Highway network restoration after the great flood in Thailand

Flood is one of the common natural phenomena anywhere. In the second half of 2011, Thailand has recently faced with the most devastating flood of her modern history. More than 3,330 national highways are damaged by this flood. Some roads are heavily destroyed while others are partially damaged and emergency relieves cannot access to the flooded areas. Food and medicine distribution in the central part of the country has by large been disrupted. As a consequence, highway restoration is an urgent responsibility of road authorities. This study presents the sequential highway network restoration decision model when budgets and resources are unknown. Highways are restored one by one in sequence. To determine an optimal restoration sequence, the model is formulated as a dynamic program where the primary objective is to sequentially restore roadways to minimize the travel demand loss for the disconnected network. Once the network is connected, the secondary objective is to sequentially restore roadways to minimize the network travel time where traffic assignment onto the network is based on user equilibrium concept. The heuristic solution method using particle swarm optimization technique is provided for practical size problems. A sample network is examined to investigate the solution characteristics. It is found that the proposed algorithm can provide good practical solutions to the sequential highway network recovery problems and is incorporated to the Thailand highway maintenance management system.     

高速公路高填方路基沉降量的神经网络预测

利用BP神经网络较强的高次非线性映射能力和学习功能 ,建立了基于人工神经网络的高速公路路基沉降量的预测模型。该模型依据现场实测资料 ,避免了计算过程中各种人为因素的影响。通过对某高速公路高填方路基沉降量的现场监测成果的学习与预测检验 ,证明其预测精度与适用性良好 ,具有较大的工程实用价值     

A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan

An assessment of the susceptibility to rock slope failure by means of a back-propagation network is proposed for the eastern portion of the Southern Cross-Island Highway in Taiwan. The model was developed on the basis of six influence parameters of rock slope instability, which include the rock type, slope aspect, slope angle, joint set number, joint spacing and bedding–slope relationship. The values of these influence parameters were used as inputs for the network and were classified as nominal scales in terms of binary numbers, while the state of failure/non-failure of a given slope was assumed to be the output variable. Data on a total of 170 slopes along the highway was fed into the network for learning. According to the outputs of the network, the susceptibility to rock slope failure is categorized into four levels, namely low, medium–low, medium and high, which are mapped along the highway. Three highly susceptible regions are found, which can be viewed as hazardous sections requiring cautionary measures. Moreover, the proposed model can be used as a tool for determining the possible state of an unfamiliar rock slope in the context of devising management strategies to be applied to the investigated portion of the highway.     

贵州省公路工程岩溶环境区划

为了反映岩溶对公路建设影响的区域性特点,选择贵州省开展公路工程岩溶环境区划工作,提出了岩溶地区公路工程岩溶环境区划的原则和指标体系、建立了评价模型。运用层次分析法和GIS技术,绘出了岩溶对公路影响程度分区图。将贵州省划分为4个影响区,即西部以高～中影响为主的地区、东北部以中～高影响为主的地区、中部以中等影响为主的地区和低影响区。通过对贵州省公路路网和评价结果的综合分析,在12 462 km省道以上公路中,大约有6 265 km公路处于高影响区,而在1 587 km已建和在建高速公路中,有567 km处于高影响区,将对贵州公路规划、建设、养护与管理起到很好的指导作用。     

通渝隧道围岩变形的神经网络预测

隧道新奥法施工中 ,常以围岩变形量作为评判围岩稳定性和支护结构经济合理性的重要指标。公路隧道围岩变形量是随时间而变化的数据序列 ,因而可以建立一些实时跟踪预测模型和方法。根据通渝隧道围岩拱顶下沉位移变形的特性 ,采用神经网络技术来预测其变形量 ,结果表明该方法简易、有效     

降雨影响下滑坡变形预测的GA-NN模型研究

八尺门滑坡是福-宁高速公路施工现场的一个降雨型滑坡,近年来该滑坡由于人为及降雨的因素而频频发生滑动,对坡下的高速公路施工及滑坡区内的人民生命财产安全造成了严重威胁。为了准确掌握降雨量与滑坡位移之间的关系,在对八尺门滑坡现场降雨量及滑坡位移的长达一年的监测资料的基础上,利用GA-NN模型,对降雨条件下的滑坡位移演化发展做出了精确的预测。研究成果已作为重要的基础性数据被有关施工和研究部门采用,对于指导现场施工和保证滑坡影响区内人民生命财产的安全发挥了重要作用。     

国道212线甘肃段地貌特征与滑坡泥石流关系研究

国道212线甘肃段是甘肃省南北向的主要通道,在甘肃境内全长707km,公路沿线是我国滑坡、泥石流的主要发育区,特别是陇南市的宕昌武都段是我国最主要的滑坡、泥石流灾害区之一。线路通过地区的自然地理条件迥异,造成滑坡、泥石流类型和性质有很大差别。本文分析了该区地形地貌时、时空规律性及其与滑坡、泥石流的关系,为未来的高速公路建设提供理论依据。     

Optimized height of noise barrier for non-urban highway using artificial neural network

This study applies artificial neural network (ANN) for the determination of optimized height of a highway noise barrier. Field measurements were carried out to collect traffic volume, vehicle speed, noise level, and site geometry data. Barrier height was varied from 2 to 5 m in increments of 0.1 m for each measured data set to generate theoretical data for network design. Barrier attenuation was calculated for each height increment using Federal Highway Administration model. For neural network design purpose, classified traffic volume, corresponding traffic speed, and barrier attenuation data have been taken as input parameters, while barrier height was considered as output. ANNs with different architectures were trained, cross validated, and tested using this theoretical data. Results indicate that ANN can be useful to determine the height of noise barrier accurately, which can effectively achieve the desired noise level reduction, for a given set of traffic volume, vehicular speed, highway geometry, and site conditions.     

