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基于LTPP数据的水泥混凝土路面错台影响因素与预测模型研究
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摘要
水泥混凝土路面错台严重影响行车舒适性与道路通行能力。为减小错台的不利影响,在设计施工及管养过程中有效控制错台,需要了解错台影响因素,并全面把握错台发展规律。目前我国缺乏足量的错台实测数据,无法深入开展水泥混凝土路面错台发展规律及预测研究。美国AASHTO《新建与改建路面力学—经验设计指南》(MEPDG)提出了错台预测模型,但需要输入较多的设计参数及参数标定。由于我国路面管理系统缺少参数标定所需的足量错台数据,导致MEPDG水泥混凝土路面错台模型短期内无法成功应用于我国水泥混凝土路面工程实践。
     美国长期路面性能(LTPP)数据库中包含大量错台数据,可应用于错台预测研究。虽然我国道路交通状况、路面结构类型、环境等因素与美国不尽相同,但错台发展机理仍存在客观性和相似性。因此,有必要基于美国LTPP数据库错台数据进行错台预测研究,这对于全面掌握错台发展规律以及研究我国水泥混凝土路面错台防治措施具有重要现实意义。
     本文在分析美国普通水泥混凝土路面设计及工程实践基础上,基于LTPP数据库提取与预处理错台分析样本,并研究错台发展过程,结合LTPP数据库分析各因素对错台的影响,进行错台影响因素敏感性分析,建立了BP神经网络与正交设计相结合的水泥混凝土路面错台预测模型。本文研究思想和方法以及主要成果分别为:
     (1)分析了MEPDG错台模型构建及其软件运用方法,探讨了MEPDG错台模型在短期内应用的可行性,并基于LTPP数据研究了美国水泥混凝土路面错台表现,从而把握了错台发展的实际规律。
     (2)探讨了水泥混凝土路面错台发展的力学机理,深入分析了错台发生的原因,详细讨论了错台三个阶段的发展情况,开展了各因素对错台影响研究,并结合MEPDG模型量化研究了主要因素对错台的影响。
     (3)提出了错台分析样本提取思路,从空间与时间角度建立了针对错台样本质量评价与处理方法,并分析了错台样本相关变量与错台的关系,采用接缝水分允许入渗率量化接缝状况,基层冲刷系数量化基层类型,基层顶面当量回弹模量量化路基类型与结构组合,边缘传荷能力与路肩支撑能力量化路肩类型,降雨量排除率量化排水能力,提出了数据量化原则,从而将LTPP数据库中数据转换成与错台相关的变量,实现了错台样本分析数据的重构。
     (4)基于重构数据库进行了影响因素敏感性分析,分析了不同数据质量以及不同试验设计对敏感性分析结果的影响,研究了不同试验设计下路面的不同类型因素的重要性排序,并提出了不同气候分区下有利于减小路面错台的设计建议。
     (5)结合敏感性分析结果,开展了错台发展的BP神经网络预测研究,提出了BP神经网络预测模型优选思路,分别建立了不同气候分区路面错台BP神经网络预测模型,并结合正交试验设计,提出了量化BP神经网络预测结果的分析思路与方法,建立了基于BP神经网络预测结果的路面错台计算公式。
     本文研究将理论分析与水泥混凝土路面工程实际问题相结合,对于水泥混凝土路面错台发展规律研究具有重要参考价值,所建立的错台预测模型可在我国进行验证,为水泥混凝土路面错台的防治提供理论依据。
Concrete pavement faulting affects the comfortableness of driving and road trafficcapacity. In order to decrease the adverse effects of faulting, and control faulting duringdesign and maintenance management, it is necessary for us to know the impact factors andtotally grasp the development law of faulting. In China, due to the lack of faulting and itsrelated data accumulated over a long time, it is difficult for deeply research into thedevelopment law and forecasting of faulting. The United States AASHTO’s concretepavement design method in MEPDG put forward faulting forecasting model, but it needsmany design parameters to calibrate. The lack of enough faulting data in our pavementmanagement system leads MEPDG concrete pavement faulting model cannot successfullyapply in our country in a short term.
     There are a lot of faulting data in the United States Long-Term Pavement Performance(LTPP) database, and those data can be used in to build fault model. Although the road trafficconditions, pavement structure types and environment in our country is not the same withUnited States, but the development law of faulting still has inner links and similarity. So it isnecessary to predict the faulting based on the faulting data from the LTPP database, thus wecan master the impact factors and development law of faulting, which is feasibility andpractical.
     This thesis analyze the pavement design and engineering practices of united states,combine with LTPP database, a faulting database is established to study the development offaulting. Considering the impact factors on faulting and make a sensitivity analysis, a cementpavement forecasting model is established with BP neural network and orthogonal design isconsidered. The paper’s idea, methods and main results are as follows:
     (1) Analyze the MEPDG model building and its software, discuss the feasibility of itsapplication in short-term. The behavior of concrete pavement fault in America is studiedbased on LTPP data.
     (2) To study the mechanics mechanism of concrete pavement in the development offaulting, deeply probe into the reasons of the occurrence of faulting, and refine the threedifferent steps of faulting’s development process. The influence of factors to faulting is also studied In addition, the influence of main factors to faulting is also discussed combining withthe quantitative studies of MEPDG model.
     (3) Put forward extraction method of faulting analytical samples and establish a set ofthorough treatment method to faulting samples’ quality from the prospective of time andspace. Analyze the relationship between correlated variable and faulting of faulting samples.Using the infiltration rate of joint allowing water quantize the joint condition, coefficient ofbasement washing quantize joint form, load transfer ability of the edge, support ability of theshoulder quantize shoulder form and drainage rate of rainfall quantize drainage capacity. Dataquantitative principle is put forward and LTPP data are converted into variables related tofaulting so that faulting sample analyzing database can be reestablished.
     (4) Sensitivity analysis to the factors is conducted based on the new database, analysesthe qualities of data and experiment designs’ influence on sensitivity analysis. Then studythe importance ranking of different type of factors under different experiment designs. Andfinally put forward some design suggestions to faulting for different climate partition.
     (5) Combined with sensitivity analysis results, the BP neural network prediction researchto the development of the faulting is carried out. As a result, optimization idea of BP neuralnetwork prediction model is presented and a reasonable BP neural network prediction modelof different climate partition is established. At the same time, combined with orthogonalexperiment design, analysis thought and method to quantizing the predict results of BP neuralnetwork is put forward and the faulting calculation formula of pavement is given out based onthe predict result of BP neural network.
     Theoretical analysis and practical engineering problems of concrete pavement arecombined in this paper. The result is helpful for learning developing rules of faulting and theprediction model can be further corrected through the accumulated process of faulting data inour country, so it has values of certain theoretical research and engineering application.
引文
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