【作者】 张涛；

【导师】 侯相深；

【作者基本信息】 哈尔滨工业大学 ， 道路与铁道工程， 2009， 硕士

【摘要】 在我国公路养护管理过程中,传统的经验决策仍占据重要地位,已有的使用性能预测模型方法明确简单,但精度低,局限性大,不可避免地造成养护维修决策的盲目性和片面性。而许多先建的重要道路的使用性能已经衰减至低谷,建立在传统理论模式下的路面管理系统迫切需要进一步完善以适应现代社会对交通的需求。随着路面检测技术的不断进步,路面性能的历史数据已经积累到一定程度,研究开发一种能够使用海量数据和兼顾多种因素的复杂的路面使用性能预测系统成为可能。随着计算机技术和人工智能技术的发展,人工神经网络(ANN)、遗传算法等理论开始用于路面使用性能衰变规律方面的研究。人工神经网络(ANN)因其优越性能而广泛地应用于各个学科的研究领域。其中以BP网络(Back-Propagation Neural Network)的应用技术最为显著,它超强的非线性映射能力能够逼近任意函数,为解决非线性问题提供了有力的途径,可以模拟复杂的非线性动态系统或过程,实现系统的模式识别等。它能够从不同角度对不同的路面性能对象、应用不同的方法建立相应的预估模型,在一定程度上克服了传统方法的固有缺陷,能够较为客观地反映路面使用性能衰减规律。国内对沥青路面使用性能的评价有若干指标,这些指标的发展趋势将直接影响路面使用性能的优劣。本文在MATLAB软件平台上,利用神经网络工具箱,结合道路使用性能的评价方法建立了基于BP网络的预测模型,结合整体路网内路面使用状况的历年实测数据,选取了若干路面使用性能指标进行了预测分析,为提高预测精度修正了预测模型,并采用修正后的模型对实测数据进行了预测分析,为公路管养部门提供了养护建议。更多还原

【Abstract】 The traditional experience still plays an important role in highway maintenance management in our country. The old performance prediction model are clear and simple, but with low accuracy and limitations, maintenance decision-making will be blindness and one-sided. As the pavement performance of many important roads has been attenuated to a low ebb, Pavement Management System based on the traditional models need to be improved in order to adapt to modern society’s transport needs. With the development of road detection technology, pavement performance data has accumulated to a certain extent. It is possible to develop a pavement performance prediction system which uses a huge amount of data and considers a variety of factors. With the development of computer technology and artificial intelligence technology, artificial neural network (ANN), genetic algorithms are used in studies of the decay law of pavement performance.Artificial Neural Network (ANN) is widely used in various research disciplines because of its superior performance. BP network (Back-Propagation Neural Network)‘s application is the most significant. Its non-linear mapping ability is able to approximate any function to solve nonlinear problems. BP net work can simulate complex non-linear dynamic system or process to achieve the pattern recognition. It can establish different prediction models from different angles for objects. BP net work can overcome the inherent shortcomings of traditional models and reflect the attenuation law of pavement performance more objectively.Domestic asphalt pavement performance evaluation has a number of indicators that can directly affect the pavement performance. In this paper, prediction models based on BP neural network are established by neural network toolbox on the MATLAB software platform. Combining with factual data of the whole road network conditions, some indicators are selected to predictive analyze. The prediction model is amended to improve the prediction accuracy. Using the revised prediction model, this paper analyzes the prediction results and gives a piece of advice to highway department.更多还原