滑动平均法在刚构桥施工应力预测中的应用

文章针对刚构桥施工过程中应力不确定性影响因素,介绍通过滑动平均法GM(1,1)模型对数据进行建模前预处理方法,并结合葫芦鼎大桥工程实例,验证了滑动平均法建立GM(1,1)模型在刚构桥施工应力预测中较好的适用性和较高的预测精度,也是对原始GM(1,1)模型的一种改进。     

不等时距GM(1,1)模型在预测输气管道腐蚀中的应用

根据等时距GM(1,1)模型建立了不等时距GM(1,1)预测模型,该模型可应用于利用腐蚀指标的原始数据来预测以后的输气管道腐蚀情况。验证表明:不等时距灰色模型扩大了等时距灰色模型的应用范围,在小样本的情况下同样可以做出较准确预测,为输气管道的防腐提供了可靠的依据。     

优化GM（1,1）模型在连续刚构桥施工监控中的应用

文章结合优化GM（1,1）模型用于罗天乐大桥施工监控的工程实例,介绍了优化GM（1,1）模型的原理、计算方法及计算过程,并根据实际监控经验提出了建议。罗天乐大桥的具体施工监控实践表明,优化GM（1,1）模有着较高的预测精度,能较好地应用于大跨度连续刚构桥的施工监控。     

灰色理论在土石混填路基工后沉降预测中的应用

运用灰色理论建立了土石混填路基的非等时距预测模型,同时,为了预测土石混填路基工后沉降,建立了土石混填路基工后沉降的GM(2,1)模型。通过实例计算表明,GM(2,1)模型预测精度较GM(1,1)灰色模型高,在土石混填路基工后沉降预测中具有较大的实用价值。     

灰色模型在路基沉降预测中的对比研究

为了确定合适的工后沉降预测模型,文章以桂东四都至八面山公路典型路基断面实测数据为研究对象,采用GM(1,1)模型、UGM(1,1)模型及Verhulst模型分别建立模型,对工后沉降进行预测分析。结果表明:在拟合阶段时,GM(1,1)模型的平均误差绝对值比UGM(1,1)模型和Verhulst模型分别高出约30%和15%;在预测阶段的精度对比分析中,UGM(1,1)模型在各方面均优于Verhulst模型,能够准确地预测路基沉降的真实情况。     

基于灰色理论的桥梁群桩基础工后沉降预测

文章基于灰色系统理论,应用其等时距GM（1,1）模型及改进的新陈代谢模型对桥梁群桩基础工后沉降进行预测,通过与蕴藻浜特大桥某墩的沉降观测资料的对比分析,提出了桥梁群桩基础工后沉降灰色理论预测方法。     

基于非齐次指数函数灰色模型的隧道围岩变形预测

准确地测量、预测围岩变形是保证隧道施工安全的重要措施之一。围岩变形具有时间序列的单调增长性和随机性等特点,针对其特点,文章采用常规GM(1,1)模型、齐次指数函数灰色模型、非齐次指数函数灰色模型,对隧道的围岩变形进行了预测与对比分析。结果表明,常规GM(1,1)模型、齐次指数函数灰色模型、非齐次指数函数灰色模型三种模型的中误差分别为7.16%、5.01%、2.99%,最终沉降预测值相对误差分别为-8%、-5.52%、-3.05%。非齐次指数函数灰色模型具有较高的精度,在隧道围岩变形预测中具有广泛推广的应用价值。     

基于积分求导还原思想的新型边坡变形预测UGM(1,1)建模方法

文章通过研究传统预测模型的误差来源和传统UGM(1,1)灰色预测模型建模原理,提出一种基于积分求导还原思想构建的新UGM(1,1)模型,并以某高速公路一段路基边坡实测数据为依据,验证新型UGM(1,1)灰色预测模型的可行性及其预测结果的可靠性。算例预测结果表明:新UGM(1,1)模型的拟合和预测精度误差均达到合理范围5%以内,对非等时距灰色预测模型数值预测具有一定的参考价值。     

