Optimized neural network model for predicting ultimate bearing capacity of statically-pressured pipe pile based on principal component analysis

SHI Yong-qiang; ZHAO Jian-bin; YANG Jun

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Optimized neural network model for predicting ultimate bearing capacity of statically-pressured pipe pile based on principal component analysis

更新时间：2026-06-25

- Optimized neural network model for predicting ultimate bearing capacity of statically-pressured pipe pile based on principal component analysis
- Vol. 32, Issue S2, (2011)
- 作者机构：
  
  1. 哈尔滨工业大学土木工程学院,哈尔滨,150001
  2. 沈阳建筑大学土木工程学院,沈阳,110168
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU443
- Online First：26 August 2011，
  
  Published：2011
- 稿件说明：
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SHI Yong-qiang, ZHAO Jian-bin, YANG Jun. Optimized neural network model for predicting ultimate bearing capacity of statically-pressured pipe pile based on principal component analysis[J]. 2011, 32(S2).
DOI：

SHI Yong-qiang, ZHAO Jian-bin, YANG Jun. Optimized neural network model for predicting ultimate bearing capacity of statically-pressured pipe pile based on principal component analysis[J]. 2011, 32(S2). DOI：

摘要

引入主成分分析法和基于共轭梯度优化算法的人工神经网络模型原理，建立了静压管桩单桩竖向承载力预测估算的新方法。通过对影响单桩极限承载力的各因素进行主成分分析确定了综合变量，构建了以综合变量为输入，以单桩极限承载力为输出的神经网络模型。应用神经网络结构分析的共轭梯度算法，优化计算获得给定样本的网络权值和阈值，获得静压管桩极限承载力的估算网络，应用实例分析计算了静压管桩单桩极限承载力问题。结果表明，利用所建立的神经网络预测静压管桩极限承载力是可行的，且具有较好的预测精度和良好的适用性。该方法为静压管桩竖向承载性状的理论分析开辟了一个新的研究途径，为今后相关问题研究提供借鉴和指导

Abstract

A new method for predicting the vertical ultimate bearing capacity of statically-pressured pipe pile is established by using artificial neural network based on principal component analysis and conjugate gradient algorithm. The integrated variables are determined through the principal component analysis of factors which influenced the ultimate bearing capacity of single pile. Then

the artificial neural network with integrated variables as input and ultimate bearing capacity as output is confirmed. Also

the conjugate gradient algorithm is applied to computing the weight values and threshold values with given samples in order to obtain the network of predicting ultimate bearing capacity. At last

the engineering examples are used to verify the established model. It could be concluded that it is feasible to predicting the ultimate bearing capacity of statically-pressured pipe pile with this model

which has a better prediction accuracy and applicability. This method provides a new way to analyze the vertical bearing character of statically-pressured pipe pile

and gives references to the relevant problems.

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