东南大学学报（自然科学版）2012，Vol.42Issue(5)：970-974,5.DOI:10.3969/j.issn.1001-0505.2012.05.031
基于便携式落锤动力弯沉的路基弯沉预测模型
Predictive models of subgrade deflection using data from portable falling deflectometer
摘要
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 (PF-WD) 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.关键词
弯沉/携式落锤弯沉仪/贝克曼梁/神经网络Key words
deflection/ portable falling weight deflectometer/ Benkelman beam/ artificial neural network分类
交通工程引用本文复制引用
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):970-974,5.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). （CMMI-0644552）
