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CivilComp Proceedings
ISSN 17593433 CCP: 74
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and B. Kumar
Paper 35
Analysis of Bearing Features of RockSocketed Piles Using Neural Networks Y. Huang, W.M. Ye and Y.Q. Tang
Department of Geotechnical Engineering, Tongji University, Shanghai, P.R. China Y. Huang, W.M. Ye, Y.Q. Tang, "Analysis of Bearing Features of RockSocketed Piles Using Neural Networks ", in B.H.V. Topping, B. Kumar, (Editors), "Proceedings of the Sixth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", CivilComp Press, Stirlingshire, UK, Paper 35, 2001. doi:10.4203/ccp.74.35
Keywords: neural network, back propagation, rocksocketed pile, bearing capacity, soil, rock.
Summary
Rocksocketed piles have been widely used in civil engineering for recent
decades. Some researches were undertaken to explore them[1,2,3].
However, it is
difficult to summarize the relationship between bearing capacity and the interacting
factors including lengthdiameter ratio, socketed length, soil property, and rock
property. The relative importance of factors influencing rocksocketed pile bearing
capacity hasn't been known precisely. Therefore, the main objective of this paper is
to compare the effects on bearing capacity of rocksocketed piles quantificationally
by artificial neural networks.
The growing interest in neural networks among geotechnical engineers is due to its excellent performance in modelling nonlinear multivariate problems[4,5]. It has been proved that a threelayer neural network can be used to represent an arbitrary continuous function. Artificial neural network consists of a number of interconnected processing elements, commonly referred to as neurons. The neurons interact with each other via weighted connections. These scalar weights determine the nature and strength of the influence between the interconnected neurons. The relationship between input layer and output layer of neurons is represented by sigmoid nonlinear transfer function. It is generally named BP network when the neural network paradigm adopted the backpropagation learning algorithm. Multi layers BP network has the following advantages: firstly, because nonlinear hyperplane area is given out, the result is more accurate and reasonable, and then this network has good faulttolerance. Secondly, the transfer function is continuously differential, so the process of weight study is very clear. Using above theorem, a suitable backpropagation neural network model for studying the bearing characteristics is proposed on the basis of 27 static vertical loading tests of rocksocketed piles in China. The data was used as the input signals of BP network. The established BP network can describe the complex relationships on pile bearing capacity. Four principal factors affecting bearing capacity of rock socketed piles were taken into accounted: lengthdiameter ratio, the ratio of diameter to socketed length, soil property, and rock property. Through study training, the case data of static tests of rocksocketed piles is input into the weights of neural. Therefore, the relative importance of above four factors can be calculated via the trained neural network. Because a threelayer neural network can be used to represent an arbitrary continuous function, one hidden layer and 8 units of hidden layer are chosen in this research after several experiments. In order to accelerate the network astringency and avoid concussion, the momentum gene and the learning rate is involved into study training procedure. According to the actual error, and may be taken as and respectively. Computation results indicate the connection weights and relative importance of the various interacting factors. The relative importance of the various input factors can be assessed by examining these connection weights. The results suggest that the more important input factors are the rock property and lengthdiameter ratio. The model also demonstrates that neural network theory can be applied to describe the complex geotechnical engineering system reasonably after learning from actual test data. This method has the advantage over other more conventional methods in that it can be readily retrained as additional data from actual field records are acquired. References
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