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基于GPR的路基土介电特性影响因素试验研究 被引量:1

Test on Influence Factors of Dielectric Properties for Subgrade Soil Based on GPR
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摘要 利用探地雷达(GPR)检测路基土压实质量时,路基土介电常数的确定是保证探测精度和进行图像识别的关键技术之一。运用探地雷达对3种常见路基填筑材料(粉土、中砂与砾砂)进行了介电常数的室内测试,得到填筑材料在不同含水量与干密度条件下的介电常数,分析了介电常数与路基材料类别、含水量及干密度等因素之间的关系,且拟合相关系数均大于0.95。以粉土为例,通过MATLAB软件进行多元非线性回归,获得介电常数随土体含水量、干密度双因素变化的拟合函数,拟合效果良好,表明可以采用拟合函数求取粉土在不同含水量和干密度下的介电常数。试验方法及结论为路基土介电常数确定、大范围路基土压实质量的快速、无损、连续检测与评价提供了重要依据,同时可为类似问题提供技术参考。 When ground penetrating radar( GPR) is used to detect the compaction quality of subgrade soil,the determination of the dielectric constant of subgrade soil is one of the key techniques to ensure the detecting precision and image identification. The GPR is introduced to perform laboratory test on the three common subgrade filling materials( silt,medium sand and gravel sand) to acquire the dielectric constant under different water content and different dry density. Some comparisons and directional deduction analyses on the obtained images are done,and the relationship between the dielectric constant and subgrade soil class,water content and dry density is known. The fitting correlation coefficients are all more than 0. 95. Take silt for example,the MATLAB program is used to do multiple nonlinear regression and the fitted function is gotten which reflects the relationship between the dielectric constant and water content and dry density. The fitting function has a good fitting effect and could be used to compute the dielectric constant of silt under different water content and different dry density. The test method and result provide an important basis for the determination of dielectric constant and the quick detection and evaluation for the compactness of subgrade soil without any damage. At the same time,it can provide technique references for the similar problems.
作者 白哲
出处 《公路工程》 北大核心 2015年第3期100-104,共5页 Highway Engineering
基金 河南省高等学校重点科研项目(14B560027 15B440001)
关键词 路基土 压实度 介电特性 探地雷达(GPR) 多元非线性回归 subgrade soil compactness dielectric constant ground penetrating radar(GPR) multiple nonlinear regressions
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