Multipoint Monitoring Model on Road Foundation Settlement  Considering Heaping Pre-compaction Effect

Abstract

Abstract: To analyze and forecast road foundation settlement caused by heaping pre-compaction, the relationship between settlement and heaping process were studied according to the hereditycreep theory. Radial basis function (RBF) artificial neural network was used to establish a monitoring model. In the model, the input units aim at heaping settlement and the output units including multipoint settlement are formed based on surveyed settlement insite and heaping recordation. Moreover, a pre-select RBF center method was presented considering heaping settlement character. It works with fuzzy C-means algorithm (FCM) together to decide final centers. Model instance established in above way with monitoring data describes the heaping effect well, and with good training and forecast results.

Key words: road foundation settlement, heaping pre-compaction, multi-point monitoring, radial basis function (RBF) neural network, monitoring model

CLC Number:

TU470

HUANG Ming-1, LIU Jun-2. Multipoint Monitoring Model on Road Foundation Settlement Considering Heaping Pre-compaction Effect[J]. Journal of Shanghai Jiaotong University, 2011, 45(05): 753-756.

References

［1］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，2008:19, 8395.

［2］Kim D, Huh S H, Seo S J, et al. Selforganizing radial basis function network modeling for robot manipulator［C］//Lecture Notes in Computer Science. Berlin: Springer, 2005:579587.

［3］王宁，黄铭.开挖作用下的深基坑变形神经网络监测模型［J］. 上海交通大学学报，2009,43(6)：990993.

WANG Ning, HUANG Ming. Deformation monitoring artificial neural network model of deep foundation pit considering the excavation effect［J］. Journal of Shanghai Jiaotong University, 2009,43(6)：990993.

［4］Casasent D, Chen X W. Radial basis function neural networks for nonlinear fisher discrimination and neymanpearson classification ［J］. Neural Networks, 2003, 16(5):529535.

［5］Liu Z D, Chen J B, Song C L. A new RBF neural network with GAbased fuzzy Cmeans clustering algorithm for SINS fault diagnosis［C］//2009 Chinese Control and Decision Conference. Piscataway: IEEE Computer Society, 2009: 208211.

［6］陶振宇，潘别桐. 岩石力学原理与方法［M］﹒武汉:中国地质大学出版社，1991：95112.

［7］黄铭，刘俊. 土石坝沉降填筑灰色监测模型分析［J］.上海交通大学学报,2005,39(5)：786789.

HUANG Ming, LIU Jun. Settlementfill monitoring grey model of embankment dam［J］. Journal of Shanghai Jiaotong University, 2005, 39(5):786789.

［8］Li D Y, Zhou Y C. Application of BP network to multiplespot model of dam deformation monitoring［C］//Proceedings of the International Symposium on Dams in the Societies of the 21st Century, ICOLDSPANCOLDDams and Reservoirs, Societies and Environment in the 21st Century. Leiden: Taylor and Francis/Balkema, 2006: 541543.

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Multipoint Monitoring Model on Road Foundation Settlement Considering Heaping Pre-compaction Effect

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