SEA WALL MONITORING MODEL BASED ON RADIAL BASIS FUNCTION AND DISTINGUISHABILITY ON ITS FORECAST CONFIDENCE LEVEL

Huang Ming; Liu Jun

doi:10.13204/j.gyjz201309016

Volume 43 Issue 9

Dec. 2014

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > INDUSTRIAL CONSTRUCTION > 2013 > 43(9): 88-91

Huang Ming, Liu Jun. SEA WALL MONITORING MODEL BASED ON RADIAL BASIS FUNCTION AND DISTINGUISHABILITY ON ITS FORECAST CONFIDENCE LEVEL[J]. INDUSTRIAL CONSTRUCTION, 2013, 43(9): 88-91. doi: 10.13204/j.gyjz201309016

Citation:

Huang Ming, Liu Jun. SEA WALL MONITORING MODEL BASED ON RADIAL BASIS FUNCTION AND DISTINGUISHABILITY ON ITS FORECAST CONFIDENCE LEVEL[J]. INDUSTRIAL CONSTRUCTION, 2013, 43(9): 88-91. doi: 10.13204/j.gyjz201309016

Citation:

PDF( 0 KB)

SEA WALL MONITORING MODEL BASED ON RADIAL BASIS FUNCTION AND DISTINGUISHABILITY ON ITS FORECAST CONFIDENCE LEVEL

doi: 10.13204/j.gyjz201309016

Huang Ming¹,
Liu Jun²

1.
1. School of Civil Engineering,Hefei University of Technology,Hefei 230009,China;
2.
2． School of Naval Architecture,Ocean and Civil Engineering,Shanghai Jiaotong University,Shanghai 200240,China

Received Date: 2012-10-15
Publish Date: 2013-09-20

Abstract

Abstract

To analyze and forecast sea wall working state，a sea wall monitoring model was established by thefollowing steps: selecting former tidewater factor，integral rain factor and time effect factor based on causality study，using radial basis function ( ＲBF ) artificial neural network as modeling tool， considering monitoring datacharacteristics，and using fuzzy C-mean algorithm(FCM) to confirm ＲBF centers． And then，the forecast of sea wallworking state was realized by the methods below: errors of the model were studied，including error values，trend anddistribution，based on these，a hypothesis testing method was presented to evaluate forecast results consideringconfidence level，and stabilities of different forecast length was compared on a same confidence level，finally theinstance model were set up，the training and forecast effects were analyzed．
- sea wall safety monitoring,
- ＲBF model,
- forecast error,
- confidence level,
- hypothesis testing

FullText(HTML)

References(14)

References

Huang Ming, Liu Jun.Monitoring and Analysis of ShanghaiPudong Seawall Performance [J].Journal of Performance ofConstructed Facilities,2009,23(6):399-405.

[2] 黄铭.数学模型与工程安全监测[M].上海:上海交通大学出版社,2008.

[3] 李珍照.大坝安全监测[M].北京:中国电力出版社,1997.

[4] 吴中如,沈长松,阮焕祥.水工建筑物安全监控理论及其应用[M].南京:河海大学出版社,1990.

[5] Kim Dongwon, Huh Sung-Hoe, Seo Sam-Jun, et al.Self-Organizing Radial Basis Function Network Modeling for RobotManipulator[J].Lecture Notes in Computer Science,2005,35(33): 579-587.

[6] 王维斌,赵新华.基于RBF 神经网络的活性污泥模型的应用[J].南开大学学报:自然科学版,2008,41(2):91-97.

[7] 张秀玲,陈丽杰,季颖,等.基于径向基函数神经网络的板形模式识别研究[J].工业仪表与自动化装置,2009(3):7-9.

[8] Huang Ming,Liu Jun,Wang Ning.Foundation Pit Multi-PointDisplacement RBF Monitoring Model and Application Key Points[C]// 2010 International Conference on Mechanic Automationand Control Engineering, MACE2010.Piscataway: IEEEComputer Society,2010. 4562-4565.

[9] 黄铭,刘俊.堆载预压作用下路基沉降的多测点监测模型[J].上海交通大学学报,2011,45(5): 753-756.

[10] 刘笛,朱学峰,苏彩红.一种新型的模糊C 均值聚类初始化方法[J].计算机仿真,2004,21(11):148-151.

[11] 张新波.两阶段模糊C-均值聚类算法[J].电路域系统学报,2005,10(2):117-120.

[12] Pal N R,Bezdek J C.On Clusters Validity for the Fuzzy C-MeansMode [J].IEEE Trans.on Fuzzy Systems,1995,28(3): 370-379.

[13] Huang M,Liu J,Huang W,et al.Analysis of Sea Wall OsmosisPressures Influencing Factors and Monitoring Models[C]//Advances in Earth Structures: Research to Practice,Proceedingsof Sessions of Geoshanghai.ASCE,2006: 310-316.

[14] Everitt B S.The Cambridge Dictionary of Statistical[M].2nd ed.Cambridge: Press Syndicate of the University of Cambridge,2002.

Relative Articles

Supplements(0)

Cited By

Proportional views