基于混合遗传算法神经网络的路面性能评价模型

Wei-dong Qian
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引用次数: 7

摘要

道路路面性能评价与预测是路面管理系统的两个重要组成部分。为了科学准确地预测未来道路路面状况,对路面性能的评价指标和主要影响因素进行了分析。选取功能性能、结构性能、安全性能和舒适性作为演化指标,以温度、年降水量、年平均日交通量3个因素作为参数。分别建立了BP神经网络预测模型和基于神经网络和遗传算法的混合预测模型。预测结果表明,基于遗传算法的神经网络模型比BP神经网络具有更高的预测精度和更强的网络泛化能力;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Road pavement performance evaluation model based on hybrid genetic algorithm neural network
Road pavement perfoemance evaluation and prediction is two most important parts of pavement management system. In order to scientifically and accurately predict the future road pavement situation,evaluation indexes and main influence factors of pavement performance were analyzed. Then functional performance,structure performance, safety performance, and comfortability performance was selected as the evolution index and three factors were taken as parameters, including temperature, annual precipitation,annual average daily traffic. The two prediction models of BP neural network and hybrid algorithms based on neural network and genetic algorithm were built respectively. Forecasting result shows that neural network model based on genetic algorithms has higher prediction accuracy and more network generalization than those of BP neural network;
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