排序方式： 共有11条查询结果，搜索用时 15 毫秒
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This study presents a probabilistic neural network (PNN) technique for predicting the stability number of armor blocks of breakwaters. The PNN is prepared using the experimental data of Van der Meer. The predicted stability numbers of the PNN are compared with those of previous studies, i.e. by an empirical formula and a previous neural network model. The agreement index between the measured and predicted stability numbers by PNN are better than those by the previous studies. The PNN offers a way to interpret the network's structure in the form of a probability density function and it is easy to implement. Therefore, it can be an effective tool for designers of rubble mound breakwaters. 相似文献
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The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are considered as inputs to the SVM and GPR. We give an equation for determination of reservoir induced earthquake M. The developed SVM and GPR have been compared with the Artificial Neural Network (ANN) method. The results show that the developed SVM and GPR are efficient tools for prediction of reservoir induced earthquake M. 相似文献
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水库诱发地震震级(M)的预测是在地震工程中的一项重要任务。本文采用支持向量机(SVM)和高斯过程回归(GPR)模型根据水库的参数预测了水库诱发地震震级(M)。综合参数(E)和最大的水库深度(H)作为支持向量机和高斯过程回归模型的输入参数。我们给出一个方程确定水库诱发地震震级(M)。将本文开发的支持向量机和建立的高斯过程回归方法与人工神经网络(ANN)方法相比。结果表明,本文研发的支持向量机和高斯过程回归方法是预测水库诱发地震震级(M)的有效工具。 相似文献
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In this study,an advanced probabilistic neural network(APNN)method is proposed to reflect the global probability density function(PDF)by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables.The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of van der Meer,and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network(ANN)model.The APNN shows better results in predicting the stability number of armor blocks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable. 相似文献
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The dynamics of jacket supported offshore wind turbine (OWT) in earthquake environment is one of the progressing focuses in the renewable energy field. Soil-structure interaction (SSI) is a fundamental principle to analyze stability and safety of the structure. This study focuses on the performance of the multiple tuned mass damper (MTMD) in minimizing the dynamic responses of the structures objected to seismic loads combined with static wind and wave loads. Response surface methodology (RSM) has been applied to design the MTMD parameters. The analyses have been performed under two different boundary conditions: fixed base (without SSI) and flexible base (with SSI). Two vibration modes of the structure have been suppressed by multi-mode vibration control principle in both cases. The effectiveness of the MTMD in reducing the dynamic response of the structure is presented. The dynamic SSI plays an important role in the seismic behavior of the jacket supported OWT, especially resting on the soft soil deposit. Finally, it shows that excluding the SSI effect could be the reason of overestimating the MTMD performance. 相似文献
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This paper proposes to use least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of the magnitude (M) of induced earthquakes based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are used as input variables of the LSSVM and RVM. The output of the LSSVM and RVM is M. Equations have been presented based on the developed LSSVM and RVM. The developed RVM also gives variance of the predicted M. A comparative study has been carried out between the developed LSSVM, RVM, artificial neural network (ANN), and linear regression models. Finally, the results demonstrate the effectiveness and efficiency of the LSSVM and RVM models. 相似文献
8.
Multiple Tuned Mass Damper Based Vibration Mitigation of Offshore Wind Turbine Considering Soil–Structure Interaction 总被引：1，自引：0，他引：1
The dynamics of jacket supported offshore wind turbine(OWT) in earthquake environment is one of the progressing focuses in the renewable energy field. Soil–structure interaction(SSI) is a fundamental principle to analyze stability and safety of the structure. This study focuses on the performance of the multiple tuned mass damper(MTMD) in minimizing the dynamic responses of the structures objected to seismic loads combined with static wind and wave loads. Response surface methodology(RSM) has been applied to design the MTMD parameters. The analyses have been performed under two different boundary conditions: fixed base(without SSI) and flexible base(with SSI). Two vibration modes of the structure have been suppressed by multi-mode vibration control principle in both cases. The effectiveness of the MTMD in reducing the dynamic response of the structure is presented. The dynamic SSI plays an important role in the seismic behavior of the jacket supported OWT, especially resting on the soft soil deposit.Finally, it shows that excluding the SSI effect could be the reason of overestimating the MTMD performance. 相似文献
9.
This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction of Angle of Shearing Resistance(?) of soil. MARS is an adaptive, non-parametric regression approach. Percentages of fine-grained (FG), coarse-grained (CG), liquid limit (LL), and bulk density (BD) have been used as input variables of MARS. The developed MARS gives an equation for prediction of ? of soil. The results of MARS have been compared with Genetic Expression Programming (GEP), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) models. These results demonstrate that the developed MARS can be used as a robust model for determination of ? of soil. 相似文献
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Kumar Deepak Samui Pijush Kim Dookie Singh Anshuman 《Geotechnical and Geological Engineering》2021,39(2):1049-1058
In this research, deep learning (DL) model is proposed to classify the soil reliability for liquefaction. The applicability of the DL model is tested in comparison with emotional backpropagation neural network (EmBP). The database encompassing cone penetration test of Chi–Chi earthquake. This study uses cone resistance (qc) and peck ground acceleration as inputs for prediction of liquefaction susceptibility of soil. The performance of developed models has been assessed by using various parameters (receiver operating characteristic, sensitivity, specificity, Phi correlation coefficient, Precision–Recall F measure). The performance of DL is excellent. Consistent results obtained from the proposed deep learning model, compared to the EmBP, indicate the robustness of the methodology used in this study. In addition, both the developed model was also tested on global earthquake data. During validation on global data, both the models shows good results based on fitness parameters. The developed classification models a simple, but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction potential. The finding of this paper can be further used to capture the relationship between soil and earthquake parameters.
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