Evaluation of coseismic landslide hazard on the proposed Haast-Hollyford Highway,South Island,New Zealand

Coseismic landsliding presents a major hazard to infrastructure in mountains during large earthquakes. This is particularly true for road networks, as historically coseismic landsliding has resulted in road losses larger than those due to ground shaking. Assessing the exposure of current and planned highway links to coseismic landsliding for future earthquake scenarios is therefore vital for disaster risk reduction. This study presents a method to evaluate the exposure of critical infrastructure to landsliding from scenario earthquakes from an underlying quantitative landslide hazard assessment. The method is applied to a proposed new highway link in South Island, New Zealand, for a scenario Alpine Fault earthquake and compared to the current network. Exposure (the likelihood of a network being affected by one or more landslides) is evaluated from a regional-scale coseismic landslide hazard model and assessed on a relative basis from 0 to 1. The results show that the proposed Haast-Hollyford Highway (HHH) would be highly exposed to coseismic landsliding with at least 30–40?km likely to be badly affected (the Simonin Pass route being the worse affected of the two routes). In the current South Island State Highway network, the HHH would be the link most exposed to landsliding and would increase the total network exposure by 50–70% despite increasing the total road length by just 3%. The present work is intended to provide an effective method to assess coseismic landslide hazard of infrastructure in mountains with seismic hazard, and potentially identify mitigation options and critical network segments.     

圆弧形公路边坡稳定性分析的神经网络法

边坡的稳定性往往取决于一些难以确定的非线性因素。而人工神经网络法具有并行处理数据与信息、良好的容错特性和较强的抗噪声能力,可以通过自学功能从样本实例中获得复杂的非线性关系,能模拟人脑的某些智能行为,因而适用于解决非确定性的边坡稳定性评价问题。本文建立了边坡稳定性评价的神经网络BP模型,用收集到的边坡稳定破坏实例作为样本进行学习,对桂林-柳州一级公路中K250段公路边坡进行了稳定性评价,结果表明：神经网络法是一种有效的边坡稳定性分析方法。     

GPS静态定位技术在平面控制测量中的应用

在现代桥梁建设过程中,用常规控制测量的方法进行测量难以保证精度,为满足通视要求,往往还需要大量的砍伐工作,通过GPS定位技术在望东长江公路大桥控制网的建立中应用,提高了精度,减少了工作量,降低了成本,取得了事半功倍的效果。     

Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation

This paper is aimed at creating an empirical model for assessing failure potential of highway slopes, with a special attention to the failure characteristics of the highway slopes in the Alishan, Taiwan area prior to, and post, the 1999 Chi-Chi, Taiwan earthquake. The basis of the study is a large database of 955 slope records from four highways in the Alishan area. Artificial neural network (ANN) is utilized to “learn” from this database. The developed ANN model is then used to study the effect of the Chi-Chi earthquake on the slope failure characteristics in the Alishan area. Significant changes in the degrees of influence of several factors (variables) are found and possible reasons for such changes are discussed. The novelty of this paper lies in the fact that the developed ANN models are used as a tool to investigate the slope failure characteristics before and after the Chi-Chi earthquake.     

基于神经网络在路面基层压实参数中的应用

石灰工业废渣稳定类半刚性材料是高等级公路路面基层材料常见形式之一,根据规范和设计要求可分为含骨料类和不含骨料类。当骨料含量超过50％时,室内重型击实试验劳动量大,并且干密度和含水率睦线不稳定。在已知不含骨料的石灰工业废渣稳定类半刚性材料（即结合料）的最大干密度和最优含水率的基础上,通过结合人工神经网络理论,基于Matlab的BP人工神经网络,建立并编制了含骨料的石灰工业废渣稳定类半刚性材料压实参数（最大干密度和最优含水率）的预测网络模型,经过对网络模型的大量训练、训练函数和传递函数的调整及初始训练数据的规一化,最后建立了6→15→2的网络模型,其网络模型预测结果稳定准确,有一定实际应用价值。     

高频探地雷达技术在香港工程质量检测中的应用

结合几个典型的工程质量检测实例，详尽介绍了高频探地雷达技术在香港特别行政区公路路面厚度、机场跑道面层脱空、海底隧道村砌以及城市地下管网调查等工程检测方面的应用成果，进一步证明了探地雷达技术在无损工程质量检测应用方面的有效性和必要性。     

