路基工程
路基工程
로기공정
SUBGRADE ENGINEERING
2012年
3期
110-113
,共4页
灌浆压力%BP神经网络%偏差单元%预测模型
灌漿壓力%BP神經網絡%偏差單元%預測模型
관장압력%BP신경망락%편차단원%예측모형
grouting pressure%BP neural network%bias element%prediction model
灌浆压力是土体灌浆加固的重要参数。基于神经网络非线性映射特性,分析土体灌浆压力主要影响因素,建立符合一般工程判断和决策思维的BP网络预测模型,并引入偏差单元对其结构进行改进,实现了快速收敛,较高精度得出灌浆预测压力的具体数值。预测结果与室内灌浆试验压力对比表明,带偏差单元BP神经网络的土体灌浆压力预测结果具有较高准确性和一定的实用意义。
灌漿壓力是土體灌漿加固的重要參數。基于神經網絡非線性映射特性,分析土體灌漿壓力主要影響因素,建立符閤一般工程判斷和決策思維的BP網絡預測模型,併引入偏差單元對其結構進行改進,實現瞭快速收斂,較高精度得齣灌漿預測壓力的具體數值。預測結果與室內灌漿試驗壓力對比錶明,帶偏差單元BP神經網絡的土體灌漿壓力預測結果具有較高準確性和一定的實用意義。
관장압력시토체관장가고적중요삼수。기우신경망락비선성영사특성,분석토체관장압력주요영향인소,건립부합일반공정판단화결책사유적BP망락예측모형,병인입편차단원대기결구진행개진,실현료쾌속수렴,교고정도득출관장예측압력적구체수치。예측결과여실내관장시험압력대비표명,대편차단원BP신경망락적토체관장압력예측결과구유교고준학성화일정적실용의의。
Grouting pressure is an important parameter in soil mass consolidation by grouting. Based on the nonlinear mapping characteristics of neural network, the main factors affecting the grouting pressure are analyzed and the BP network prediction model consistent with general engineering judgment and decision- making thoughts is established. In addition, the structure of the model is improved by introduction of bias element; thus fast convergence is realized and the specific value of grouting pressure estimate with high accuracy is obtained. The comparison between predicted result and measured pressure from indoor grouting test shows that the estimate of the grouting pressure by BP neural network with bias element has high accuracy and certain practical significance.
