[1]孙璐,王登忠,张惠民.基于便携式落锤动力弯沉的路基弯沉预测模型[J].东南大学学报(自然科学版),2012,42(5):970-974.[doi:10.3969/j.issn.1001-0505.2012.05.031]
　Sun Lu,Wang Dengzhong,et al.Predictive models of subgrade deflection using data from portable falling deflectometer[J].Journal of Southeast University (Natural Science Edition),2012,42(5):970-974.[doi:10.3969/j.issn.1001-0505.2012.05.031]
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基于便携式落锤动力弯沉的路基弯沉预测模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第5期
页码:
970-974
栏目:
交通运输工程
出版日期:
2012-09-20

文章信息/Info

Title:
Predictive models of subgrade deflection using data from portable falling deflectometer
作者:
孙璐1; 2;  王登忠1;  张惠民3
1 东南大学交通学院,南京 210096; 2 美国Catholic大学土木工程系,华盛顿特区20064; 3 晋中公路分局,晋中030600
Author(s):
Sun Lu1;  2;  Wang Dengzhong1;  Zhang Huimin3
1 School of Transportation,Southeast University, Nanjing 210096,China
2 Department of Civil Engineering,The Catholic University of America,Washington DC 20064, USA
3 Jinzhong Highway Administration Bureau, Jinzhong 030600, China
关键词:
弯沉;  携式落锤弯沉仪;  贝克曼梁;  神经网络
Keywords:
deflection;  portable falling weight deflectometer;  Benkelman beam;  artificial neural network
分类号:
U416.03
DOI:
10.3969/j.issn.1001-0505.2012.05.031
摘要:
为了提高路基弯沉检测的效率和精度,以便携式落锤弯沉仪PFWD为平台,对路基弯沉盆指标进行了分析研究.并且针对含砾黏土和含碎石黏土2种不同路基结构,基于回归分析技术及人工神经网络智能方法构建了动弯沉与贝克曼梁静态弯沉之间的分析模型,并对2种不同模型的预测结果进行评价分析.实测数据表明,由于考虑了弯沉盆指标,采用回归模型对2种不同路基的贝克曼梁静弯沉预测相对误差均值分别由4.64%和3.99%下降到3.01%和2.35%,回归方程对贝克曼梁静弯沉的预测精度得到了提高; 同时,采用BP神经网络模型预测相对误差均值分别为1.66%和1.80%,优于多元回归模型.研究结果可以为路基贝克曼梁静弯沉的检测提供参考.
Abstract:
In order to improve the efficiency and precision of subgrade deflection detection, this paper analyzes subgrade deflection basin indicators using portable falling weight deflectometer(PFWD)as the platform. For different embankment structures, regression analysis and artificial neural network method are used to establish models relating dynamic deflection to the static deflection. The measured data show that, considering the deflection basin indicators, the average of the relative error of Benkelman beam deflection predicted by regression models are reduced from 4.64% and 3.99% to 3.01% and 2.35% for two different embankment structures respectively. Meanwhile, the average of the relative error predicted by BP(back-propagation)neural network model are 1.66% and 1.80%, which is better than the multiple regression models. The results provide reference for prediction of static deflection.

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备注/Memo

备注/Memo:
作者简介: 孙璐(1972—),男,博士,教授,博士生导师,workingworking123@163.com.
基金项目: 教育部“长江学者”特聘教授奖励基金资助项目、美国国家科学基金资助项目(CMMI-0644552)、江苏省“六大人才高峰”资助项目、教育部霍英东基金资助项目(114024)、江苏省自然科学重点资助项目(SBK200910046).
引文格式: 孙璐,王登忠,张惠民.基于便携式落锤动力弯沉的路基弯沉预测模型[J].东南大学学报:自然科学版,2012,42(5):970-974. [doi:10.3969/j.issn.1001-0505.2012.05.031]
更新日期/Last Update: 2012-09-20