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.

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.

scholarly journals A New SVM-Based Modeling Method of Cabin Path Loss Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaonan Zhao ◽  
Chunping Hou ◽  
Qing Wang
Keyword(s):  
Support Vector Machine ◽  
Path Loss ◽  
Prediction Method ◽  
Modeling Method ◽  
Support Vector ◽  
Propagation Modeling ◽  
Fitting Method ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Curve Fitting Method

A new modeling method of cabin path loss prediction based on support vector machine (SVM) is proposed in this paper. The method is trained with the path loss values of measured points inside the cabin and can be used to predict the path loss values of the unmeasured points. The experimental results demonstrate that our modeling method is more accurate than the curve fitting method. This SVM-based path loss prediction method makes the prediction much easier and more accurate, which covers performance traditional methods in the channel propagation modeling.

A prediction method for deck-motion based on online least square support vector machine and genetic algorithm

2018 ◽  
Vol 24 (2) ◽  
pp. 382-397
Author(s):  
Xixiang Liu ◽  
Qiming Wang ◽  
Rong Huang ◽  
Songbing Wang ◽  
Xianjun Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector

Prediction of Ground Settlement Based on Immune Genetic Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 1056-1060 ◽  
Author(s):  
Gui Quan Bi
Keyword(s):  
Genetic Algorithm ◽  
Soft Soil ◽  
Back Analysis ◽  
Foundation Settlement ◽  
Ground Settlement ◽  
Immune Genetic Algorithm ◽  
Improve Calculation ◽  
Soil Foundation ◽  
Soft Soil Foundation ◽  
Speed And Accuracy

It is very important to predict ground settlement and provide effective dada for construction on soft soil foundation. There are several prediction methods.However, back analysis method is identified as the most effective method in all these methods. The most primarily used method in back analysis methods is optimization algorithm. In this paper, to realize accurate prediction and calculation of soft soil foundation settlement, an improved immune genetic algorithm is presented by introducing immune mechanism to genetic algorithm. A example was given and illustrated that this algorithm can greatly improve calculation speed and accuracy in predicting soft soil foundation settlement.

Study on the Law of Cement-Soil Mixing Pile Composite Foundation Settlement in Ancient Tower Translocation Project

2017 ◽  
Vol 730 ◽  
pp. 463-472
Author(s):  
Dong Fan Shang ◽  
Tie Cheng Wang ◽  
Wan Ming Qiang ◽  
Lei Qiang Miao
Keyword(s):  
Retention Time ◽  
Soft Soil ◽  
Composite Foundation ◽  
Design Solution ◽  
Foundation Settlement ◽  
Soil Mixing ◽  
Soil Foundation ◽  
Soft Soil Foundation ◽  
Cement Soil ◽  
Translocation Speed

Longxi Tower translocation project is the first case of high-rise ancient tower structure translocation on soft soil foundation in China. Foundation treatment protocol and building materials are essential to control soft soil foundation settlement. Depending on Longxi Tower translocation project, firstly this thesis analyzed and confirmed control factors that impact foundation settlement and deformation; then combing with project experience, it selected cement soil mixing pile composite foundation method to treat soft soil foundation; and finally it confirmed the optimal solution to treat cement soil mixing pile composite foundation through analyzing the settlement law of cement soil mixing pile composite foundation. The analysis indicated that cement soil mixing pile composite foundation settlement are obviously impacted by translocation speed and retention time; foundation settlement and deformation level increase as translocation speed decreases and retention time increases; in the case that foundation settlement difference is not beyond 1/1000 of half the distance of track beams at the bottom of building, a treatment solution was made by analyzing how cement soil mixing pile settlement changes as translocation speed, retention time and other factors. The result showed that this treatment solution saved 42% of project cost more than the previous design solution. The research result of this thesis could be taken as a reference for the future similar projects.

Application of Grey Least Square Support Vector Machine in New Equipment Materiel Demand Prediction

2014 ◽  
Vol 602-605 ◽  
pp. 3333-3337
Author(s):  
Shuang Shuang Yu ◽  
Tie Ning Wang ◽  
Ning Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Original Data ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Grey Prediction ◽  
Demand Prediction ◽  
Efficient Prediction ◽  
General Demand

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.

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