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风积砂地区路面基层材料的试验研究及仿真
引用本文:张向东,李殿维,张林.风积砂地区路面基层材料的试验研究及仿真[J].辽宁工程技术大学学报(自然科学版),2007,26(3):366-368.
作者姓名:张向东  李殿维  张林
作者单位:1. 辽宁工程技术大学,土木建筑工程学院,辽宁,阜新,123000
2. 通辽市交通工程局,第一工程处,内蒙古,通辽,028000
基金项目:辽宁省教育厅高等学校科学研究基金资助项目(2004D120)
摘    要:采用均匀设计的方法来优化安排试验,以水泥、石灰、碎石、风积砂为原材料进行试验研究,得到水泥石灰稳定风积砂的最佳含水量、最大干密度、无侧限抗压强度、回弹模量等指标,得到满足工程要求的最佳配合比为,水泥掺量900 g、石灰掺量1 000 g、碎石掺量3 000 g、风积砂掺量5 300 g。通过现场工业性试验和观测,结果表明:试验段水泥石灰稳定风积砂基层状况良好,且弯沉值满足有关的要求。将人工神经网络应用于水泥石灰稳定风积砂无侧限抗压强度的预测中,预测结果有较高的精度,同时对工程具有一定的指导意义。

关 键 词:风积砂土  均匀设计  路面基层  人工神经网络  无侧限抗压强度  预测
文章编号:1008-0562(2007)03-0366-03
修稿时间:2005-04-20

Experimental investigation and simulation for road surface base material in aeolian area
ZHANG Xiang-dong,LI Dian-wei,ZHANG Lin.Experimental investigation and simulation for road surface base material in aeolian area[J].Journal of Liaoning Technical University (Natural Science Edition),2007,26(3):366-368.
Authors:ZHANG Xiang-dong  LI Dian-wei  ZHANG Lin
Institution:1. College of Civil and Architectural Engineering, Liaoning Technical University, Fuxin 123000, China; 2. The NO.1 Engineering Department, Traffic Engineering Bureau of Tongliao, Tongliao 028000, China
Abstract:Method by uniform design is used to posit majorized experiment,experimental investigation is carried out with raw materials of cement,lime,macadam and Aeolian,obtaining the target of optimal moisture content,maximum dry density,unconfined compression strength,modulus of resilience and so on for cement and lime stabilizing Aeolian soil,gaining the optimal mix for blending composition to meet project requirement is,cement is 900g,lime is 1000g,macadam is 3000g,Aeolian is 5300g.Through field engineering industrial experiment and observation,results indicate working section base is in good condition and flexure value can satisfy related demands.Artificial neural network is used in predicting unconfined compressive strength,obtaining higher precision in predicting outcome,which has some guiding significance for projects.
Keywords:Aeolian soil  uniform design  road surface base  artificial neural network  unconfined compression strength  predicting
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