slope stability based on PCA-RVM is established. The results show that under the same sample, the maximum relative error of the PCA-RVM model is only 1.26%, the average relative error is 0.95%, and the mean square error is 0.011, which is far lower than that of the RVM model and the GEP model. By comparing the results of traditional calculation method and PCA-RVM model, it can be concluded that the PCA-RVM model has the characteristics of high prediction accuracy, small discreteness, and high reliability, which provides reference value for accurately predicting the stability of rock slope.

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Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Cited by 38 publications

References 55 publications

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example

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