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运营期公路隧道排水系统通畅与否直接关系到隧道支护结构的安全状态。为更好地了解我国不同区域环境下公路隧道排水系统设计特征及适用性,基于我国气候环境与地理信息特征,搜集了十四个省、自治区公路隧道典型排水系统设计样本,分别从排水系统总排水方式、局部排水设计与不同构造组合三个方面对比分析了不同区域环境下排水系统的设计差异和特征,同时对碳酸盐岩地区公路隧道排水系统设计和排水模型构建与优化进行了讨论。结果表明:我国公路隧道排水系统类型可归纳为侧式暗沟总排式、中心排水沟总排式及侧式暗沟+中心排水沟总排式三种类型,区域特点明显,各排水类型之间优点各异,但缺点相似;非冻结区内排水设计呈多样化,较小排水量需求下推荐采用侧式暗沟总排式设计,排水路径较短且易于后期维护,大排水量需求下可采用侧式暗沟+中心排水沟总排式设计;冻结地区隧道排水系统设计均为中心排水沟总排式,中心排水沟检查井内可布置多层保温材料,形成多层内腔结构,能够有效保持沟内水环境温度;通过降低纵向盲管检查井内横向盲管的标高,可将现行网状排水模型优化为串并联排水模型,该排水模型排水路径短,排水系统堵塞概率低,排水量较大时可自动转换为网状排水模型,保... 相似文献
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《现代隧道技术》2018,(5)
在软土隧道施工与运营期间,隧道周围土体沉降会对附近建筑物产生不利影响。文章采用两阶3D段分析方法,借助有限差分软件FLAC探讨在考虑软土流变特性时隧道施工期与运营期的土体沉降与邻近群桩受力情况。在第一阶段,当隧道处于开挖阶段时,采用位移控制法分析隧道开挖引起的土体短期沉降,将模型中的土体沉降槽曲线、桩体挠度、沉降、弯矩、轴力与相关文献计算结果进行对比,验证数值模型的正确性。在第二阶段,当隧道处于运营期时,采用CVISC流变模型对土体长期沉降与群桩附加受力进行计算,探究CVISC模型中4个参数对土体变形及群桩响应的影响,并提出隧道运营期长期沉降拟合公式。 相似文献
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长大公路隧道网络通风研究 总被引:2,自引:1,他引:1
文章讨论了长大公路隧道通风网络的理论,建立了通风网络的数值模型,在此基础上编制了公路隧道网络通风解算程序,通过对秦岭终南山公路隧道通风方案的分析和研究,验证了程序的适用性和正确性,为解决长大公路隧道运营通风问题提供了一种有效途径. 相似文献
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小净距大跨度隧道风险影响因素复杂、施工期风险较大,需要开展有效的安全风险管理.通过对各类小净距大跨度隧道施工安全事故的分析统计,并结合现场施工实践,建立了安全风险评价指标体系,提出了施工阶段风险管理理念.文章引入ANP方法综合分析风险指标体系各指标间的相互关系,计算出各指标的整体权重,评价出施工中的主要风险源,并针对其制定了有效的施工控制措施.广安翠屏山隧道工程实践证明,实行安全风险管理可提高施工期安全水平,保证工程的顺利推进以至完工.其理论和方法可为同类工程所借鉴. 相似文献
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隧道建设过程中往往会遇到涌水的情况,给施工带来困难和不安全因素。文章通过对雪峰山隧道施工过程中洞内涌水的长期观测,总结出隧道施工涌水的一些规律和特征,为今后类似隧道的施工涌水的预测和防治提供参考。 相似文献
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To reduce injuries in road crashes, better understanding is needed between the relationship of injury severity and risk factors. This study seeks to identify the contributing factors affecting crash severity with broad considerations of driver characteristics, roadway features, vehicle types, pedestrian characteristics and crash characteristics using an ordered probit model. It also explores how the interaction of these factors will affect accident severity risk. Three types of accidents were investigated: two-vehicle crashes, single vehicle crashes and pedestrian accidents. The reported crash data in Singapore from 1992 to 2001 were used to illustrate the process of parameter estimation. Several factors such as vehicle type, road type, collision type, location type, pedestrian age, time of day of accident occurrence were found to be significantly associated with injury severity. It was also found that injury severity decreases over time for the three types of accident investigated. 相似文献
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Different clearance methods in traffic accident management lead to varied duration distributions. Apart from investigating the influence of various factors associated with accidents on the duration of such accidents using different clearance methods, this study also examines the cumulative incidence probability. We used traffic accident data obtained for 12 months from the Fourth Ring Expressway main line in Beijing to develop a subdistribution hazard regression model, which can assess the risk factors of two clearance methods. The regression results show that the different factors have statistically significant effects on the duration of two accident groups with different clearance methods; furthermore, opposite effects occur even for some factors that have a strong effect on both accident groups. For example, an accident involving a taxi extends the duration time with clearance method 1; in comparison, the accident is shorter with clearance method 2. The predicted cumulative incidence curves of the two types of clearance methods are shown as examples, with stratification based on the influence factors (taxi involved, season). Finally, the Gray test of the cumulative incidence functions and the log‐rank test of the Kaplan–Meier estimates of the survival functions are compared, in order to demonstrate the importance of using proper methods for analyses. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature. 相似文献