Study on Durability Damage Rules and Aging Prediction Method of Geosynthetics

2010 ◽  
Vol 163-167 ◽  
pp. 3242-3248
Author(s):  
Ze Liu ◽  
Guo Lin Yang
Keyword(s):  
Support Vector Machine ◽  
Tensile Strength ◽  
Radiation Intensity ◽  
Prediction Method ◽  
Field Tests ◽  
Elongation Rate ◽  
Support Vector ◽  
Prediction Methods ◽  
Durability Prediction ◽  
Main Factors

As geosynthetics is made from polymer, its durability is one of the most important issues people have been concerning. Based on the analysis of data gotten from laboratory, field tests and engineering samplings, the reasons for geosynthetics durability damage are summarized, the injury rules and main factors in various applications are analyzed; after studied the existing durability prediction methods, Support Vector Machine (SVM) theory is applied to predict the durability, and an example is analyzed in which temperature, humidity, UV radiation intensity and the aging time were set as input parameters, residual tensile strength and elongation rate were set as output parameters.

Research on Prediction Method of Soft Soil Foundation Settlement

2011 ◽  
Vol 97-98 ◽  
pp. 36-39
Author(s):  
Xiao Ma Dong
Keyword(s):  
Support Vector Machine ◽  
Soft Soil ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector ◽  
Prediction Methods ◽  
Foundation Settlement ◽  
Soil Foundation ◽  
Soft Soil Foundation ◽  
Curve Fitting Method

The current prediction methods of foundation settlement have biggish error under the condition of lesser foundation settlement observational datum. Aim at the localization of present prediction methods and the virtues of Support Vector Machine arithmetic, the method of predicting soft soil foundation settlement based on Least Square Support Vector Machine (LS-SVM) was proposed in this paper and compared with the neural network method and curve fitting method. The research results show that this proposed method is feasible and effective for predicting soft soil foundation settlement. Least Square Support Vector Machine provides a more advanced method than these conventional methods for predicting foundation settlement.

scholarly journals Voting Based Classification Method for Diabetes Prediction

2019 ◽  
Vol 8 (2S6) ◽  
pp. 913-918
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Research Work ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Prediction Analysis ◽  
Tree Classifier ◽  
Diabetes Prediction

This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.

CPI Big Data Prediction Based on Wavelet Twin Support Vector Machine

2020 ◽  
pp. 2159013
Author(s):  
Yiqing Fan ◽  
Zhihui Sun
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Consumer Price Index ◽  
Low Frequency ◽  
Prediction Method ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Reconstruction Method ◽  
Data Prediction ◽  
Svm Model

In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

Gas Pipeline Corrosion Prediction Based on Modified Support Vector Machine and Unequal Interval Model

2013 ◽  
Vol 373-375 ◽  
pp. 1987-1994 ◽  
Author(s):  
Wei Dong Zhang ◽  
Bin Shen ◽  
Yi Bo Ai ◽  
Bin Yang
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Prediction Method ◽  
Original Data ◽  
Gas Pipeline ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Grey Prediction ◽  
Interval Model ◽  
Pipeline Corrosion

The corrosion is an important problem for the service safety of oil and gas pipeline. This research focuses. This paper proposed a new prediction algorithm on corrosion prediction of gathering gas pipeline, which combined modified Support Vector Machine (SVM) with unequal interval model. Firstly, grey prediction method with unequal interval model was used to pretreatment original data because there is unequal interval problem in actual collected data of pipeline. Secondly, improved Support Vector Regression (SVR) based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) has been proposed to resolve parameters selection problem for SVR. Finally, the corrosion prediction model of gas pipeline has been proposed which combined improved SVR and unequal interval grey prediction method. The experiment results show this algorithm could increase precision of the pipeline corrosion prediction compared with the traditional SVM. This research provides reliable basis for in-service pipeline life prediction and confirming inspecting cycle.

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

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