基于非等间距GM(1,1)模型群对深井巷道围岩变形的预测

针对时间间距不同的深井巷道围岩变形问题,文章建立了全数据非等间距GM(1,1)模型和不同维数的部分数据非等间距GM(1,1)模型,通过误差比较分析,得到了相对误差最小的模型为5维部分数据非等间距GM(1,1)模型,并对巷道顶底板和两帮位移量做出了预测。结果表明:在一定范围内,维数越小,非等间距GM(1,1)模型的平均相对误差越小,为预测巷道围岩变形提供了一种良好的方法。     

灰色GM(1,1)模型在靖西天然气管道负荷预测中的应用

靖西天然气管道是重要输气干线,准确预测需求负荷变化情况,确保管道安全、平稳、高效供气意义重大。文中以灰色理论为基础,利用管道历年气量数据建立灰色预测的GM(1,1)模型,采用后验差检验对预测模型进行检验,并对该管道未来用气需求量进行预测。计算结果显示:灰色GM(1,1)模型预测结果与实际结果具有较好的一致性,精度能够满足实际应用的要求,预测结果对靖西管道运行管理具有一定的借鉴作用。     

等里程间隔GM(1,1)模型在关角隧道6号斜井涌水量预测中的应用

在原始灰色GM(1,1)模型的基础上,通过运用等间距里程序列的分析方法建立模型,并对关角隧道6号斜井的涌水量进行了模拟预测。经检验,预测结果精度较高,对隧道工程涌水量的短期预测具有较大的实用价值。     

油气管道腐蚀速度灰色动态多级残差模型的确立及应用

为客观评价及预测油气管道的腐蚀速度和现状,人们一般采用灰色系统的GM(1,1)理论,但该方法有其固有的缺陷,预测精度不高,初始点的选择不尽合理,文中针对初始点和预测精度问题,运用最小二乘法拟合原理及残差修正理论进行了两处改进,从而提高了预测精度。最后针对某输油管道的实际监测腐蚀速度进行了分析预测,并对方法改进前后的预测结果进行了对比,可以看出预测精度大大提高。     

隧道变形预测的灰色与回归模型对比分析

模型预测法是目前常用的隧道围岩变形预测的方法之一。文章结合广梧高速公路茶林顶隧道工程实例,建立GM（1,1）灰色模型、GM（2,1）灰色模型和双曲函数回归模型分别对隧道围岩变形进行预测,并对各模型的预测情况进行对比分析。结果表明,不论是从短期还是从长期看,GM（1,1）灰色模型都体现了优越的模拟和预测效果,且建立预测模型时不需要大量的统计数据,可应用于工程实际。     

Comparing prediction methods for maritime accidents

ABSTRACT

The purpose of maritime accident prediction is to reasonably forecast an accident occurring in the future. In determining the level of maritime traffic management safety, it is important to analyze development trends of existing traffic conditions. Common prediction methods for maritime accidents include regression analysis, grey system models (GM) and exponential smoothing. In this study, a brief introduction is provided that discusses the aforementioned prediction models, including the associated methods and characteristics of each analysis, which form the basis for an attempt to apply a residual error correction model designed to optimize the grey system model. Based on the results, in which the model is verified using two different types of maritime accident data (linear smooth type and random-fluctuation type, respectively), the prediction accuracy and the applicability were validated. A discussion is then presented on how to apply the Markov model as a way to optimize the grey system model. This method, which proved to be correct in terms of prediction accuracy and applicability, is explored through empirical analysis. Although the accuracy of the residual error correction model is usually higher than the accuracy of the original GM (1,1), the effect of the Markov correction model is not always superior to the original GM (1,1). In addition, the accuracy of the former model depends on the characteristics of the original data, the status partition and the determination method for the status transition matrix.     

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.