黄土湿陷性物性指标分析与定量预测模型研究  

Analysis and quantitative prediction model of loess collapsibility physical properties

作　　者：王凯[1,2] 李向国 李帅 曹晨旭[2,3] WANG Kai;LI Xiangguo;LI Shuai;CAO Chenxu(Key Laboratory of Road and Railway Engineering Safety and Security,Ministry of Education(Shijiazhuang Railway University),Shijiazhuang 050043,China;School of Civil Engineering,Shijiazhuang University of Railways,Shijiazhuang 050043,China;China Railway Beijing Bureau Group Corporation Baoding Public Works Section,Baoding 071000,China)

机构地区：[1]道路与铁道工程安全保障省部共建教育部重点实验室(石家庄铁道大学),河北石家庄050043 [2]石家庄铁道大学土木工程学院,河北石家庄050043 [3]中国铁路北京局集团有限公司保定工务段,河北保定071000

出　　处：《甘肃科学学报》2024年第5期138-145,共8页Journal of Gansu Sciences

摘　　要：依托甘肃省通定高速项目,结合某路段典型黄土区域以及现场黄土物性实验结果,研究了黄土湿陷性等级与单项物性指标之间的相关性,确定其中相关性较强的物性指标;应用因子分析理论,消除了各指标间的多重共线性影响,提出了影响判别湿陷性黄土的独立指标,并建立了湿陷性黄土的定性判断标准;在此基础上构建适用于该地区的黄土湿陷系数的BP神经网络定量预测模型。结果表明:黄土湿陷系数与天然含水率、饱和度、孔隙率、孔隙比、干密度、压缩系数的相关性强,与塑性指数、压缩模量相关性较弱;当孔隙比e<0.825、压缩系数a<0.244、天然含水率w>18.2%时,可判定该类土不具有湿陷性;采用BP神经网络能够准确可靠预测湿陷系数,其精度能够满足实际工程应用。研究成果可为精确评价该地区及类似工程的湿陷性黄土等级方面的研究和现场判别提供理论依据,具有很强的理论和实践应用价值。Based on the project of Tongding Expressway in Gansu Province,the correlation between loess collapsibility grade and single physical index is studied in this paper,and the physical index with strong correlation are determined by combining the results of a typical loess area of a road section and the field loess physical experiment.The BP neural network quantitative prediction model of collapsibility coefficient of loess applicable to the region was constructed on this basis.The results show that(1)the correlation between collapsibility coefficient of loess and natural water content,saturation,porosity,pore ratio,dry density and compression coefficient is strong,and the correlation with plasticity index and compression modulus is weak.(2)When the pore ratio e<0.825,compression coefficient a<0.244 and natural water content w>18.2%,it can be judged that this type of soil does not have collapsibility.(3)The BP neural network can predict the collapsibility accurately and reliably,and its accuracy can meet the practical engineering applications.The research results can be used as a theoretical basis for research and field discrimination in terms of accurate evaluation of loess collapsibility grade in this area and similar projects,and has strong theoretical and practical application value.

关 键 词：黄土湿陷 物性指标 BP神经网络 预测模型 

分 类 号：U444[建筑科学—桥梁与隧道工程]