Groundwater resources evaluation case study via discrete fracture flow modeling

The Arizona Department of Transportation (ADOT) is preparing to upgrade State Route 260 between Payson and Heber. It is estimated that a total of about one million cubic meters of water will be required for embankment construction during a period of about 84 months to upgrade the first 33.8 km of the highway. ADOT is investigating various sources of construction water for use in the highway improvement project, including groundwater resources along the highway corridor. A region known as the RV site, underlain by fractured granite, is located 12.9 km east of Payson. The site includes three springs, a creek and several wells. Several boreholes and observation wells were made to a maximum depth of 157 m to obtain fracture data and to conduct pumping tests with monitoring. Fracture data recorded by acoustic televiewer logs were used to build a fracture network model for the rock mass. Results of a 24-hour and a 7-day pumping tests were used to calibrate hydraulic parameters of a finite element discrete fracture fluid flow model considering the region as a heterogeneous, anisotropic, fractured medium. A 38-day multi-well pumping test was used to validate the calibrated numerical model. The calibrated model showed the capability to provide reasonably accurate predictions for new pumping tests conducted in the same well field. The validated model was then used to simulate pumping exceeding a 7 year period under different scenarios incorporating different sets of boundary conditions and different pumping rates at multi-wells, with and without recharge, to evaluate the yield of the aquifer and to assess the effect of long-term pumping on the environment. The results indicated that (a) the combined yield of the wells in the RV site is sufficient to meet the water demand for the ADOT highway project and (b) the water levels in the well field would decline between 3.0 and 7.6 m after one year of pumping and by 12.2 to over 30.5 m during the life of the project.     

山区公路路基边坡地质灾害远程监测预报系统开发及应用

针对山区公路路基边坡点状分布、数量较多、规模相对较小且易受人类活动和环境变化影响的特点,确立了以位移和降雨量为主的监测原则,基于GPRS技术,集成触发式位移计、容栅式雨量计、数据采集传输模块和太阳能供电装置,形成了经济、节能、高效的单体边坡远程监测硬件系统。采用Visual C++网络编程技术,以SQL Server2005为数据库平台,开发了地质灾害远程监测预报软件系统,实现了数据的远程实时接收和可视化分析管理。由于公路边坡变形破坏模式多样,系统还集成了多种时间预测模型,建立了边坡变形预报模型库。通过在常吉高速公路的安装运行,系统成功地进行远程实时监控,保障了极端天气下公路的安全运营。     

广西公路地质灾害特征及三级地貌区划研究

滑坡、崩塌、岩溶、膨胀土是影响广西地区公路工程建设的主要地质灾害,文章在全面调查广西地貌,自然环境特征的基础上,系统研究了该区地质灾害特点、分布及对公路工程的危害;针对以往公路自然区划中对气候（水热）因素考虑较多的情况,依据《公路自然区划标准》三级区划的第一种方法,遵循公路自然区划原则,借鉴自然区划方法,利用计算机平台（GIS）,制定广西公路三级地貌区划体系,该体系对广西地区的公路工程建设具有较大使用价值;依据该区划方案提出预防和治理该区主要公路地质灾害的措施。     

金丽温高速公路连拱隧道超挖预测及原因分析

通过对金丽温高速公路连拱隧道的地质特征进行现场调查,经分析得出隧道区岩体结构面的特征参数,利用分析出的结构面特征参数值,结合隧道超挖预测中的神经网络理论及结构面网络模拟理论,对连拱隧道的超挖情况进行预测。从分析结果看,超挖问题在连拱隧道的施工过程中不可避免,其产生部位主要集中在中导洞与隧道的连接部位,通过预测得出的结果与现场开挖情况进行对比分析说明,隧道的超挖预测理论预测结果与实际有较好的一致性,文中最后针对隧道的特点,分析了产生超挖的基本原因,为确保隧道开挖过程中减少超挖提供依据。     

多年冻土区公路病害对冻土地温和含冰类型的敏感性分析

多年冻土的年平均温度和含冰类型作为表征多年冻土特征的两个基本指标,在一定程度上决定了多年冻土区公路工程所要采取的技术措施. 因此,年平均温度和含冰类型是多年冻土公路工程设计和施工中必须考虑的两个因素. 通过对青康公路国道214线沿线冻土工程地质状况调查和公路病害的分析,结合区域内气象水文等资料,研究了公路工程病害对多年冻土年平均温度和含冰类型的敏感性,查清了影响公路病害的主要因素. 研究结果为不同地温分区、不同含冰量多年冻土区公路病害防治技术措施的选择提供了科学依据,将减少或延缓多年冻土区公路病害的发生,提高工程质量,节省养护成本,保障安全运营,促进冻土区经济社会的发展.     

宜黄高速公路噪声污染规律研究

高速公路的噪声污染愈来愈受到社会的关注, 以宜黄高速公路为例, 对全线不同剖面进行了系统的噪声监测、评价、分析与预测, 研究了高速公路噪声污染规律, 并提出了噪声污染防治措施, 为高速公路的环境管理提供科学依据.